PathME: pathway based multi-modal sparse autoencoders for clustering of patient-level multi-omics data

Background Recent years have witnessed an increasing interest in multi-omics data, because these data allow for better understanding complex diseases such as cancer on a molecular system level. In addition, multi-omics data increase the chance to robustly identify molecular patient sub-groups and he...

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Veröffentlicht in:	BMC bioinformatics Jg. 21; H. 1; S. 146 - 20
Hauptverfasser:	Lemsara, Amina, Ouadfel, Salima, Fröhlich, Holger
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	London BioMed Central 16.04.2020 BioMed Central Ltd Springer Nature B.V BMC
Schlagworte:	Algorithms Analysis Bias Bioinformatics Biomedical and Life Sciences Cancer Clustering Computational Biology/Bioinformatics Computer Appl. in Life Sciences Deep learning Deoxyribonucleic acid DNA DNA methylation Gene expression Generalized linear models Genetic research Learning algorithms Life Sciences Machine learning Machine Learning and Artificial Intelligence in Bioinformatics Machine learning for computational and systems biology Medical treatment Methodology Methodology Article Methods Methylation Microarrays miRNA Multi-omics Mutation Noise reduction Patient clustering Patients Ribonucleic acid RNA Sensory integration Sparsity Subgroups Therapeutics Deep learning Multi-omics Patient clustering
ISSN:	1471-2105, 1471-2105
Online-Zugang:	Volltext
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