PONYTA: prioritization of phenotype-related genes from mouse KO events using PU learning on a biological network.

ABSTRACT:

Motivation

Transcriptome data from gene knock-out (KO) experiments in mice provide crucial insights into the intricate interactions between genotype and phenotype. Differentially expressed gene (DEG) analysis and network propagation (NP) are well-established methods for analysing transcriptome data. To determine genes related to phenotype changes from a KO experiment, we need to choose a cutoff value for the corresponding criterion based on the specific method. Using a rigorous cutoff value for DEG analysis and NP is likely to select mostly positive genes related to the phenotype, but many will be rejected as false negatives. On the other hand, using a loose cutoff value for either method is prone to include a number of genes that are not phenotype-related, which are false positive

SUBMITTER: Kim JH 

PROVIDER: S-EPMC11561041 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature