Dealing with dimensionality: the application of machine learning to multi-omics data

Dylan Feldner-Busztin, Panos Firbas Nisantzis, Shelley Jane Edmunds, Gergely Boza, Fernando Racimo, Shyam Gopalakrishnan, Morten Tønsberg Limborg, Leo Lahti, Gonzalo G. de Polavieja

Abstract: Machine learning (ML) methods are motivated by the need to automate information extraction from large datasets in order to support human users in data-driven tasks. This is an attractive approach for integrative joint analysis of vast amounts of omics data produced in next generation sequencing and other -omics assays. A systematic assessment of the current literature can help to identify key trends and potential gaps in methodology and applications. We surveyed the literature on ML multi-omic data integration and quantitatively explored the goals, techniques and data involved in this field. We were particularly interested in examining how researchers use ML to deal with the volume and complexity of these datasets.