Nothing Special   »   [go: up one dir, main page]

Kovacheva et al., 2014 - Google Patents

DiSWOP: a novel measure for cell-level protein network analysis in localized proteomics image data

Kovacheva et al., 2014

View HTML
Document ID
9671435976807276402
Author
Kovacheva V
Khan A
Khan M
Epstein D
Rajpoot N
Publication year
Publication venue
Bioinformatics

External Links

Snippet

Motivation: New bioimaging techniques have recently been proposed to visualize the colocation or interaction of several proteins within individual cells, displaying the heterogeneity of neighbouring cells within the same tissue specimen. Such techniques …
Continue reading at academic.oup.com (HTML) (other versions)

Classifications

    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F19/00Digital computing or data processing equipment or methods, specially adapted for specific applications
    • G06F19/10Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology
    • G06F19/28Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology for programming tools or database systems, e.g. ontologies, heterogeneous data integration, data warehousing or computing architectures
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F19/00Digital computing or data processing equipment or methods, specially adapted for specific applications
    • G06F19/10Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology
    • G06F19/24Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology for machine learning, data mining or biostatistics, e.g. pattern finding, knowledge discovery, rule extraction, correlation, clustering or classification
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F19/00Digital computing or data processing equipment or methods, specially adapted for specific applications
    • G06F19/10Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology
    • G06F19/12Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology for modelling or simulation in systems biology, e.g. probabilistic or dynamic models, gene-regulatory networks, protein interaction networks or metabolic networks
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F19/00Digital computing or data processing equipment or methods, specially adapted for specific applications
    • G06F19/10Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology
    • G06F19/18Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology for functional genomics or proteomics, e.g. genotype-phenotype associations, linkage disequilibrium, population genetics, binding site identification, mutagenesis, genotyping or genome annotation, protein-protein interactions or protein-nucleic acid interactions
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F19/00Digital computing or data processing equipment or methods, specially adapted for specific applications
    • G06F19/10Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology
    • G06F19/20Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology for hybridisation or gene expression, e.g. microarrays, sequencing by hybridisation, normalisation, profiling, noise correction models, expression ratio estimation, probe design or probe optimisation
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01NINVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00Investigating or analysing materials by specific methods not covered by the preceding groups
    • G01N33/48Investigating or analysing materials by specific methods not covered by the preceding groups biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/5005Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving human or animal cells
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/20Handling natural language data
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/30Information retrieval; Database structures therefor; File system structures therefor
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/00127Acquiring and recognising microscopic objects, e.g. biological cells and cellular parts
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00Image analysis
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00Indexing scheme for image analysis or image enhancement
    • G06T2207/30Subject of image; Context of image processing
    • G06T2207/30004Biomedical image processing

Similar Documents

Publication Publication Date Title
Rahman et al. Alternative preprocessing of RNA-Sequencing data in The Cancer Genome Atlas leads to improved analysis results
Jiang et al. Big data in basic and translational cancer research
Cheerla et al. Deep learning with multimodal representation for pancancer prognosis prediction
Calderaro et al. Artificial intelligence-based pathology for gastrointestinal and hepatobiliary cancers
Behrmann et al. Deep learning for tumor classification in imaging mass spectrometry
Zhang et al. Enhancing Hi-C data resolution with deep convolutional neural network HiCPlus
Filipp Opportunities for artificial intelligence in advancing precision medicine
Abdelmoula et al. Data-driven identification of prognostic tumor subpopulations using spatially mapped t-SNE of mass spectrometry imaging data
Hebestreit et al. Detection of significantly differentially methylated regions in targeted bisulfite sequencing data
Sun et al. Penalized logistic regression for high-dimensional DNA methylation data with case-control studies
Xu et al. An image-based multi-label human protein subcellular localization predictor (i locator) reveals protein mislocalizations in cancer tissues
Poulet et al. NucleusJ: an ImageJ plugin for quantifying 3D images of interphase nuclei
Chen et al. Automated image analysis of protein localization in budding yeast
Rahimi et al. Discriminating early-and late-stage cancers using multiple kernel learning on gene sets
Harder et al. Tissue Phenomics for prognostic biomarker discovery in low-and intermediate-risk prostate cancer
Ju et al. Development of a robust classifier for quality control of reverse-phase protein arrays
Huang et al. Predicting colorectal cancer tumor mutational burden from histopathological images and clinical information using multi-modal deep learning
Wu et al. meGPS: a multi-omics signature for hepatocellular carcinoma detection integrating methylome and transcriptome data
Feher et al. Cell population identification using fluorescence-minus-one controls with a one-class classifying algorithm
Janssens et al. Fully unsupervised deep mode of action learning for phenotyping high-content cellular images
Xu et al. Learning complex subcellular distribution patterns of proteins via analysis of immunohistochemistry images
Warchal et al. Evaluation of machine learning classifiers to predict compound mechanism of action when transferred across distinct cell lines
Tran et al. Omics-based deep learning approaches for lung cancer decision-making and therapeutics development
Hong et al. ConDo: protein domain boundary prediction using coevolutionary information
Ren et al. Recurrence analysis on prostate cancer patients with Gleason score 7 using integrated histopathology whole-slide images and genomic data through deep neural networks