Deep learning for high-throuput data
||Deep learning for high-throuput data|
||SNIC Medium Storage|
||Mika Gustafsson <firstname.lastname@example.org>|
||2022-05-11 – 2023-06-01|
Background: Many common drugs work ineffectively for sub-groups of patients, as a result of the interplay between a multitude of small-effect genetic and epigenetic factors in complex diseases. New biotechnology methods, called omics, have made it possible to measure molecular imprints of a whole cell, which could be useful for development of more individualized therapies. Deep auto-encoders (DAEs), a type of artificial neural networks, are flexible non-linear dimension reduction methods, that recently have emerged as effective tools for summarizing high-dimensional complex genomics data.
Aim: To create a flexible multi-omic data integration tool that captures disease-specific structure across multiple levels of biological data to help identify processes related to disease outcome, severity, and response to treatment.
Methods: DAEs will be developed with latent spaces constrained by biological side information such as cellular pathways, that can be combined into Deep translational networks (DTNs). The DTNs are further constrained with biological information on the samples, e.g., disease subtypes or clinical outcome.
Significance: More advanced data-driven data-integrative methods will be essential in biology as multi-omics single-cell data sets are rapidly emerging. This project will illustrate how flexible integration of multiple data sources can lead to new insights into disease processes, which is the key to individualized treatment strategies.