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Mehmet Tan
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2020 – today
- 2022
- [c21]Bahattin Can Maral
, Mehmet Tan
Transfer Learning for Predicting Gene Regulatory Effects of Chemicals. AIAI (2) 2022: 414-425 - 2021
- [j12]Batuhan Bardak
Improving clinical outcome predictions using convolution over medical entities with multimodal learning. Artif. Intell. Medicine 117: 102112 (2021) - [j11]Ozan Firat Özgül
A Convolutional Deep Clustering Framework for Gene Expression Time Series. IEEE ACM Trans. Comput. Biol. Bioinform. 18(6): 2198-2207 (2021) - [c20]Riza Isik, Mehmet Tan:
Automated Molecule Generation using Deep Q-Learning and Graph Neural Networks. BIBM 2021: 2237-2244 - [c19]Batuhan Bardak, Mehmet Tan:
Using Clinical Drug Representations for Improving Mortality and Length of Stay Predictions. CIBCB 2021: 1-8 - [c18]Benan Bardak, Mehmet Tan:
DeepGREP: A deep convolutional neural network for predicting gene-regulating effects of small molecules. CIBCB 2021: 1-8 - [c17]Batuhan Bardak
Prediction of Mortality and Length of Stay with Deep Learning. SIU 2021: 1-4 - [i4]Batuhan Bardak, Mehmet Tan:
Using Clinical Drug Representations for Improving Mortality and Length of Stay Predictions. CoRR abs/2110.08918 (2021) - 2020
- [c16]Riza Isik, Isiksu Eksioglu, Bahattin Can Maral, Benan Bardak, Mehmet Tan:
Chemical Induced Differential Gene Expression Prediction on LINCS Database. BIBE 2020: 111-114 - [i3]Isiksu Eksioglu, Mehmet Tan:
Prediction of Drug Synergy by Ensemble Learning. CoRR abs/2001.01997 (2020) - [i2]Batuhan Bardak, Mehmet Tan:
Improving Clinical Outcome Predictions Using Convolution over Medical Entities with Multimodal Learning. CoRR abs/2011.12349 (2020)
2010 – 2019
- 2018
- [c15]Ozan Firat Özgül, Mehmet Ulas Çakir, Mehmet Tan
A Fully Unsupervised Framework for Scoring Driving Style. IEEE Conf. on Intelligent Systems 2018: 228-234 - [c14]Ozan Firat Özgül, Batuhan Bardak
Predicting drug activity by image encoded gene expression profiles. SIU 2018: 1-4 - [i1]Mehmet Tan, Ozan Firat Özgül, Batuhan Bardak, Isiksu Eksioglu, Suna Sabuncuoglu:
Drug response prediction by ensemble learning and drug-induced gene expression signatures. CoRR abs/1802.03800 (2018) - 2017
- [j10]Mehmet Tan
Edge distance graph kernel and its application to small molecule classification. Turkish J. Electr. Eng. Comput. Sci. 25: 2479-2490 (2017) - [c13]Batuhan Bardak
Disease outbreak prediction by data integration and multi-task learning. CIBCB 2017: 1-7 - 2016
- [j9]Utku Sirin
Effective gene expression data generation framework based on multi-model approach. Artif. Intell. Medicine 70: 41-61 (2016) - [j8]Mehmet Tan
Prediction of anti-cancer drug response by kernelized multi-task learning. Artif. Intell. Medicine 73: 70-77 (2016) - [c12]Ertan Tolan, Mehmet Tan
Anti-cancer Drug Activity Prediction by Ensemble Learning. KDIR 2016: 431-436 - 2015
- [c11]Batuhan Bardak
Prediction of influenza outbreaks by integrating Wikipedia article access logs and Google flu trend data. BIBE 2015: 1-6 - [c10]Yunus Gökçer, M. Fatih Demirci, Mehmet Tan:
A Graph-based Pattern Recognition for Chemical Molecule Matching. BIOINFORMATICS 2015: 158-162 - 2014
- [c9]Mehmet Tan
Drug sensitivity prediction for cancer cell lines based on pairwise kernels and miRNA profiles. BIBM 2014: 156-161 - [c8]Doruk Pancaroglu, Mehmet Tan
Improving Positive Unlabeled Learning Algorithms for Protein Interaction Prediction. PACBB 2014: 81-88 - 2013
- [j7]Utku Erdogdu, Mehmet Tan
Integrating machine learning techniques into robust data enrichment approach and its application to gene expression data. Int. J. Data Min. Bioinform. 8(3): 247-281 (2013) - [j6]Mehmet Tan
Subtree selection in kernels for graph classification. Int. J. Data Min. Bioinform. 8(3): 294-310 (2013) - 2012
- [j5]Cumhur Kiliç, Mehmet Tan
Positive unlabeled learning for deriving protein interaction networks. Netw. Model. Anal. Health Informatics Bioinform. 1(3): 87-102 (2012) - [j4]Mohammed Al-Shalalfa, Mehmet Tan
Revealing miRNA Regulation and miRNA Target Prediction Using Constraint-Based Learning. IEEE Trans. Syst. Man Cybern. Part C 42(6): 1354-1364 (2012) - [c7]Utku Sirin, Utku Erdogdu, Mehmet Tan
Effective Enrichment of Gene Expression Data Sets. ICMLA (1) 2012: 76-81 - 2011
- [j3]Mehmet Tan
Influence of Prior Knowledge in Constraint-Based Learning of Gene Regulatory Networks. IEEE ACM Trans. Comput. Biol. Bioinform. 8(1): 130-142 (2011) - [c6]Utku Erdogdu, Mehmet Tan
Employing Machine Learning Techniques for Data Enrichment: Increasing the Number of Samples for Effective Gene Expression Data Analysis. BIBM 2011: 238-242 - 2010
- [j2]Mehmet Tan
Scalable approach for effective control of gene regulatory networks. Artif. Intell. Medicine 48(1): 51-59 (2010) - [j1]Mehmet Tan
Automated Large-Scale Control of Gene Regulatory Networks. IEEE Trans. Syst. Man Cybern. Part B 40(2): 286-297 (2010) - [c5]Mehmet Tan
Feature selection for graph kernels. BIBM 2010: 632-637
2000 – 2009
- 2009
- [b1]Mehmet Tan:
Induction and control of large-scale gene regulatory networks ; Büyük-ölçekli gen düzenleyici ağların modellenmesi ve kontrolü. Middle East Technical University, Çankaya, Ankara, Turkey, 2009 - [c4]Mehmet Tan
Derivation of Transcriptional Regulatory Relationships by Partial Least Squares Regression. BIBM 2009: 88-91 - 2008
- [c3]Mehmet Tan
Large-scale approximate intervention strategies for Probabilistic Boolean Networks as models of gene regulation. BIBE 2008: 1-6 - [c2]Mehmet Tan
Combining multiple types of biological data in constraint-based learning of gene regulatory networks. CIBCB 2008: 90-97 - 2007
- [c1]Mehmet Tan
Feature Reduction for Gene Regulatory Network Control. BIBE 2007: 1260-1264
Coauthor Index
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