A Multi-Domain Multi-Task Approach for Feature Selection from Bulk RNA Datasets

TL;DR

This paper introduces a multi-domain multi-task algorithm for feature selection in bulk RNA sequencing data, demonstrating its ability to identify discriminative features across different biological tissues and mouse strains, which single-domain methods cannot detect.

Contribution

The paper presents a novel multi-domain multi-task feature selection algorithm specifically designed for bulk RNAseq data, improving cross-domain feature detection over traditional single-domain approaches.

Findings

The algorithm successfully identifies cross-domain discriminative features.

Multi-domain approach uncovers features missed by single-domain methods.

Experiments on mouse immune response datasets validate the method's effectiveness.

Abstract

In this paper a multi-domain multi-task algorithm for feature selection in bulk RNAseq data is proposed. Two datasets are investigated arising from mouse host immune response to Salmonella infection. Data is collected from several strains of collaborative cross mice. Samples from the spleen and liver serve as the two domains. Several machine learning experiments are conducted and the small subset of discriminative across domains features have been extracted in each case. The algorithm proves viable and underlines the benefits of across domain feature selection by extracting new subset of discriminative features which couldn't be extracted only by one-domain approach.