  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.
