Essential gene prediction using limited gene essentiality information-An integrative semi-supervised machine learning strategy.

ABSTRACT: Essential gene prediction helps to find minimal genes indispensable for the survival of any organism. Machine learning (ML) algorithms have been useful for the prediction of gene essentiality. However, currently available ML pipelines perform poorly for organisms with limited experimental data. The objective is the development of a new ML pipeline to help in the annotation of essential genes of less explored disease-causing organisms for which minimal experimental data is available. The proposed strategy combines unsupervised feature selection technique, dimension reduction using the Kamada-Kawai algorithm, and semi-supervised ML algorithm employing Laplacian Support Vector Machine (LapSVM) for prediction of essential and non-essential genes from genome-scale metabolic networks using very

SUBMITTER: Nandi S 

PROVIDER: S-EPMC7703937 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature