More Is Better: Recent Progress in Multi-Omics Data Integration Methods
By Sijia Huang, Kumardeep Chaudhary, Lana X. Garmire. Frontiers in Genetics, 2017
616 citations recorded by the source corpus.
Multi-omics data integration is one of the major challenges in the era of precision medicine. Considerable work has been done with the advent of high-throughput studies, which have enabled the data access for downstream analyses. To improve the clinical outcome prediction, a gamut of software tools has been developed. This review outlines the progress done in the field of multi-omics integration and comprehensive tools developed so far in this field. Further, we discuss the integration methods to predict patient survival at the end of the review.
Subfields: Molecular Biology
View Paper: https://www.semanticscholar.org/paper/68d61d5673a1ecbe3e40a49797a9bdfd0b418fcd
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In Molecular Biology
- Olivier Périn
- Marie-Pier Scott-Boyer
- Kumardeep Chaudhary
- Milan Picard
- Arnaud Droit
- Lana X. Garmire
- Antoine Bodein
Citations in the corpus
- Integration strategies of multi-omics data for machine learning analysis (2021, methodology): “Moreover, some machine learning methods can directly handle missing values like Random Forest 17 or K-Nearest Neighbor 18 or more recent meth-ods [19,20].”
- Deep Learning based multi-omics integration robustly predicts survival in liver cancer (2017, background): “However, most of the studies explored the molecular subtypes without relying on survival during the process of defining subtypes (21).”