Abstract
In recent years, high-throughput sequencing technologies provide unprecedented opportunity to depict cancer samples at multiple molecular levels. The integration and analysis of these multi-omics datasets is a crucial and critical step to gain actionable knowledge in a precision medicine framework. This paper explores recent data-driven methodologies that have been developed and applied to respond major challenges of stratified medicine in oncology, including patients' phenotyping, biomarker discovery, and drug repurposing. We systematically retrieved peer-reviewed journals published from 2014 to 2019, select and thoroughly describe the tools presenting the most promising innovations regarding the integration of heterogeneous data, the machine learning methodologies that successfully tackled the complexity of multi-omics data, and the frameworks to deliver actionable results for clinical practice. The review is organized according to the applied methods: Deep learning, Network-based methods, Clustering, Features Extraction, and Transformation, Factorization. We provide an overview of the tools available in each methodological group and underline the relationship among the different categories. Our analysis revealed how multi-omics datasets could be exploited to drive precision oncology, but also current limitations in the development of multi-omics data integration.
Highlights
The integration and analysis of high-throughput molecular assays is a major focus for precision medicine in enabling the understanding of patient and disease specific variations
While complexities underling cancer still hampers our understanding of how this disease arises and progresses [8], multi-omics approaches have been suggested as promising tools to dissect patient’s dysfunctions in multiple biological systems that may be altered by cancer mechanisms [9]
Precision oncology would greatly benefit from actionable knowledge gained from multi-omics assays
Summary
The integration and analysis of high-throughput molecular assays is a major focus for precision medicine in enabling the understanding of patient and disease specific variations. Computational multi-omics approaches are based on machine learning techniques and typically aim at classifying patients into cancer subtypes [1,2,3,4,5], designed for biomarker discovery and drug repurposing [6, 7]. While complexities underling cancer still hampers our understanding of how this disease arises and progresses [8], multi-omics approaches have been suggested as promising tools to dissect patient’s dysfunctions in multiple biological systems that may be altered by cancer mechanisms [9]. Several efforts have been made to generate comprehensive multi-omics profiles of cancer patients. Analysis of datasets generated by multi-omics sequencing requires the development of computational approaches spanning from data integration [10], statistical methods, and artificial intelligence systems to gain actionable knowledge from data
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