Essential gene prediction in Drosophila melanogaster using machine learning approaches based on sequence and functional features.

ABSTRACT: Genes are termed to be essential if their loss of function compromises viability or results in profound loss of fitness. On the genome scale, these genes can be determined experimentally employing RNAi or knockout screens, but this is very resource intensive. Computational methods for essential gene prediction can overcome this drawback, particularly when intrinsic (e.g. from the protein sequence) as well as extrinsic features (e.g. from transcription profiles) are considered. In this work, we employed machine learning to predict essential genes in Drosophila melanogaster. A total of 27,340 features were generated based on a large variety of different aspects comprising nucleotide and protein sequences, gene networks, protein-protein interactions, evolutionary conservation and funct

SUBMITTER: Aromolaran O 

PROVIDER: S-EPMC7096750 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature