The influence of the inactives subset generation on the performance of machine learning methods
about
Target prediction utilising negative bioactivity data covering large chemical spacePractical application of the Average Information Content Maximization (AIC-MAX) algorithm: selection of the most important structural features for serotonin receptor ligands.The influence of negative training set size on machine learning-based virtual screening.An Algorithm to Identify Target-Selective Ligands - A Case Study of 5-HT7/5-HT1A Receptor Selectivity.Use of machine learning approaches for novel drug discovery.A systematic approach to prioritize drug targets using machine learning, a molecular descriptor-based classification model, and high-throughput screening of plant derived molecules: a case study in oral cancer.The influence of the negative-positive ratio and screening database size on the performance of machine learning-based virtual screening.Influence of Varying Training Set Composition and Size on Support Vector Machine-Based Prediction of Active Compounds.Unearthing new genomic markers of drug response by improved measurement of discriminative power.How to Achieve Better Results Using PASS-Based Virtual Screening: Case Study for Kinase Inhibitors.
P2860
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P2860
The influence of the inactives subset generation on the performance of machine learning methods
description
2013 nî lūn-bûn
@nan
2013 թուականի Ապրիլին հրատարակուած գիտական յօդուած
@hyw
2013 թվականի ապրիլին հրատարակված գիտական հոդված
@hy
2013年の論文
@ja
2013年論文
@yue
2013年論文
@zh-hant
2013年論文
@zh-hk
2013年論文
@zh-mo
2013年論文
@zh-tw
2013年论文
@wuu
name
The influence of the inactives ...... ce of machine learning methods
@ast
The influence of the inactives ...... ce of machine learning methods
@en
The influence of the inactives ...... ce of machine learning methods
@nl
type
label
The influence of the inactives ...... ce of machine learning methods
@ast
The influence of the inactives ...... ce of machine learning methods
@en
The influence of the inactives ...... ce of machine learning methods
@nl
prefLabel
The influence of the inactives ...... ce of machine learning methods
@ast
The influence of the inactives ...... ce of machine learning methods
@en
The influence of the inactives ...... ce of machine learning methods
@nl
P2860
P50
P356
P1476
The influence of the inactives ...... ce of machine learning methods
@en
P2860
P2888
P356
10.1186/1758-2946-5-17
P577
2013-04-05T00:00:00Z
P5875
P6179
1024250561