Inferring pseudogene-MiRNA associations based on an ensemble learning framework with similarity kernel fusion.

ABSTRACT: Accumulating evidence shows that pseudogenes can function as microRNAs (miRNAs) sponges and regulate gene expression. Mining potential interactions between pseudogenes and miRNAs will facilitate the clinical diagnosis and treatment of complex diseases. However, identifying their interactions through biological experiments is time-consuming and labor intensive. In this study, an ensemble learning framework with similarity kernel fusion is proposed to predict pseudogene-miRNA associations, named ELPMA. First, four pseudogene similarity profiles and five miRNA similarity profiles are measured based on the biological and topology properties. Subsequently, similarity kernel fusion method is used to integrate the similarity profiles. Then, the feature representation for pseudogenes and miRNAs is

SUBMITTER: Fan C 

PROVIDER: S-EPMC10232424 | biostudies-literature | 2023 May

REPOSITORIES: biostudies-literature