about
Rocker: Open source, easy-to-use tool for AUC and enrichment calculations and ROC visualizationApplying DEKOIS 2.0 in structure-based virtual screening to probe the impact of preparation procedures and score normalizationOptimal assignment methods for ligand-based virtual screeningLarge scale study of multiple-molecule queriesA machine learning-based method to improve docking scoring functions and its application to drug repurposingRecommendations for evaluation of computational methodsMethods of protein structure comparison.A statistical framework to evaluate virtual screening.A CROC stronger than ROC: measuring, visualizing and optimizing early retrieval.An unbiased method to build benchmarking sets for ligand-based virtual screening and its application to GPCRsss-TEA: Entropy based identification of receptor specific ligand binding residues from a multiple sequence alignment of class A GPCRs.Do crystal structures obviate the need for theoretical models of GPCRs for structure-based virtual screening?Utility-aware screening with clique-oriented prioritization.The flexible pocketome engine for structural chemogenomics.Optimization and visualization of the edge weights in optimal assignment methods for virtual screening.Reconstructing coherent networks from electroencephalography and magnetoencephalography with reduced contamination from volume conduction or magnetic field spreadPathway-Dependent Effectiveness of Network Algorithms for Gene Prioritization.Pharmacophore-based discovery of a novel cytosolic phospholipase A(2)α inhibitor.Influence relevance voting: an accurate and interpretable virtual high throughput screening method.Validation of molecular docking programs for virtual screening against dihydropteroate synthase.Autocorrelation descriptor improvements for QSAR: 2DA_Sign and 3DA_SignCheminformatics meets molecular mechanics: a combined application of knowledge-based pose scoring and physical force field-based hit scoring functions improves the accuracy of structure-based virtual screeningEnhancing the rate of scaffold discovery with diversity-oriented prioritization.An economic framework to prioritize confirmatory tests after a high-throughput screen.Decoys Selection in Benchmarking Datasets: Overview and Perspectives.Caveats and pitfalls of ROC analysis in clinical microarray research (and how to avoid them)
P2860
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P2860
description
2008 nî lūn-bûn
@nan
2008年の論文
@ja
2008年学术文章
@wuu
2008年学术文章
@zh
2008年学术文章
@zh-cn
2008年学术文章
@zh-hans
2008年学术文章
@zh-my
2008年学术文章
@zh-sg
2008年學術文章
@yue
2008年學術文章
@zh-hant
name
Managing bias in ROC curves.
@en
Managing bias in ROC curves.
@nl
type
label
Managing bias in ROC curves.
@en
Managing bias in ROC curves.
@nl
prefLabel
Managing bias in ROC curves.
@en
Managing bias in ROC curves.
@nl
P1476
Managing bias in ROC curves.
@en
P2093
Daniel J Webster-Clark
P2888
P304
P356
10.1007/S10822-008-9181-Z
P577
2008-02-07T00:00:00Z