iOmicsPASS: network-based integration of multiomics data for predictive subnetwork discovery

• Published in: NPJ systems biology and applications Vol. 5; no. 1; pp. 22 - 10
• Main Authors: Koh, Hiromi W. L., Fermin, Damian, Vogel, Christine, Choi, Kwok Pui, Ewing, Rob M., Choi, Hyungwon
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 09.07.2019; Nature Publishing Group
• Subjects: 631/114; 631/553; Algorithms; Bioinformatics; Biomedical and Life Sciences; Breast cancer; Breast Neoplasms - genetics; Computational Biology - methods; Computational Biology/Bioinformatics; Computer Appl. in Life Sciences; Computer applications; Datasets; Gene Expression Profiling - methods; Gene Expression Regulation - genetics; Gene Regulatory Networks - genetics; Genomes; Genomics - methods; Humans; Integration; Life Sciences; Mathematical models; Models, Statistical; Models, Theoretical; Phenotypic variations; Protein Interaction Mapping - methods; Proteomics - methods; Software; Systems Biology; Technology Feature; Transcription; Systems biology; Computational biology and bioinformatics

Computational tools for multiomics data integration have usually been designed for unsupervised detection of multiomics features explaining large phenotypic variations. To achieve this, some approaches extract latent signals in heterogeneous data sets from a joint statistical error model, while othe...