Integrate multi-omics data with biological interaction networks using Multi-view Factorization AutoEncoder (MAE)

Background Comprehensive molecular profiling of various cancers and other diseases has generated vast amounts of multi-omics data. Each type of -omics data corresponds to one feature space, such as gene expression, miRNA expression, DNA methylation, etc. Integrating multi-omics data can link differe...

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Vydáno v:	BMC genomics Ročník 20; číslo Suppl 11; s. 944 - 11
Hlavní autoři:	Ma, Tianle, Zhang, Aidong
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	London BioMed Central 20.12.2019 BioMed Central Ltd Springer Nature B.V BMC
Témata:	Algorithms Animal Genetics and Genomics Autoencoder Bias Biochemistry Biological interaction networks Biomedical and Life Sciences Biomedical data Cancer Clustering Criminal investigation Data analysis Data integration Data Mining Databases, Genetic Deep learning Deoxyribonucleic acid DNA DNA methylation Domains Factorization Gene expression Genes Genomics Humans Knowledge Knowledge Bases Learning algorithms Learning strategies Life Sciences Machine Learning Methylation Microarrays Microbial Genetics and Genomics MicroRNA Mining industry miRNA Models, Biological Molecular interactions Multi-omics data Multi-view learning Natural language processing Neoplasms - genetics Neoplasms - mortality Neoplasms - pathology Networks Patients Plant Genetics and Genomics Proteins Proteomics Regularization Representations Ribonucleic acid RNA Systems Biology - methods Training Deep learning Graph regularization Multi-omics data Autoencoder Data integration Biological interaction networks Multi-view learning
ISSN:	1471-2164, 1471-2164
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Popis
Shrnutí:	Background Comprehensive molecular profiling of various cancers and other diseases has generated vast amounts of multi-omics data. Each type of -omics data corresponds to one feature space, such as gene expression, miRNA expression, DNA methylation, etc. Integrating multi-omics data can link different layers of molecular feature spaces and is crucial to elucidate molecular pathways underlying various diseases. Machine learning approaches to mining multi-omics data hold great promises in uncovering intricate relationships among molecular features. However, due to the “big p, small n” problem (i.e., small sample sizes with high-dimensional features), training a large-scale generalizable deep learning model with multi-omics data alone is very challenging. Results We developed a method called Multi-view Factorization AutoEncoder (MAE) with network constraints that can seamlessly integrate multi-omics data and domain knowledge such as molecular interaction networks. Our method learns feature and patient embeddings simultaneously with deep representation learning. Both feature representations and patient representations are subject to certain constraints specified as regularization terms in the training objective. By incorporating domain knowledge into the training objective, we implicitly introduced a good inductive bias into the machine learning model, which helps improve model generalizability. We performed extensive experiments on the TCGA datasets and demonstrated the power of integrating multi-omics data and biological interaction networks using our proposed method for predicting target clinical variables. Conclusions To alleviate the overfitting problem in deep learning on multi-omics data with the “big p, small n” problem, it is helpful to incorporate biological domain knowledge into the model as inductive biases. It is very promising to design machine learning models that facilitate the seamless integration of large-scale multi-omics data and biomedical domain knowledge for uncovering intricate relationships among molecular features and clinical features.
Bibliografie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
ISSN:	1471-2164 1471-2164
DOI:	10.1186/s12864-019-6285-x