Predicting gene essentiality in <i>Caenorhabditis elegans</i> by feature engineering and machine-learning.

Predicting gene essentiality in Caenorhabditis elegans by feature engineering and machine-learning.

Campos, Tulio L; Korhonen, Pasi K; Sternberg, Paul W; Gasser, Robin B; Young, Neil D.

Afiliação

Campos TL; Department of Veterinary Biosciences, Melbourne Veterinary School, The University of Melbourne, Parkville, Victoria 3010, Australia.
Korhonen PK; Instituto Aggeu Magalhães, Fundação Oswaldo Cruz (IAM-Fiocruz), Recife, Pernambuco, Brazil.
Sternberg PW; Department of Veterinary Biosciences, Melbourne Veterinary School, The University of Melbourne, Parkville, Victoria 3010, Australia.
Gasser RB; Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, United States.
Young ND; Department of Veterinary Biosciences, Melbourne Veterinary School, The University of Melbourne, Parkville, Victoria 3010, Australia.

Comput Struct Biotechnol J ; 18: 1093-1102, 2020.

Article em En | MEDLINE | ID: mdl-32489524

Palavras-chave

CDS, coding sequence; CRISPR, Clustered Regularly Interspaced Short Palindromic Repeats; Caenorhabditis elegans; ES, Essentiality Score; EST, expressed sequence tag; Essential genes; Essentiality predictions; GBM, Gradient Boosting Method; GFF, general feature format; GLM, Generalised Linear Model; GO, gene ontology; ML, machine-learning; Machine-learning; NN, Artificial Neural Network; PPI, protein-protein interaction; PR-AUC, Area Under the Precision-Recall Curve; RF, Random Forest; RNAi, RNA interference; ROC-AUC, Area Under the Receiver Operating Characteristic Curve; SNP, single nucleotide polymorphism; SPLS, Sparse Partial Least Squares; SVM, Support-Vector Machine; TEA, Tissue Enrichment Analysis tool (WormBase); TSS, transcription start site; VCF, variant call file

Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2020 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2020 Tipo de documento: Article