Machine learning-based predictions of dietary restriction associations across ageing-related genes

被引:0
作者
Gustavo Daniel Vega Magdaleno
Vladislav Bespalov
Yalin Zheng
Alex A. Freitas
Joao Pedro de Magalhaes
机构
[1] University of Liverpool,Integrative Genomics of Ageing Group, Institute of Life Course and Medical Sciences
[2] ITMO University,School of Computer Technologies and Controls
[3] University of Liverpool,Department of Eye and Vision Science, Institute of Life Course and Medical Sciences
[4] University of Kent,School of Computing
来源
BMC Bioinformatics | / 23卷
关键词
Dietary restriction; Ageing; Machine learning;
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[21]  
Freitas AA(2016)Evolution of centrality measurements for the detection of essential proteins in biological networks Front Physiol 7 375-150
[22]  
Fabris F(2015)A novel approach to high-quality postmortem tissue procurement: the GTEx project Biopreserv Biobank 13 311-2830
[23]  
Palmer D(2015)Genefriends: a human rna-seq-based gene and transcript co-expression database Nucleic Acids Res 43 1124-5
[24]  
Salama KM(2019)Ensembldb: An R package to create and use ensembl-based annotation resources Bioinformatics 35 3151-241
[25]  
de Magalhaes JP(2015)protr/protrweb: R package and web server for generating various numerical representation schemes of protein sequences Bioinformatics 31 1857-377
[26]  
Freitas AA(2015)Peptides: a package for data mining of antimicrobial peptides R J 7 4-347
[27]  
Huang T(2014)Do we need hundreds of classifiers to solve real world classification problems? J Mach Learn Res 15 3133-71
[28]  
Zhang J(2017)An up-to-date comparison of state-of-the-art classification algorithms Expert Syst Appl 82 128-1277
[29]  
Xu Z-P(2018)Ensemble learning or deep learning? Application to default risk analysis J Risk Financ Manag 11 12-12024
[30]  
Weidner CI(2011)Scikit-learn: machine learning in Python J Mach Learn Res 12 2825-555