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

In this paper a multi-domain multi-task algorithm for feature selection inbulk RNAseq data is proposed. Two datasets are investigated arising from mousehost immune response to Salmonella infection. Data is collected from severalstrains of collaborative cross mice. Samples from the spleen and liver serve asthe two domains. Several machine learning experiments are conducted and thesmall subset of discriminative across domains features have been extracted ineach case. The algorithm proves viable and underlines the benefits of acrossdomain feature selection by extracting new subset of discriminative featureswhich couldn't be extracted only by one-domain approach.