Artificial Intelligence and Bioinformatics

• Abdallah EB, Folschette M, Roux O, Magnin M (2017) ASP-based method for the enumeration of attractors in non-deterministic synchronous and asynchronous multi-valued networks. Algorithms Mol Biol 12(1):20

  Article  Google Scholar 

• Abdo A, Chen B, Mueller C, Salim N, Willett P (2010) Ligand-based virtual screening using Bayesian networks. J Chem Inf Model 50(6):1012–1020

  Article  Google Scholar 

• Abdo A, Leclère V, Jacques P, Salim N, Pupin M (2014) Prediction of new bioactive molecules using a Bayesian belief network. J Chem Inf Model 54(1):30–36. PMID: 24392938

  Google Scholar 

• Abou-Jaoudé W, Traynard P, Monteiro PT, Saez-Rodriguez J, Helikar T, Thieffry D, Chaouiya C (2016) Logical modeling and dynamical analysis of cellular networks. Front Genet 7

  Google Scholar 

• Abu-Srhan A, Al Daoud E (2013) A hybrid algorithm using a genetic algorithm and cuckoo search algorithm to solve the traveling salesman problem and its application to multiple sequence alignment. Int J Adv Sci Technol 61:29–38

  Article  Google Scholar 

• Acuna V, Chierichetti F, Lacroix V, Marchetti-Spaccamela A, Sagot M-F, Stougie L (2009) Modes and cuts in metabolic networks: complexity and algorithms. Biosystems 95(1):51–60

  Article  Google Scholar 

• Adhikari B, Bhattacharya D, Cao R, Cheng J (2015) CONFOLD: residue-residue contact-guided ab initio protein folding. Proteins: Struct Funct Bioinform 83(8):1436–1449

  Google Scholar 

• Akutsu T (2010) A bisection algorithm for grammar-based compression of ordered trees. Inf Process Lett 110(18–19):815–820

  Article  MathSciNet  MATH  Google Scholar 

• Akutsu T, Hayashida M, Ching W-K, Ng MK (2007) Control of boolean networks: hardness results and algorithms for tree structured networks. J Theor Biol 244(4):670–679

  Article  MathSciNet  Google Scholar 

• Akutsu T, Kosub S, Melkman AA, Tamura T (2012) Finding a periodic attractor of a Boolean network. IEEE/ACM Trans Comput Biol Bioinform 9(5):1410–1421

  Article  Google Scholar 

• Albert R, Thakar J (2014) Boolean modeling: a logic-based dynamic approach for understanding signaling and regulatory networks and for making useful predictions. Wiley Interdiscip Rev: Syst Biol Med 6(5):353–369

  Google Scholar 

• Allouche D, André I, Barbe S, Davies J, de Givry S, Katsirelos G, O’Sullivan B, Prestwich S, Schiex T, Traoré S (2014) Computational protein design as an optimization problem. Artif Intell 212:59–79

  Article  MathSciNet  MATH  Google Scholar 

• Alocci D, Mariethoz J, Horlacher O, Bolleman JT, Campbell MP, Lisacek F (2015) Property graph vs RDF triple store: a comparison on glycan substructure search. PLOS ONE 10(12):1–17

  Article  Google Scholar 

• Aniba MR, Poch O, Marchler-Bauer A, Thompson JD (2010) Alexsys: a knowledge-based expert system for multiple sequence alignment construction and analysis. Nucleic Acids Res 38(19):6338

  Article  Google Scholar 

• Antonov I, Borodovsky M (2010) Genetack: frameshift identification in protein-coding sequences by the viterbi algorithm. J Bioinform Comput Biol 8(03):535–551

  Article  Google Scholar 

• Aoki-Kinoshita KF (2015) Analyzing glycan-binding patterns with the ProfilePSTMM tool. Springer, New York, pp 193–202

  Google Scholar 

• Aoki-Kinoshita KF, Ueda N, Mamitsuka H, Kanehisa M (2006) ProfilePSTMM: capturing tree-structure motifs in carbohydrate sugar chains. Bioinformatics 22(14):e25–e34

  Article  Google Scholar 

• Arellano G, Argil J, Azpeitia E, Benítez M, Carrillo M, Góngora P, Rosenblueth DA, Alvarez-Buylla ER (2011) Antelope: a hybrid-logic model checker for branching-time Boolean GRN analysis. BMC Bioinformatics 12(1):490

  Google Scholar 

• Aronson SJ, Rehm HL (2015) Building the foundation for genomics in precision medicine. Nature 526(7573):336–342

  Article  Google Scholar 

• Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, Davis AP, Dolinski K, Dwight SS, Eppig JT et al (2000) Gene ontology: tool for the unification of biology. Nat Genet 25(1):25–29

  Article  Google Scholar 

• Awada W, Khoshgoftaar TM, Dittman D, Wald R, Napolitano A (2012) A review of the stability of feature selection techniques for bioinformatics data. In: 2012 IEEE 13th International conference on information reuse and integration (IRI). IEEE, pp 356–363

  Google Scholar 

• Backofen R, Will S (2006) A constraint-based approach to fast and exact structure prediction in three-dimensional protein models. Constraints 11(1):5–30

  Article  MathSciNet  MATH  Google Scholar 

• Baldi P, Brunak S (2001) Bioinformatics: the machine learning approach. MIT Press, Cambridge

  MATH  Google Scholar 

• Ballester PJ, Mitchell JBO (2010) A machine learning approach to predicting protein-ligand binding affinity with applications to molecular docking. Bioinformatics 26(9):1169–1175

  Article  Google Scholar 

• Banerjee P, Siramshetty VB, Drwal MN, Preissner R (2016) Computational methods for prediction of in vitro effects of new chemical structures. J Cheminformatics 8(1):51

  Article  Google Scholar 

• Barahona P, Krippahl L (2008) Constraint programming in structural bioinformatics. Constraints 13(1):3–20

  Article  MathSciNet  MATH  Google Scholar 

• Barberis M, Todd RG, van der Zee L (2017) Advances and challenges in logical modeling of cell cycle regulation: perspective for multi-scale, integrative yeast cell models. FEMS Yeast Res 17(1)

  Google Scholar 

• Bard JB, Rhee SY (2004) Ontologies in biology: design, applications and future challenges. Nat Rev Genet 5(3):213

  Article  Google Scholar 

• Baron M, Yanai I (2017) New skin for the old rna-seq ceremony: the age of single-cell multi-omics. Genome Biol 18(1):159

  Article  Google Scholar 

• Batt G, De Jong H, Page M, Geiselmann J (2008) Symbolic reachability analysis of genetic regulatory networks using discrete abstractions. Automatica 44(4):982–989

  Article  MathSciNet  MATH  Google Scholar 

• Batt G, Besson B, Ciron P-E, de Jong H, Dumas E, Geiselmann J, Monte R, Monteiro PT, Page M, Rechenmann F, Ropers D (2012) Genetic network analyzer: a tool for the qualitative modeling and simulation of bacterial regulatory networks. Springer, New York, pp 439–462

  Google Scholar 

• Baú D, Martin AJ, Mooney C, Vullo A, Walsh I, Pollastri G (2006) Distill: a suite of web servers for the prediction of one-, two- and three-dimensional structural features of proteins. BMC Bioinform 7(1):402

  Article  Google Scholar 

• Bechhofer S, Buchan I, De Roure D, Missier P, Ainsworth J, Bhagat J, Couch P, Cruickshank D, Delderfield M, Dunlop I et al (2013) Why linked data is not enough for scientists. Future Gener Comput Syst 29(2):599–611

  Article  Google Scholar 

• Bellazzi R (2014) Big data and biomedical informatics: a challenging opportunity. Yearb Med Inform 9(1):8

  Google Scholar 

• Benigni R, Bossa C (2008) Structure alerts for carcinogenicity, and the Salmonella assay system: a novel insight through the chemical relational databases technology. Mutat Res/Rev Mutat Res 659(3):248–261

  Article  Google Scholar 

• Beretta S, Bonizzoni P, Vedova GD, Pirola Y, Rizzi R (2014) Modeling alternative splicing variants from RNA-seq data with isoform graphs. J Comput Biol 21(1):16–40

  Article  MathSciNet  Google Scholar 

• Berger B, Leighton T (1998) Protein folding in the hydrophobic-hydrophilic (HP) model is NP-complete. J Comput Biol 5(1):27–40

  Article  Google Scholar 

• Bhaskar H, Hoyle DC, Singh S (2006) Machine learning in bioinformatics: a brief survey and recommendations for practitioners. Comput Biol Med 36(10):1104–1125. Intelligent technologies in medicine and bioinformatics intelligent technologies in medicine and bioinformatics

  Google Scholar 

• Bizer C, Heath T, Berners-Lee T (2009) Linked data-the story so far. Int J Semant Web Inf Syst 5(3):1–22

  Article  Google Scholar 

• Blake JA, Bult CJ (2006) Beyond the data deluge: data integration and bio-ontologies. J Biomed Inform 39(3):314–320. Biomedical ontologies

  Google Scholar 

• Blaszczyk M, Jamroz M, Kmiecik S, Kolinski A (2013) CABS-fold: server for the de novo and consensus-based prediction of protein structure. Nucleic Acids Res 41(W1):W406–W411

  Article  Google Scholar 

• Blaszczyk M, Gront D, Kmiecik S, Ziolkowska K, Panek M, Kolinski A (2014) Coarse-grained protein models in structure prediction. Springer, Berlin, pp 25–53

  Google Scholar 

• Bock C, Farlik M, Sheffield NC (2016) Multi-omics of single cells: strategies and applications. Trends Biotechnol 34

  Google Scholar 

• Bonizzoni P, Ciccolella S, Della Vedova G, Soto M (2017) Beyond perfect phylogeny: multisample phylogeny reconstruction via ILP. In: Proceedings of the 8th ACM international conference on bioinformatics, computational biology, and health informatics, ACM-BCB ’17, ACM, New York, USA, pp 1–10

  Google Scholar 

• Bouziane H, Messabih B, Chouarfia A (2015) Effect of simple ensemble methods on protein secondary structure prediction. Soft Comput 19(6):1663–1678

  Article  Google Scholar 

• Boyer F, Viari A (2003) Ab initio reconstruction of metabolic pathways. Bioinformatics 19(suppl\(\_{2}\)):ii26–ii34

  Google Scholar 

• Brahim AB, Limam M (2017) Ensemble feature selection for high dimensional data: a new method and a comparative study. Adv Data Anal Classif 1–16

  Google Scholar 

• Brim L, Češka M, Šafránek D (2013) Model checking of biological systems. In: Formal methods for dynamical systems. Springer, Berlin, pp 63–112

  Google Scholar 

• Brooks DR, Erdem E, Erdoğan ST, Minett JW, Ringe D (2007) Inferring phylogenetic trees using answer set programming. J Autom Reason 39(4):471–511

  Article  MathSciNet  MATH  Google Scholar 

• Brown JB, Niijima S, Okuno Y (2013) Compound protein interaction prediction within chemogenomics: theoretical concepts, practical usage, and future directions. Mol Inform 32(11–12):906–921

  Article  Google Scholar 

• Cannata N, Schröder M, Marangoni R, Romano P (2008) A semantic web for bioinformatics: goals, tools, systems, applications. BMC Bioinform 9(4):S1

  Article  Google Scholar 

• Caravagna G, Graudenzi A, Ramazzotti D, Sanz-Pamplona R, De Sano L, Mauri G, Moreno V, Antoniotti M, Mishra B (2016) Algorithmic methods to infer the evolutionary trajectories in cancer progression. Proc Natl Acad Sci 113(28):E4025–E4034

  Article  Google Scholar 

• Carrillo M, Góngora PA, Rosenblueth DA (2012) An overview of existing modeling tools making use of model checking in the analysis of biochemical networks. Front Plant Sci 3

  Google Scholar 

• Chapman SD, Adami C, Wilke CO, KC DB (2017) The evolution of logic circuits for the purpose of protein contact map prediction. PeerJ 5:e3139

  Google Scholar 

• Chauhan JS, Rao A, Raghava GPS (2013) In silico platform for prediction of N-, O- and C-glycosites in eukaryotic protein sequences. PLOS ONE 8(6):1–10

  Article  Google Scholar 

• Chelliah V, Juty N, Ajmera I, Ali R, Dumousseau M, Glont M, Hucka M, Jalowicki G, Keating S, Knight-Schrijver V, Lloret-Villas A, Natarajan KN, Pettit J-B, Rodriguez N, Schubert M, Wimalaratne SM, Zhao Y, Hermjakob H, Le Novère N, Laibe C (2015) BioModels: ten-year anniversary. Nucleic Acids Res 43(D1):D542–D548

  Article  Google Scholar 

• Chen L, Liu H, Friedman C (2005) Gene name ambiguity of eukaryotic nomenclatures. Bioinformatics 21(2):248–256

  Article  Google Scholar 

• Chen Q, Chen Y-PP, Zhang C (2007) Detecting inconsistency in biological molecular databases using ontologies. Data Min Knowl Discov 15:275–296

  Article  MathSciNet  Google Scholar 

• Cheng J, Baldi P (2007) Improved residue contact prediction using support vector machines and a large feature set. BMC Bioinform 8(1):113

  Article  Google Scholar 

• Chen M, Yu S, Franz N, Bowers S, Ludäscher B (2013) Euler/X:a toolkit for logic-based taxonomy integration. Technical report 1306, 22nd International workshop on functional and (Constraint) logic programming, Technische Berichte des Instituts fur Informatik der Christian-Albrechts-Universitat zu Kiel

  Google Scholar 

• Clark C, Divvala S (2016) Pdffigures 2.0: mining figures from research papers. In: 2016 IEEE/ACM joint conference on digital libraries (JCDL). IEEE, pp 143–152

  Google Scholar 

• Coluzza I (2017) Computational protein design: a review. J Phys: Condens Matter 29(14):143001

  Google Scholar 

• Coste F (2016) Learning the language of biological sequences. Springer, Berlin, pp 215–247

  MATH  Google Scholar 

• Cruz-Monteagudo M, Medina-Franco JL, Perez-Castillo Y, Nicolotti O, Cordeiro MND, Borges F (2014) Activity cliffs in drug discovery: Dr. Jekyll or Mr. Hyde? Drug Discov Today 19(8):1069–1080

  Google Scholar 

• Cuff JA, Barton GJ (2000) Application of multiple sequence alignment profiles to improve protein secondary structure prediction. Proteins: Struct Funct Bioinform 40(3):502–511

  Google Scholar 

• Dallas DC, Guerrero A, Parker EA, Robinson RC, Gan J, German JB, Barile D, Lebrilla CB (2015) Current peptidomics: applications, purification, identification, quantification, and functional analysis. PROTEOMICS 15(5–6):1026–1038

  Article  Google Scholar 

• De Brevern A, Etchebest C, Hazout S (2000) Bayesian probabilistic approach for predicting backbone structures in terms of protein blocks. Proteins: Struct Funct Bioinform 41(3):271–287

  Google Scholar 

• De Raedt L, Kramer S (2001) The levelwise version space algorithm and its application to molecular fragment finding. In: Proceedings of the 17th international joint conference on artificial intelligence - volume 2, IJCAI’01. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, pp 853–859

  Google Scholar 

• Deagustini CAD, Martinez MV, Falappa MA, Simari GR (2016) Datalog\(+\)-ontology consolidation. J Artif Intell Res 56:613–656

  Google Scholar 

• Diaz-Uriarte R (2007) GeneSrF and varSelRF: a web-based tool and R package for gene selection and classification using random forest. BMC Bioinform 8(1):328

  Article  Google Scholar 

• Dimova D, Bajorath J (2016) Advances in activity cliff research. Mol Inform 35(5):181–191

  Article  Google Scholar 

• Doğan B, Ölmez T (2015) A novel state space representation for the solution of 2D-HP protein folding problem using reinforcement learning methods. Appl Soft Comput 26:213–223

  Article  Google Scholar 

• Drozdetskiy A, Cole C, Procter J, Barton GJ (2015) Jpred4: a protein secondary structure prediction server. Nucleic Acids Res 43(W1):W389–W394

  Article  Google Scholar 

• Dubey SP, Kini NG, Balaji S, Kumar MS (2017) Protein structure prediction on 2D square HP lattice with revised fitness function. In: 2017 International conference on advances in computing, communications and informatics (ICACCI), pp 1732–1736

  Google Scholar 

• Dubrova E, Teslenko M (2011) A SAT-based algorithm for finding attractors in synchronous Boolean networks. IEEE/ACM Trans Comput Biol Bioinform 8(5):1393–1399

  Article  Google Scholar 

• Dunn S, Wahl L, Gloor G (2008) Mutual information without the influence of phylogeny or entropy dramatically improves residue contact prediction. Bioinformatics 24(3):333–340

  Article  Google Scholar 

• Eddy SR (1998) Profile hidden Markov models. Bioinformatics (Oxford, England) 14(9):755–763

  Article  Google Scholar 

• El-Kebir M, Oesper L, Acheson-Field H, Raphael BJ (2015) Reconstruction of clonal trees and tumor composition from multi-sample sequencing data. Bioinformatics 31(12):i62–i70

  Article  Google Scholar 

• Erdem E (2011) Applications of answer set programming in phylogenetic systematics. Springer, Berlin, pp 415–431

  Google Scholar 

• Faraggi E, Kloczkowski A (2017) Accurate prediction of one-dimensional protein structure features using SPINE-X. Springer, New York, pp 45–53

  Google Scholar 

• Faraggi E, Zhang T, Yang Y, Kurgan L, Zhou Y (2012) SPINE X: improving protein secondary structure prediction by multistep learning coupled with prediction of solvent accessible surface area and backbone torsion angles. J Comput Chem 33(3):259–267

  Article  Google Scholar 

• Feigenbaum EA, Buchanan BG (1993) Dendral and meta-dendral: roots of knowledge systems and expert system applications. Artif Intell 59(1):233–240

  Article  Google Scholar 

• Feig M, Rotkiewicz P, Kolinski A, Skolnick J, Brooks CL (2000) Accurate reconstruction of all-atom protein representations from side-chain-based low-resolution models. Proteins: Struct Funct Bioinform 41(1):86–97

  Google Scholar 

• Ferreira LG, dos Santos RN, Oliva G, Andricopulo AD (2015) Molecular docking and structure-based drug design strategies. Molecules 20(7):13384–13421

  Article  Google Scholar 

• Ford E, St. John K, Wheeler WC, (2015) Towards improving searches for optimal phylogenies. Syst Biol 64(1):56–65

  Google Scholar 

• Frank M, Schloissnig S (2010) Bioinformatics and molecular modeling in glycobiology. Cell Mol Life Sci 67(16):2749–2772

  Article  Google Scholar 

• Franz NM, Chen M, Yu S, Kianmajd P, Bowers S, Ludäscher B (2015) Reasoning over taxonomic change: exploring alignments for the Perelleschus use case. PLOS ONE 10(2):1–34

  Article  Google Scholar 

• Galiez C, Magnan CN, Coste F, Baldi P (2016) VIRALpro: a tool to identify viral capsid and tail sequences. Bioinformatics 32(9):1405–1407

  Article  Google Scholar 

• Galperin MY, Fernández-Suárez XM, Rigden DJ (2017) The 24th annual nucleic acids research database issue: a look back and upcoming changes. Nucleic Acids Res 45(D1):D1

  Google Scholar 

• Gan X, Kapsokalivas L, Albrecht AA, Steinhöfel K (2008) A symmetry-free subspace for ab initio protein folding simulations. Bioinformatics research and development. Springer, Berlin, pp 128–139

  Chapter  Google Scholar 

• Gao Y, Wang S, Deng M, Xu J (2017) Real-value and confidence prediction of protein backbone dihedral angles through a hybrid method of clustering and deep learning. bioRxiv

  Google Scholar 

• Gawad C, Koh W, Quake SR (2016) Single-cell genome sequencing: current state of the science. Nat Rev Genet 17(3):175

  Article  Google Scholar 

• Gawehn E, Hiss JA, Schneider G (2016) Deep learning in drug discovery. Mol Inform 35(1):3–14

  Article  Google Scholar 

• Gertheiss J, Tutz G (2009) Supervised feature selection in mass spectrometry-based proteomic profiling by blockwise boosting. Bioinformatics 25(8):1076–1077

  Article  Google Scholar 

• Ghisalberti G, Masseroli M, Tettamanti L (2010) Quality controls in integrative approaches to detect errors and inconsistencies in biological databases. J Integr Bioinform 7(3):52–64

  Article  Google Scholar 

• Ghoorah AW, Devignes M-D, Smaïl-Tabbone M, Ritchie DW (2013) Protein docking using case-based reasoning. Proteins: Struct Funct Bioinform 81(12):2150–2158

  Google Scholar 

• Gonnet GH, Korostensky C, Benner S (2000) Evaluation measures of multiple sequence alignments. J Comput Biol 7(1–2):261–276

  Article  Google Scholar 

• Gordon JJ, Towsey MW, Hogan JM, Mathews SA, Timms P (2005) Improved prediction of bacterial transcription start sites. Bioinformatics 22(2):142–148

  Article  Google Scholar 

• Götz S, García-Gómez JM, Terol J, Williams TD, Nagaraj SH, Nueda MJ, Robles M, Talón M, Dopazo J, Conesa A (2008) High-throughput functional annotation and data mining with the Blast2GO suite. Nucleic Acids Res 36(10):3420–3435

  Google Scholar 

• Greene D, Richardson S, Turro E (2017) OntologyX: a suite of R packages for working with ontological data. Bioinformatics 33(7):1104–1106

  Google Scholar 

• Grivell L (2002) Mining the bibliome: searching for a needle in a haystack? EMBO Rep 3(3):200–203

  Article  Google Scholar 

• Gront D, Kmiecik S, Koliński A, Meinke JH, Zimmermann MT, Mohanty S, Hansmann UHE (2006) High throughput method for protein structure prediction. In: NIC workshop 2006: from computational biophysics to system biology, vol 34. John von Neumann institute for computing, Julich, pp 79–82

  Google Scholar 

• Guan Y, Myers CL, Hess DC, Barutcuoglu Z, Caudy AA, Troyanskaya OG (2008) Predicting gene function in a hierarchical context with an ensemble of classifiers. Genome Biol 9(S1):S3

  Article  Google Scholar 

• Guermeur Y, Geourjon C, Gallinari P, Deléage G (1999) Improved performance in protein secondary structure prediction by inhomogeneous score combination. Bioinformatics 15(5):413–421

  Article  Google Scholar 

• Guo Y, Tao F, Wu Z, Wang Y (2017) Hybrid method to solve HP model on 3D lattice and to probe protein stability upon amino acid mutations. BMC Syst Biol 11(4):93

  Article  Google Scholar 

• Gupta SK, Kececioglu JD, Schäffer AA (1995) Improving the practical space and time efficiency of the shortest-paths approach to sum-of-pairs multiple sequence alignment. J Comput Biol 2(3):459–472

  Article  Google Scholar 

• Gusfield D (2015) Persistent phylogeny: a galled-tree and integer linear programming approach. In: Proceedings of the 6th ACM conference on bioinformatics, computational biology and health informatics, BCB ’15, ACM, New York, USA, pp 443–451

  Google Scholar 

• Han Y, Gao S, Muegge K, Zhang W, Zhou B (2015) Advanced applications of RNA sequencing and challenges. Bioinform Biol Insights 9s1:BBI.S28991

  Google Scholar 

• Hassanien A-E, Milanova MG, Smolinski TG, Abraham A (2008) Computational intelligence in solving bioinformatics problems: reviews, perspectives, and challenges. Springer, Berlin, pp 3–47

  Google Scholar 

• Hassanien AE, Al-Shammari ET, Ghali NI (2013) Computational intelligence techniques in bioinformatics. Comput Biol Chem 47:37–47

  Article  Google Scholar 

• Hastings J, de Matos P, Dekker A, Ennis M, Harsha B, Kale N, Muthukrishnan V, Owen G, Turner S, Williams M, Steinbeck C (2013) The chebi reference database and ontology for biologically relevant chemistry: enhancements for 2013. Nucleic Acids Res 41(D1):D456–D463

  Article  Google Scholar 

• Hatem M, Ruml W (2013) External memory best-first search for multiple sequence alignment. In: 27th AAAI conference on artificial intelligence

  Google Scholar 

• Hayes-Roth B, Buchanan BG, Lichtarge O, Hewitt M, Altman RB, Brinkley JF, Cornelius C, Duncan BS, Jardetzky O (1986) PROTEAN: deriving protein structure from constraints. In: AAAI, pp 904–909

  Google Scholar 

• Heffernan R, Paliwal K, Lyons J, Dehzangi A, Sharma A, Wang J, Sattar A, Yang Y, Zhou Y (2015) Improving prediction of secondary structure, local backbone angles, and solvent accessible surface area of proteins by iterative deep learning. Sci Rep 5:11476

  Article  Google Scholar 

• Helma C, Kramer S, Luc DR (2002) The molecular feature miner MOLFEA. In: Proceedings of the Beilstein workshop 2002: molecular informatics: confronting complexity; Beilstein Institut, pp 79–93

  Google Scholar 

• Hinselmann G, Rosenbaum L, Jahn A, Fechner N, Ostermann C, Zell A (2011) Large-scale learning of structure- activity relationships using a linear support vector machine and problem-specific metrics. J Chem Inf Model 51(2):203–213

  Article  Google Scholar 

• Hirschman L, Park JC, Tsujii J, Wong L, Wu CH (2002) Accomplishments and challenges in literature data mining for biology. Bioinformatics 18(12):1553–1561

  Article  Google Scholar 

• Hoehndorf R, Slater L, Schofield PN, Gkoutos GV (2015) Aber-owl: a framework for ontology-based data access in biology. BMC Bioinform 16(1):26

  Article  Google Scholar 

• Hoff KJ, Lange S, Lomsadze A, Borodovsky M, Stanke M (2015) Braker1: unsupervised rna-seq-based genome annotation with genemark-et and augustus. Bioinformatics 32(5):767–769

  Article  Google Scholar 

• Hoff K, Stanke M (2015) Current methods for automated annotation of protein-coding genes. Curr Opin Insect Sci 7(Supplement C):8–14. Insect genomics * Development and regulation

  Google Scholar 

• Holzinger A, Dehmer M, Jurisica I (2014) Knowledge discovery and interactive data mining in bioinformatics-state-of-the-art, future challenges and research directions. BMC Bioinform 15(6):I1

  Article  Google Scholar 

• Hosoda M, Akune Y, Aoki-Kinoshita KF (2017) Development and application of an algorithm to compute weighted multiple glycan alignments. Bioinformatics 33(9):1317–1323

  Google Scholar 

• Huang DW, Sherman BT, Lempicki RA (2008) Systematic and integrative analysis of large gene lists using David bioinformatics resources. Nat Protoc 4(1):44

  Article  Google Scholar 

• Hundley L, Lederberg J, Levinthal E (1963) Multivator- a biochemical laboratory for Martian experiments. Technical report NSG-81-60, NASA

  Google Scholar 

• Huntley RP, Harris MA, Alam-Faruque Y, Blake JA, Carbon S, Dietze H, Dimmer EC, Foulger RE, Hill DP, Khodiyar VK, Lock A, Lomax J, Lovering RC, Mutowo-Meullenet P, Sawford T, Van Auken K, Wood V, Mungall CJ (2014) A method for increasing expressivity of gene ontology annotations using a compositional approach. BMC Bioinform 15(1):155

  Article  Google Scholar 

• Ideker T, Nussinov R (2017) Network approaches and applications in biology. PLOS Comput Biol 13(10):1–3

  Article  Google Scholar 

• Ikeda T, Imai H (1999) Enhanced A* algorithms for multiple alignments: optimal alignments for several sequences and k-opt approximate alignments for large cases. Theor Comput Sci 210(2):341–374

  Article  MathSciNet  MATH  Google Scholar 

• Inokuchi A, Washio T, Motoda H (2000) An apriori-based algorithm for mining frequent substructures from graph data. In: Zighed DA, Komorowski J, Żytkow J (eds) Principles of data mining and knowledge discovery. Springer, Berlin, pp 13–23

  Chapter  Google Scholar 

• Inoue K (2011) Logic programming for Boolean networks. In: Proceedings of the twenty-second international joint conference on artificial intelligence - volume volume two, IJCAI’11. AAAI Press, pp 924–930

  Google Scholar 

• Inza I, Calvo B, Armañanzas R, Bengoetxea E, Larrañaga P, Lozano JA (2010) Machine learning: an indispensable tool in bioinformatics. Humana Press, Totowa, pp 25–48

  Google Scholar 

• Islamaj Doğan R, Kim S, Chatr-aryamontri A, Chang CS, Oughtred R, Rust J, Wilbur WJ, Comeau DC, Dolinski K, Tyers M (2017) The bioc-biogrid corpus: full text articles annotated for curation of protein-protein and genetic interactions. Database 2017(1):baw147

  Google Scholar 

• Jiang S-Y, Ramachandran S (2010) Assigning biological functions to rice genes by genome annotation, expression analysis and mutagenesis. Biotechnol Lett 32(12):1753–1763

  Article  Google Scholar 

• Jones DT, Singh T, Kosciolek T, Tetchner S (2015) Metapsicov: combining coevolution methods for accurate prediction of contacts and long range hydrogen bonding in proteins. Bioinformatics 31(7):999–1006

  Article  Google Scholar 

• Jong K, Marchiori E, Sebag M, Van Der Vaart A (2004) Feature selection in proteomic pattern data with support vector machines. In: Proceedings of the 2004 IEEE symposium on computational intelligence in bioinformatics and computational biology, 2004, CIBCB’04. IEEE, pp 41–48

  Google Scholar 

• Judson PN (2014) Knowledge-based approaches for predicting the sites and products of metabolism, chapter 12. Wiley-Blackwell, pp 293–318

  Google Scholar 

• Jupp S, Malone J, Bolleman J, Brandizi M, Davies M, Garcia L, Gaulton A, Gehant S, Laibe C, Redaschi N, Wimalaratne SM, Martin M, Le Novère N, Parkinson H, Birney E, Jenkinson AM (2014) The EBI RDF platform: linked open data for the life sciences. Bioinformatics 30(9):1338–1339

  Article  Google Scholar 

• Källberg M, Wang H, Wang S, Peng J, Wang Z, Lu H, Xu J (2012) Template-based protein structure modeling using the RaptorX web server. Nat Protoc 7(8):1511–1522

  Article  Google Scholar 

• Kaminski R, Schaub T, Siegel A, Videla S (2013) Minimal intervention strategies in logical signaling networks with ASP. Theory Pract Log Program 13(4–5):675–690

  Article  MathSciNet  MATH  Google Scholar 

• Kanehisa M (2017) Kegg glycan. In: A practical guide to using glycomics databases. Springer, Berlin, pp 177–193

  Google Scholar 

• Karpe PD, Latendresse M, Caspi R (2011) The pathway tools pathway prediction algorithm. Stand Genomic Sci 5(3):424

  Article  Google Scholar 

• Kavanagh J, Mitchell D, Ternovska E, Maňuch J, Zhao X, Gupta A (2006) Constructing Camin-Sokal phylogenies via answer set programming. Springer Berlin Heidelberg, Berlin, Heidelberg, pp 452–466

  Google Scholar 

• Keedwell E, Narayanan A (2005) Intelligent bioinformatics: the application of artificial intelligence techniques to bioinformatics problems. Wiley, New York

  Book  Google Scholar 

• Khatri P, Sirota M, Butte AJ (2012) Ten years of pathway analysis: current approaches and outstanding challenges. PLOS Comput Biol 8(2):1–10

  Article  Google Scholar 

• Kislyuk A, Lomsadze A, Lapidus AL, Borodovsky M (2009) Frameshift detection in prokaryotic genomic sequences. Int J Bioinform Res Appl 5(4):458–477

  Article  Google Scholar 

• Kitchen DB, Decornez H, Furr JR, Bajorath J (2004) Docking and scoring in virtual screening for drug discovery: methods and applications. Nat Rev Drug Discov 3(11):935

  Article  Google Scholar 

• Klamt S (2006) Generalized concept of minimal cut sets in biochemical networks. Biosystems 83(2–3):233–247

  Article  Google Scholar 

• Klarner H, Bockmayr A, Siebert H (2015) Computing maximal and minimal trap spaces of Boolean networks. Nat Comput 14(4):535–544

  Article  MathSciNet  MATH  Google Scholar 

• Klarner H, Streck A, Siebert H (2017) Pyboolnet: a python package for the generation, analysis and visualization of Boolean networks. Bioinformatics 33(5):770–772

  Google Scholar 

• Koliński A et al (2004) Protein modeling and structure prediction with a reduced representation. Acta Biochim Pol 51

  Google Scholar 

• Koponen L, Oikarinen E, Janhunen T, Säilä L (2015) Optimizing phylogenetic supertrees using answer set programming. Theory Pract Log Program 15(4–5):604–619

  Article  MathSciNet  MATH  Google Scholar 

• Korf RE, Zhang W, Thayer I, Hohwald H (2005) Frontier search. J ACM 52(5):715–748

  Article  MathSciNet  MATH  Google Scholar 

• Korostensky C, Gonnet G (1999) Near optimal multiple sequence alignments using a traveling salesman problem approach. In: SPIRE/CRIWG, pp 105–114

  Google Scholar 

• Koshino M, Murata H, Shirayama M, Kimura H (2006) Applying the various optimal solution search methods to multiple sequence alignments and performance evaluation. Syst Comput Jpn 37(11):1–10

  Article  Google Scholar 

• Kowalski R (1979) Algorithm \(=\) logic \(+\) control. Commun ACM 22(7):424–436

  Google Scholar 

• Kumozaki S, Sato K, Sakakibara Y (2015) A machine learning based approach to de novo sequencing of glycans from tandem mass spectrometry spectrum. IEEE/ACM Trans Comput Biol Bioinform 12(6):1267–1274

  Article  Google Scholar 

• Lacroix V, Sammeth M, Guigo R, Bergeron A (2008) Exact transcriptome reconstruction from short sequence reads. In: International workshop on algorithms in bioinformatics. Springer, pp 50–63

  Google Scholar 

• Lai J, An J, Seim I, Walpole C, Hoffman A, Moya L, Srinivasan S, Perry-Keene JL, Wang C, Lehman ML et al (2015) Fusion transcript loci share many genomic features with non-fusion loci. BMC Genomics 16(1):1021

  Article  Google Scholar 

• Lavecchia A (2015) Machine-learning approaches in drug discovery: methods and applications. Drug Discov Today 20(3):318–331

  Article  Google Scholar 

• Le Novere N (2015) Quantitative and logic modelling of gene and molecular networks. Nature Rev Genet 16(3):146

  Article  Google Scholar 

• Le T, Nguyen H, Pontelli E, Son TC (2012) ASP at work: an ASP implementation of PhyloWS. In: Dovier A, Costa VS (eds) Technical communications of the 28th international conference on logic programming (ICLP’12), volume 17 of Leibniz international proceedings in informatics (LIPIcs), pp 359–369, Dagstuhl. Schloss Dagstuhl-Leibniz-Zentrum fuer Informatik, Germany

  Google Scholar 

• Lepailleur A, Poezevara G, Bureau R (2013) Automated detection of structural alerts (chemical fragments) in (eco)toxicology. Comput Struct Biotechnol J 5(6):e201302013

  Article  Google Scholar 

• Lertampaiporn S, Thammarongtham C, Nukoolkit C, Kaewkamnerdpong B, Ruengjitchatchawalya M (2014) Identification of non-coding RNAs with a new composite feature in the hybrid random forest ensemble algorithm. Nucleic Acids Res 42(11):e93

  Article  Google Scholar 

• Li L, Zhang Y, Zou L, Li C, Yu B, Zheng X, Zhou Y (2012) An ensemble classifier for eukaryotic protein subcellular location prediction using gene ontology categories and amino acid hydrophobicity. PLOS One 7(1):1–12

  Google Scholar 

• Li F, Li C, Wang M, Webb GI, Zhang Y, Whisstock JC, Song J (2015) Glycomine: a machine learning-based approach for predicting n-, c-and o-linked glycosylation in the human proteome. Bioinformatics 31(9):1411–1419

  Article  Google Scholar 

• Li H, Hou J, Adhikari B, Lyu Q, Cheng J (2017) Deep learning methods for protein torsion angle prediction. BMC Bioinform 18(1):417

  Article  Google Scholar 

• Lihu A, Holban Ş (2015) A review of ensemble methods for de novo motif discovery in ChIP-Seq data. Brief Bioinform 16(6):964–973

  Article  Google Scholar 

• Lim CY, Wang H, Woodhouse S, Piterman N, Wernisch L, Fisher J, Göttgens B (2016) BTR: training asynchronous Boolean models using single-cell expression data. BMC Bioinform 17(1):355

  Article  Google Scholar 

• Lindsay RK, Buchanan BG, Feigenbaum EA, Lederberg J (1993) Dendral: a case study of the first expert system for scientific hypothesis formation. Artif Intell 61(2):209–261

  Article  Google Scholar 

• Liu H, Liu L, Zhang H (2010) Ensemble gene selection for cancer classification. Pattern Recognit 43(8):2763–2772

  Article  Google Scholar 

• Lomsadze A, Ter-Hovhannisyan V, Chernoff YO, Borodovsky M (2005) Gene identification in novel eukaryotic genomes by self-training algorithm. Nucleic Acids Res 33(20):6494–6506

  Article  Google Scholar 

• Lomsadze A, Burns PD, Borodovsky M (2014) Integration of mapped RNA-Seq reads into automatic training of eukaryotic gene finding algorithm. Nucleic Acids Res 42(15):e119

  Article  Google Scholar 

• Magnan CN, Baldi P (2014) SSpro/ACCpro 5: almost perfect prediction of protein secondary structure and relative solvent accessibility using profiles, machine learning and structural similarity. Bioinformatics 30(18):2592–2597

  Article  Google Scholar 

• Mahadevan R, von Kamp A, Klamt S (2015) Genome-scale strain designs based on regulatory minimal cut sets. Bioinformatics 31(17):2844–2851

  Article  Google Scholar 

• Mahé P, Vert J-P (2009) Graph kernels based on tree patterns for molecules. Mach Learn 75(1):3–35

  Article  Google Scholar 

• Malikic S, McPherson AW, Donmez N, Sahinalp CS (2015) Clonality inference in multiple tumor samples using phylogeny. Bioinformatics 31(9):1349–1356

  Article  Google Scholar 

• Mamitsuka H (2011) Glycoinformatics: data mining-based approaches. CHIMIA Int J Chem 65(1):10–13

  Article  Google Scholar 

• Mann M, Backofen R (2014) Exact methods for lattice protein models. Bio-Algorithms Med-Syst 10(4):213–225

  Google Scholar 

• Mann M, Will S, Backofen R (2008) CPSP-tools – Exact and complete algorithms for high-throughput 3D lattice protein studies. BMC Bioinformatcis 9(1):230

  Google Scholar 

• Martin M, Dague P, Pérès S, Simon L (2016) Minimality of metabolic flux modes under Boolean regulation constraints. In: 12th International workshop on constraint-based methods for bioinformatics WCB’16, Toulouse, France

  Google Scholar 

• Matentzoglu N, Vigo M, Jay C, Stevens R (2017) Inference inspector: improving the verification of ontology authoring actions. Web semantics: science services and agents on the World Wide Web

  Google Scholar 

• Maupetit J, Gautier R, Tufféry P (2006) SABBAC: online structural alphabet-based protein backbone reconstruction from alpha-carbon trace. Nucleic Acids Res 34(suppl\({\_}\)2):W147–W151

  Google Scholar 

• McGuffin LJ, Bryson K, Jones DT (2000) The psipred protein structure prediction server. Bioinformatics 16(4):404–405

  Article  Google Scholar 

• Medina and Tárraga, J., Martínez, H., Barrachina, S., Castillo, M. I., Paschall, J., Salavert-Torres, J., Blanquer-Espert, I., Hernández-García, V., Quintana-Ortí, E. S., and Dopazo, J (2016) Highly sensitive and ultrafast read mapping for RNA-seq analysis. DNA Res 23(2):93–100

  Google Scholar 

• Mei S (2012) Multi-label Multi-Kernel Transfer Learning for Human Protein Subcellular Localization. PLOS ONE 7(6):1–12

  Article  Google Scholar 

• Mei S, Zhu H (2014) AdaBoost based multi-instance transfer learning for predicting proteome-wide interactions between salmonella and human proteins. PLOS ONE 9(10):1–12

  Google Scholar 

• Merelli E, Armano G, Cannata N, Corradini F, d’Inverno M, Doms A, Lord P, Martin A, Milanesi L, Möller S, Schroeder M, Luck M (2007) Agents in bioinformatics, computational and systems biology. Brief Bioinform 8(1):45

  Article  Google Scholar 

• Mestres J, Gregori-Puigjane E, Valverde S, Sole RV (2008) Data completeness-the Achilles heel of drug-target networks. Nature Biotechnol 26(9):983

  Article  Google Scholar 

• MGlincy NJ, Ingolia NT (2017) Transcriptome-wide measurement of translation by ribosome profiling. Methods 126:112–129. Post-transcriptional regulation of gene expression

  Google Scholar 

• Michel AM, Choudhury KR, Firth AE, Ingolia NT, Atkins JF, Baranov PV (2012) Observation of dually decoded regions of the human genome using ribosome profiling data. Genome Res 22(11):2219–2229

  Article  Google Scholar 

• Midic U, Dunker AK, Obradovic Z (2005) Improving protein secondary-structure prediction by predicting ends of secondary-structure segments. In: Proceedings of the 2005 IEEE symposium on computational intelligence in bioinformatics and computational biology, CIBCB’05. IEEE, pp 1–8

  Google Scholar 

• Miranda M, Lynce I, Manquinho V (2014) Inferring phylogenetic trees using pseudo-Boolean optimization. AI Commun 27(3):229–243

  Article  MathSciNet  MATH  Google Scholar 

• Mitra S, Datta S, Perkins T, Michailidis G (2008) Introduction to machine learning and bioinformatics. CRC Press, Boca Raton

  Book  MATH  Google Scholar 

• Miyahara T, Kuboyama T (2014) Learning of glycan motifs using genetic programming and various fitness functions. J Adv Comput Intell Intell Inform 18(3):401–408

  Article  Google Scholar 

• Moll M, Schwarz D, Kavraki LE (2008) Roadmap methods for protein folding. Protein Struct Predict 219–239

  Google Scholar 

• Monteiro PT, Ropers D, Mateescu R, Freitas AT, de Jong H (2008) Temporal logic patterns for querying dynamic models of cellular interaction networks. Bioinformatics 24(16):i227–i233

  Article  Google Scholar 

• Moult J, Fidelis K, Kryshtafovych A, Schwede T, Tramontano A (2018) Critical assessment of methods of protein structure prediction (CASP)—round xii. Proteins: Struct Funct Bioinform 86:7–15

  Article  Google Scholar 

• Muggleton S, King RD, Stenberg MJ (1992) Protein secondary structure prediction using logic-based machine learning. Protein Eng Des Sel 5(7):647–657

  Article  Google Scholar 

• Muller J, Creevey CJ, Thompson JD, Arendt D, Bork P (2010) Aqua: automated quality improvement for multiple sequence alignments. Bioinformatics 26(2):263–265

  Article  Google Scholar 

• Mungall CJ, Torniai C, Gkoutos GV, Lewis SE, Haendel MA (2012) Uberon, an integrative multi-species anatomy ontology. Genome Biology 13(1):R5

  Article  Google Scholar 

• Mushthofa M, Torres G, Van de Peer Y, Marchal K, De Cock M (2014) ASP-G: an ASP-based method for finding attractors in genetic regulatory networks. Bioinformatics 30(21):3086–3092

  Article  Google Scholar 

• Nagi S, Bhattacharyya DK, Kalita JK (2017) Complex detection from ppi data using ensemble method. Netw Model Anal Health Inform Bioinform 6(1):3

  Article  Google Scholar 

• Naik AW, Kangas JD, Sullivan DP, Murphy RF (2016) Active machine learning-driven experimentation to determine compound effects on protein patterns. eLife 5:e10047

  Google Scholar 

• Naldi A, Carneiro J, Chaouiya C, Thieffry D (2010) Diversity and plasticity of th cell types predicted from regulatory network modelling. PLOS Comput Biology 6(9):1–16

  Article  Google Scholar 

• Nesbeth DN, Zaikin A, Saka Y, Romano MC, Giuraniuc CV, Kanakov O, Laptyeva T (2016) Synthetic biology routes to bio-artificial intelligence. Essays Biochem 60(4):381–391

  Article  Google Scholar 

• Newman A, Ruhl M (2004) Combinatorial problems on strings with applications to protein folding. In: Farach-Colton M (ed) LATIN 2004: theoretical informatics. Springer Berlin Heidelberg, Berlin, Heidelberg, pp 369–378

  Google Scholar 

• Ng C-T, Li C, Fan X (2017) A fast algorithm for reconstructing multiple sequence alignment and phylogeny simultaneously. Current Bioinform 12:329–348

  Article  Google Scholar 

• Offmann B, Tyagi M, de Brevern AG (2007) Local protein structures. Current Bioinform 2(3):165–202

  Article  Google Scholar 

• Okun O, Priisalu H (2007) Random forest for gene expression based cancer classification: overlooked issues. Pattern Recognit Image Anal 483–490

  Google Scholar 

• Omar M, Salam R, Abdullah R, Rashid N (2005) Multiple sequence alignment using optimization algorithms. Int J Comput Intell 1(2):81–89

  Google Scholar 

• Pandini A, Fornili A, Kleinjung J (2010) Structural alphabets derived from attractors in conformational space. BMC Bioinform 11(1):97

  Article  Google Scholar 

• Park YR, Kim J, Lee HW, Yoon YJ, Kim JH (2011) Gochase-ii: correcting semantic inconsistencies from gene ontology-based annotations for gene products. BMC Bioinform 12(1):S40

  Article  Google Scholar 

• Pashaei E, Ozen M, Aydin N (2017) Splice site identification in human genome using random forest. Health Technol 7(1):141–152

  Article  Google Scholar 

• Pashaei E, Aydin N (2017) Frequency difference based DNA encoding methods in human splice site recognition. In: 2017 international conference on computer science and engineering (UBMK). IEEE, pp 586–591

  Google Scholar 

• Pashaei E, Ozen M, Aydin N (2016a) Splice sites prediction of human genome using adaboost. In: 2016 IEEE-EMBS international conference on biomedical and health informatics (BHI). IEEE, pp 300–303

  Google Scholar 

• Pashaei E, Yilmaz A, Aydin N (2016b) A combined SVM and Markov model approach for splice site identification. In: 2016 6th international conference on computer and knowledge engineering (ICCKE). IEEE, pp 200–204

  Google Scholar 

• Peng J, Xu J (2010) Low-homology protein threading. Bioinformatics 26(12):i294–i300

  Article  Google Scholar 

• Pérez S, Sarkar A, Rivet A, Breton C, Imberty A (2015) Glyco3d: a portal for structural glycosciences. In: Glycoinformatics. Springer, pp 241–258

  Google Scholar 

• Pertea M, Pertea GM, Antonescu CM, Chang T-C, Mendell JT, Salzberg SL (2015) Stringtie enables improved reconstruction of a transcriptome from rna-seq reads. Nat Biotechnol 33(3):290–295

  Article  Google Scholar 

• Pes B, Dessì N, Angioni M (2017) Exploiting the ensemble paradigm for stable feature selection: a case study on high-dimensional genomic data. Inf Fus 35(Supplement:C):132–147

  Google Scholar 

• Petegrosso R, Park S, Hwang TH, Kuang R (2017) Transfer learning across ontologies for phenome-genome association prediction. Bioinformatics 33(4):529–536

  Google Scholar 

• Piao Y, Piao M, Park K, Ryu KH (2012) An ensemble correlation-based gene selection algorithm for cancer classification with gene expression data. Bioinformatics 28(24):3306–3315

  Article  Google Scholar 

• Pietal MJ, Bujnicki JM, Kozlowski LP (2015) GDFuzz3D: a method for protein 3D structure reconstruction from contact maps, based on a non-euclidean distance function. Bioinformatics 31(21):3499–3505

  Article  Google Scholar 

• Pirola Y, Rizzi R, Picardi E, Pesole G, Della Vedova G, Bonizzoni P (2012) Pintron: a fast method for detecting the gene structure due to alternative splicing via maximal pairings of a pattern and a text. BMC Bioinform 13(5):S2

  Article  Google Scholar 

• Pokarowski P, Kolinski A, Skolnick J (2003) A minimal physically realistic protein-like lattice model: designing an energy landscape that ensures all-or-none folding to a unique native state. Biophys J 84(3):1518–1526

  Article  Google Scholar 

• Popic V, Salari R, Hajirasouliha I, Kashef-Haghighi D, West RB, Batzoglou S (2015) Fast and scalable inference of multi-sample cancer lineages. Genome Biology 16(1):91

  Article  Google Scholar 

• Post LJG, Roos M, Marshall MS, van Driel R, Breit TM (2007) A semantic web approach applied to integrative bioinformatics experimentation: a biological use case with genomics data. Bioinformatics 23(22):3080–3087

  Article  Google Scholar 

• Raies AB, Bajic VB (2016) In silico toxicology: computational methods for the prediction of chemical toxicity. Wiley Interdiscip Rev: Comput Mol Sci 6(2):147–172

  Google Scholar 

• Ramsden JJ (2004) Bioinformatics: an introduction, Volume 21 of Computational biology. Springer

  Google Scholar 

• Rannug U, Sjögren M, Rannug A, Gillner M, Toftgård R, Gustafsson J-Å, Rosenkranz H, Klopman G (1991) Use of artificial intelligence in structure-affinity correlations of 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) receptor ligands. Carcinogenesis 12(11):2007–2015

  Article  Google Scholar 

• Ranzinger R, Aoki-Kinoshita KF, Campbell MP, Kawano S, Lütteke T, Okuda S, Shinmachi D, Shikanai T, Sawaki H, Toukach P, Matsubara M, Yamada I, Narimatsu H (2015) GlycoRDF: an ontology to standardize glycomics data in RDF. Bioinformatics 31(6):919–925

  Article  Google Scholar 

• Reid I, O’Toole N, Zabaneh O, Nourzadeh R, Dahdouli M, Abdellateef M, Gordon PM, Soh J, Butler G, Sensen CW, Tsang A (2014) Snowyowl: accurate prediction of fungal genes by using RNA-seq and homology information to select among ab initio models. BMC Bioinform 15(1):229

  Article  Google Scholar 

• Reker D, Schneider P, Schneider G (2016) Multi-objective active machine learning rapidly improves structure-activity models and reveals new protein-protein interaction inhibitors. Chem Sci 7:3919–3927

  Article  Google Scholar 

• Reker D, Schneider P, Schneider G, Brown J (2017) Active learning for computational chemogenomics. Futur Med Chem 9(4):381–402

  Article  Google Scholar 

• Requeno JI, Colom JM (2016) Evaluation of properties over phylogenetic trees using stochastic logics. BMC Bioinform 17(1):235

  Article  Google Scholar 

• Requeno JI, de Miguel Casado G, Blanco R, Colom JM (2013) Temporal logics for phylogenetic analysis via model checking. IEEE/ACM Trans Comput Biol Bioinform (TCBB) 10(4):1058–1070

  Article  Google Scholar 

• Rinaldi F, Lithgow O, Gama-Castro S, Solano H, López-Fuentes A, Muñiz Rascado LJ, Ishida-Gutiérrez C, Méndez-Cruz C-F, Collado-Vides J (2017) Strategies towards digital and semi-automated curation in RegulonDB. Database 2017:bax012

  Google Scholar 

• Robertson MP, Joyce GF (2012) The origins of the RNA world. Cold Spring Harb Perspect Biol 4(5):a003608

  Article  Google Scholar 

• Rodríguez-Penagos C, Salgado H, Martínez-Flores I, Collado-Vides J (2007) Automatic reconstruction of a bacterial regulatory network using natural language processing. BMC Bioinform 8(1):293

  Article  Google Scholar 

• Röhl A, Bockmayr A (2017) A mixed-integer linear programming approach to the reduction of genome-scale metabolic networks. BMC Bioinform 18(1):2

  Article  Google Scholar 

• Rost B, Sander C (1993) Prediction of protein secondary structure at better than 70% accuracy. J Mol Biol 232(2):584–599

  Article  Google Scholar 

• Sakiyama Y, Yuki H, Moriya T, Hattori K, Suzuki M, Shimada K, Honma T (2008) Predicting human liver microsomal stability with machine learning techniques. J Mol Graph Model 26(6):907–915

  Article  Google Scholar 

• Samaga R, Kamp AV, Klamt S (2010) Computing combinatorial intervention strategies and failure modes in signaling networks. J Comput Biol 17(1):39–53

  Article  MathSciNet  Google Scholar 

• Sarkar IN (2015) Mining the bibliome. In: Translational informatics. Springer, pp 75–96

  Google Scholar 

• Sawada R, Kotera M, Yamanishi Y (2014) Benchmarking a wide range of chemical descriptors for drug-target interaction prediction using a chemogenomic approach. Mol Inform 33(11–12):719–731

  Google Scholar 

• Schietgat L, Vens C, Struyf J, Blockeel H, Kocev D, Džeroski S (2010) Predicting gene function using hierarchical multi-label decision tree ensembles. BMC Bioinform 11(1):2

  Article  MATH  Google Scholar 

• Schroedl S (2005) An improved search algorithm for optimal multiple-sequence alignment. J Artif Intell Res 23:587–623

  Article  MathSciNet  MATH  Google Scholar 

• Schulte C, Stuckey PJ (2008) Efficient constraint propagation engines. Trans Program Lang Syst 31(1):2:1–2:43

  Google Scholar 

• Schwartz R, Schäffer AA (2017) The evolution of tumour phylogenetics: principles and practice. Nat Rev Genet 18(4):213

  Article  Google Scholar 

• Senger RS, Karim MN (2008) Prediction of n-linked glycan branching patterns using artificial neural networks. Math Biosci 211(1):89–104

  Article  MathSciNet  MATH  Google Scholar 

• Shao Z, Hirayama Y, Yamanishi Y, Saigo H (2015) Mining discriminative patterns from graph data with multiple labels and its application to quantitative structure-activity relationship (qsar) models. J Chem Inf Model 55(12):2519–2527 PMID: 26549421

  Article  Google Scholar 

• Shatabda S, Newton MAH, Sattar A (2014)Constraint-based evolutionary local search for protein structures with secondary motifs. In: Pham D-N, Park S-B (eds) PRICAI 2014: trends in artificial intelligence. Springer International Publishing, pp 333–344

  Google Scholar 

• Shaw DL, Islam AS, Rahman MS, Hasan M (2014) Protein folding in HP model on hexagonal lattices with diagonals. BMC Bioinform 15(2):S7

  Article  Google Scholar 

• Sheela T, Rangarajan L (2017) Combination of feature selection methods for the effective classification of microarray gene expression data. In: Santosh K, Hangarge M, Bevilacqua V, Negi A (eds) Recent trends in image processing and pattern recognition: first international conference, RTIP2R 2016, Bidar, India, 16–17 Dec 2016, Revised Selected Papers. Springer, Singapore, pp 137–145

  Google Scholar 

• Sherhod R, Judson PN, Hanser T, Vessey JD, Webb SJ, Gillet VJ (2014) Emerging pattern mining to aid toxicological knowledge discovery. J Chem Inf Model 54(7):1864–1879

  Article  Google Scholar 

• Shvaiko P, Euzenat J (2013) Ontology matching: state of the art and future challenges. IEEE Trans Knowl Data Eng 25(1):158–176

  Article  Google Scholar 

• Singh GB (2015) Introduction to bioinformatics. In: Fundamentals of bioinformatics and computational biology. Springer, pp 3–10

  Google Scholar 

• Singh H, Singh S, Raghava GPS (2014) Evaluation of protein dihedral angle prediction methods. PLOS ONE 9(8):1–9

  Google Scholar 

• Skwark MJ, Raimondi D, Michel M, Elofsson A (2014) Improved contact predictions using the recognition of protein like contact patterns. PLOS Comput Biology 10(11):1–14

  Article  Google Scholar 

• Smith B, Ashburner M, Rosse C, Bard J, Bug W, Ceusters W, Goldberg LJ, Eilbeck K, Ireland A, Mungall CJ et al (2007) The obo foundry: coordinated evolution of ontologies to support biomedical data integration. Nature Biotechnol 25(11):1251–1255

  Article  Google Scholar 

• Smitha SKN, Reddy SN (2016) Amyloid motif prediction using ensemble approach. Current Bioinform 11(3):357–365

  Article  Google Scholar 

• Song J, Burrage K, Yuan Z, Huber T (2006) Prediction of cis/trans isomerization in proteins using psi-blast profiles and secondary structure information. BMC Bioinform 7(1):124

  Article  Google Scholar 

• Soon WW, Hariharan M, Snyder MP (2013) High-throughput sequencing for biology and medicine. Mol Syst Biology 9(1):640

  Article  Google Scholar 

• Spencer M, Eickholt J, Cheng J (2015) A deep learning network approach to ab initio protein secondary structure prediction. IEEE/ACM Trans Comput Biol Bioinform 12(1):103–112

  Article  Google Scholar 

• Sridhar S, Lam F, Blelloch GE, Ravi R, Schwartz R (2008) Mixed integer linear programming for maximum-parsimony phylogeny inference. IEEE/ACM Trans Comput Biol Bioinform 5(3):323–331

  Article  Google Scholar 

• Sternberg MJ, Tamaddoni-Nezhad A, Lesk VI, Kay E, Hitchen PG, Cootes A, van Alphen LB, Lamoureux MP, Jarrell HC, Rawlings CJ, Soo EC, Szymanski CM, Dell A, Wren BW, Muggleton SH (2013) Gene function hypotheses for the campylobacter jejuni glycome generated by a logic-basedApproach. J Mol Biol 425(1):186–197

  Article  Google Scholar 

• Stevens R, Goble CA, Bechhofer S (2000) Ontology-based knowledge representation for bioinformatics. Brief Bioinform 1(4):398–414

  Article  Google Scholar 

• Sundfeld D, Razzolini C, Teodoro G, Boukerche A, de Melo ACMA (2017) Pa-star: a disk-assisted parallel a-star strategy with locality-sensitive hash for multiple sequence alignment. J Parallel Distrib Comput

  Google Scholar 

• Takigawa I, Mamitsuka H (2013) Graph mining: procedure, application to drug discovery and recent advances. Drug Discov Today 18(1):50–57

  Article  Google Scholar 

• Takigawa I, Hashimoto K, Shiga M, Kanehisa M, Mamitsuka H (2010) Mining patterns from glycan structures, pp 13–24

  Google Scholar 

• The GO Consortium (2017) Expansion of the gene ontology knowledgebase and resources. Nucl Acids Res 45(D1):D331–D338

  Google Scholar 

• Tiemeyer M, Aoki K, Paulson J, Cummings RD, York WS, Karlsson NG, Lisacek F, Packer NH, Campbell MP, Aoki NP et al (2017) GlyTouCan: an accessible glycan structure repository. Glycobiology 27(10):915–919

  Article  Google Scholar 

• Traoré S, Roberts KE, Allouche D, Donald BR, André I, Schiex T, Barbe S (2016) Fast search algorithms for computational protein design. J Comput Chem 37(12):1048–1058

  Article  Google Scholar 

• Traynard P, Fauré A, Fages F, Thieffry D (2016) Logical model specification aided by model-checking techniques: application to the mammalian cell cycle regulation. Bioinformatics 32(17):i772–i780

  Article  Google Scholar 

• Ueda N, Aoki-Kinoshita KF, Yamaguchi A, Akutsu T, Mamitsuka H (2005) A probabilistic model for mining labeled ordered trees: capturing patterns in carbohydrate sugar chains. IEEE Trans Knowl Data Eng 17(8):1051–1064

  Article  Google Scholar 

• Ugarte W, Boizumault P, Crémilleux B, Lepailleur A, Loudni S, Plantevit M, Raïssi C, Soulet A (2017) Skypattern mining: from pattern condensed representations to dynamic constraint satisfaction problems. Artificial Intell 244:48–69. Combining constraint solving with mining and learning

  Google Scholar 

• Vendruscolo M, Kussell E, Domany E (1997) Recovery of protein structure from contact maps. Fold Des 2(5):295–306

  Article  Google Scholar 

• Verfaillie G, Jussien N (2005) Constraint solving in uncertain and dynamic environments: A survey. Constraints 10(3):253–281

  Article  MathSciNet  MATH  Google Scholar 

• Vert J-P, Jacob L (2008) Machine learning for in silico virtual screening and chemical genomics: new strategies. Comb Chem High Throughput Screen 11(8):677–685

  Article  Google Scholar 

• Videla S, Saez-Rodriguez J, Guziolowski C, Siegel A (2017) caspo: a toolbox for automated reasoning on the response of logical signaling networks families. Bioinformatics 33(6):947–950

  Google Scholar 

• Videla S, Guziolowski C, Eduati F, Thiele S, Gebser M, Nicolas J, Saez-Rodriguez J, Schaub T, Siegel A (2015) Learning Boolean logic models of signaling networks with ASP. Theor Comput Sci 599(Supplement C):79 – 101. Advances in computational methods in systems biology

  Google Scholar 

• von Kamp A, Klamt S (2014) Enumeration of smallest intervention strategies in genome-scale metabolic networks. PLoS Comput Biology 10(1):e1003378

  Article  Google Scholar 

• Wald R, Khoshgoftaar TM, Dittman D, Awada W, Napolitano A (2012) An extensive comparison of feature ranking aggregation techniques in bioinformatics. In: 2012 IEEE 13th international conference on information reuse and integration (IRI). IEEE, pp 377–384

  Google Scholar 

• Walker SI, Davies PC (2013) The algorithmic origins of life. J R Soc Interface 10(79):20120869

  Article  Google Scholar 

• Wan S, Mak M-W, Kung S-Y (2014) HybridGO-Loc: mining hybrid features on gene ontology for predicting subcellular localization of multi-location proteins. PLOS ONE 9(3):1–12

  Google Scholar 

• Wang Z, Xu J (2013) Predicting protein contact map using evolutionary and physical constraints by integer programming. Bioinformatics 29(13):i266–i273

  Article  MathSciNet  Google Scholar 

• Wang X, Xuan Z, Zhao X, Li Y, Zhang MQ (2009) High-resolution human core-promoter prediction with CoreBoost\_HM. Genome Res 19(2):266–275

  Article  Google Scholar 

• Wang S, Li W, Liu S, Xu J (2016a) RaptorX-Property: a web server for protein structure property prediction. Nucl Acids Res 44(W1):W430–W435

  Article  Google Scholar 

• Wang S, Li W, Zhang R, Liu S, Xu J (2016b) CoinFold: a web server for protein contact prediction and contact-assisted protein folding. Nucl Acids Res 44(W1):W361–W366

  Article  Google Scholar 

• Wang S, Sun S, Li Z, Zhang R, Xu J (2017a) Accurate de novo prediction of protein contact map by ultra-deep learning model. PLOS Comput Biology 13(1):1–34

  Google Scholar 

• Wang Y, Mao H, Yi Z (2017b) Protein secondary structure prediction by using deep learning method. Knowl Based Syst 118:115–123

  Article  Google Scholar 

• Wang S, Peng J, Ma J, Xu J (2016c) Protein secondary structure prediction using deep convolutional neural fields. Scientific Rep 6(18962)

  Google Scholar 

• Wei D, Zhang H, Wei Y, Jiang Q (2013) A novel splice site prediction method using support vector machine. J Comput Inf Syst 9(20):8053–8060

  Google Scholar 

• Wei K, Iyer R, Bilmes J (2015) Submodularity in data subset selection and active learning. In: Bach F, Blei D (eds) Proceedings of the 32nd international conference on machine learning, volume 37 of Proceedings of machine learning research, Lille, France, PMLR, pp 1954–1963

  Google Scholar 

• Whetzel PL, Noy NF, Shah NH, Alexander PR, Nyulas C, Tudorache T, Musen MA (2011) Bioportal: enhanced functionality via new web services from the national center for biomedical ontology to access and use ontologies in software applications. Nucleic Acids Res 39(suppl\(_2\)):W541–W545

  Google Scholar 

• Wu B, Abbott T, Fishman D, McMurray W, Mor G, Stone K, Ward D, Williams K, Zhao H (2003) Comparison of statistical methods for classification of ovarian cancer using mass spectrometry data. Bioinformatics 19(13):1636–1643

  Article  Google Scholar 

• Wu G, You J-H, Lin G (2007) Quartet-based phylogeny reconstruction with answer set programming. IEEE/ACM Trans Comput Biol Bioinform 4(1)

  Google Scholar 

• Wuyun Q, Zheng W, Peng Z, Yang J (2016) A large-scale comparative assessment of methods for residue–residue contact prediction. Brief Bioinform bbw106

  Google Scholar 

• Xia J-F, Wu M, You Z-H, Zhao X-M, Li X-L (2010) Prediction of \(\beta \)-hairpins in proteins using physicochemical properties and structure information. Protein Pept Lett 17(9):1123–1128

  Google Scholar 

• Xiao Y, Dougherty ER (2007) The impact of function perturbations in Boolean networks. Bioinformatics 23(10):1265–1273

  Article  Google Scholar 

• Xue LC, Rodrigues JP, Dobbs D, Honavar V, Bonvin AM (2017) Template-based protein-protein docking exploiting pairwise interfacial residue restraints. Brief Bioinform 18(3):458–466

  Google Scholar 

• Yamanishi Y, Bach F, Vert J-P (2007) Glycan classification with tree kernels. Bioinformatics 23(10):1211–1216

  Article  Google Scholar 

• Yanev N, Traykov M, Milanov P, Yurukov B (2017) Protein folding prediction in a cubic lattice in hydrophobic-polar model. J Comput Biology 24(5):412–421

  Article  MathSciNet  Google Scholar 

• Yang P, Ho JW, Yang YH, Zhou BB (2011) Gene-gene interaction filtering with ensemble of filters. BMC Bioinform 12(1):S10

  Article  Google Scholar 

• Yang P, Humphrey SJ, James DE, Yang YH, Jothi R (2016a) Positive-unlabeled ensemble learning for kinase substrate prediction from dynamic phosphoproteomics data. Bioinformatics 32(2):252–259

  Google Scholar 

• Yang Y, Gao J, Wang J, Heffernan R, Hanson J, Paliwal K, Zhou Y (2016b). Sixty-five years of the long march in protein secondary structure prediction: the final stretch? Brief Bioinform bbw129

  Google Scholar 

• Yang P, Hwa Yang JY, Zhou BB, Zomaya AY (2010) A review of ensemble methods in bioinformatics. Current Bioinform 5(4):296–308

  Google Scholar 

• Yoshizumi T, Miura T, Ishida T (2000) A* with partial expansion for large branching factor problems. In: AAAI/IAAI, pp 923–929

  Google Scholar 

• Yuan G-C, Cai L, Elowitz M, Enver T, Fan G, Guo G, Irizarry R, Kharchenko P, Kim J, Orkin S, Quackenbush J, Saadatpour A, Schroeder T, Shivdasani R, Tirosh I (2017) Challenges and emerging directions in single-cell analysis. Genome Biol 18(1):84

  Article  Google Scholar 

• Zhang Y, Rajapakse JC (2009) Machine learning in bioinformatics, vol 4. Wiley, New York

  Google Scholar 

• Zhang T, Faraggi E, Li Z, Zhou Y (2013) Intrinsically semi-disordered state and its role in induced folding and protein aggregation. Cell Biochem Biophys 67(3):1193–1205

  Article  Google Scholar 

• Zhao Y, Hayashida M, Akutsu T (2010) Integer programming-based method for grammar-based tree compression and its application to pattern extraction of glycan tree structures. BMC Bioinform 11(11):S4

  Article  Google Scholar 

• Zhao Y, Hayashida M, Cao Y, Hwang J, Akutsu T (2015) Grammar-based compression approach to extraction of common rules among multiple trees of glycans and rnas. BMC Bioinform 16(1):128

  Article  Google Scholar 

• Zhou R, Hansen EA (2004) Space-efficient memory-based heuristics. In: AAAI, pp 677–682

  Google Scholar 

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