Improved prediction of gene expression through integrating cell signalling models with machine learning.

ABSTRACT:

Background

A key problem in bioinformatics is that of predicting gene expression levels. There are two broad approaches: use of mechanistic models that aim to directly simulate the underlying biology, and use of machine learning (ML) to empirically predict expression levels from descriptors of the experiments. There are advantages and disadvantages to both approaches: mechanistic models more directly reflect the underlying biological causation, but do not directly utilize the available empirical data; while ML methods do not fully utilize existing biological knowledge.

Results

Here, we investigate overcoming these disadvantages by integrating mechanistic cell signalling models with ML. Our approach to integration is to augment ML with similarity features (attributes) compute

SUBMITTER: Al Taweraqi N 

PROVIDER: S-EPMC9356471 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature