换一批Predicting gene essentiality in <i>Caenorhabditis elegans</i> by feature engineering and machine-learning.
第一作者：
Tulio L,Campos
第一单位：
Department of Veterinary Biosciences, Melbourne Veterinary School, The University of Melbourne, Parkville, Victoria 3010, Australia.;Instituto Aggeu Magalhães, Fundação Oswaldo Cruz (IAM-Fiocruz), Recife, Pernambuco, Brazil.
作者：
关键词
CDS, coding sequenceCRISPR, Clustered Regularly Interspaced Short Palindromic RepeatsCaenorhabditis elegansES, Essentiality ScoreEST, expressed sequence tagEssential genesEssentiality predictionsGBM, Gradient Boosting MethodGFF, general feature formatGLM, Generalised Linear ModelGO, gene ontologyML, machine-learningMachine-learningNN, Artificial Neural NetworkPPI, protein-protein interactionPR-AUC, Area Under the Precision-Recall CurveRF, Random ForestRNAi, RNA interferenceROC-AUC, Area Under the Receiver Operating Characteristic CurveSNP, single nucleotide polymorphismSPLS, Sparse Partial Least SquaresSVM, Support-Vector MachineTEA, Tissue Enrichment Analysis tool (WormBase)TSS, transcription start siteVCF, variant call file
DOI
10.1016/j.csbj.2020.05.008
PMID
32489524
发布时间
2020-09-28
基金项目
U24 HG002223/HG/NHGRI NIH HHS/United States
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