MSPJ: Discovering potential biomarkers in small gene expression datasets via ensemble learning.

ABSTRACT: In transcriptomics, differentially expressed genes (DEGs) provide fine-grained phenotypic resolution for comparisons between groups and insights into molecular mechanisms underlying the pathogenesis of complex diseases or phenotypes. The robust detection of DEGs from large datasets is well-established. However, owing to various limitations (e.g., the low availability of samples for some diseases or limited research funding), small sample size is frequently used in experiments. Therefore, methods to screen reliable and stable features are urgently needed for analyses with limited sample size. In this study, MSPJ, a new machine learning approach for identifying DEGs was proposed to mitigate the reduced power and improve the stability of DEG identification in small gene expression datasets. T

SUBMITTER: Yin H 

PROVIDER: S-EPMC9304602 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature