Literature DB >> 27606011

Rocker: Open source, easy-to-use tool for AUC and enrichment calculations and ROC visualization.

Sakari Lätti1, Sanna Niinivehmas1, Olli T Pentikäinen1.   

Abstract

ABSTRACT: Receiver operating characteristics (ROC) curve with the calculation of area under curve (AUC) is a useful tool to evaluate the performance of biomedical and chemoinformatics data. For example, in virtual drug screening ROC curves are very often used to visualize the efficiency of the used application to separate active ligands from inactive molecules. Unfortunately, most of the available tools for ROC analysis are implemented into commercially available software packages, or are plugins in statistical software, which are not always the easiest to use. Here, we present Rocker, a simple ROC curve visualization tool that can be used for the generation of publication quality images. Rocker also includes an automatic calculation of the AUC for the ROC curve and Boltzmann-enhanced discrimination of ROC (BEDROC). Furthermore, in virtual screening campaigns it is often important to understand the early enrichment of active ligand identification, for this Rocker offers automated calculation routine. To enable further development of Rocker, it is freely available (MIT-GPL license) for use and modifications from our web-site (http://www.jyu.fi/rocker).

Entities:  

Year:  2016        PMID: 27606011      PMCID: PMC5013620          DOI: 10.1186/s13321-016-0158-y

Source DB:  PubMed          Journal:  J Cheminform        ISSN: 1758-2946            Impact factor:   5.514


  15 in total

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Authors:  Carsten Stephan; Sebastian Wesseling; Tania Schink; Klaus Jung
Journal:  Clin Chem       Date:  2003-03       Impact factor: 8.327

2.  Comparison of three methods for estimating the standard error of the area under the curve in ROC analysis of quantitative data.

Authors:  Karim O Hajian-Tilaki; James A Hanley
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3.  Ultrafast protein structure-based virtual screening with Panther.

Authors:  Sanna P Niinivehmas; Kari Salokas; Sakari Lätti; Hannu Raunio; Olli T Pentikäinen
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4.  ROCR: visualizing classifier performance in R.

Authors:  Tobias Sing; Oliver Sander; Niko Beerenwinkel; Thomas Lengauer
Journal:  Bioinformatics       Date:  2005-08-11       Impact factor: 6.937

5.  Managing bias in ROC curves.

Authors:  Robert D Clark; Daniel J Webster-Clark
Journal:  J Comput Aided Mol Des       Date:  2008-02-07       Impact factor: 3.686

6.  Evaluation and optimization of virtual screening workflows with DEKOIS 2.0--a public library of challenging docking benchmark sets.

Authors:  Matthias R Bauer; Tamer M Ibrahim; Simon M Vogel; Frank M Boeckler
Journal:  J Chem Inf Model       Date:  2013-06-12       Impact factor: 4.956

7.  DEKOIS: demanding evaluation kits for objective in silico screening--a versatile tool for benchmarking docking programs and scoring functions.

Authors:  Simon M Vogel; Matthias R Bauer; Frank M Boeckler
Journal:  J Chem Inf Model       Date:  2011-08-18       Impact factor: 4.956

8.  The meaning and use of the area under a receiver operating characteristic (ROC) curve.

Authors:  J A Hanley; B J McNeil
Journal:  Radiology       Date:  1982-04       Impact factor: 11.105

9.  Estimation and Comparison of Receiver Operating Characteristic Curves.

Authors:  Margaret Pepe; Gary Longton; Holly Janes
Journal:  Stata J       Date:  2009-03-01       Impact factor: 2.637

10.  Directory of useful decoys, enhanced (DUD-E): better ligands and decoys for better benchmarking.

Authors:  Michael M Mysinger; Michael Carchia; John J Irwin; Brian K Shoichet
Journal:  J Med Chem       Date:  2012-07-05       Impact factor: 7.446

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  26 in total

1.  Docking-based virtual screening of Brazilian natural compounds using the OOMT as the pharmacological target database.

Authors:  Ana Paula Carregal; Flávia V Maciel; Juliano B Carregal; Bianca Dos Reis Santos; Alisson Marques da Silva; Alex G Taranto
Journal:  J Mol Model       Date:  2017-03-11       Impact factor: 1.810

2.  Virtual screening to identify Leishmania braziliensis N-myristoyltransferase inhibitors: pharmacophore models, docking, and molecular dynamics.

Authors:  Juliana Cecília de Carvalho Gallo; Larissa de Mattos Oliveira; Janay Stefany Carneiro Araújo; Isis Bugia Santana; Manoelito Coelho Dos Santos Junior
Journal:  J Mol Model       Date:  2018-08-29       Impact factor: 1.810

3.  Virtual screening of natural products against 5-enolpyruvylshikimate-3-phosphate synthase using the Anagreen herbicide-like natural compound library.

Authors:  Maycon Vinicius Damasceno de Oliveira; Gilson Mateus Bittencourt Fernandes; Kauê S da Costa; Serhii Vakal; Anderson H Lima
Journal:  RSC Adv       Date:  2022-06-29       Impact factor: 4.036

4.  Novel Insights into the Predictors of Obstructive Sleep Apnea Syndrome in Patients with Chronic Coronary Syndrome: Development of a Predicting Model.

Authors:  Yanan Xu; Zongwei Ye; Benfang Wang; Long Tang; Jun Sun; Xuedong Chen; Yi Yang; Jun Wang
Journal:  Oxid Med Cell Longev       Date:  2022-06-27       Impact factor: 7.310

5.  Ensemble learning application to discover new trypanothione synthetase inhibitors.

Authors:  Juan I Alice; Carolina L Bellera; Diego Benítez; Marcelo A Comini; Pablo R Duchowicz; Alan Talevi
Journal:  Mol Divers       Date:  2021-07-15       Impact factor: 2.943

6.  Property-Unmatched Decoys in Docking Benchmarks.

Authors:  Reed M Stein; Ying Yang; Trent E Balius; Matt J O'Meara; Jiankun Lyu; Jennifer Young; Khanh Tang; Brian K Shoichet; John J Irwin
Journal:  J Chem Inf Model       Date:  2021-01-25       Impact factor: 4.956

7.  Combination of consensus and ensemble docking strategies for the discovery of human dihydroorotate dehydrogenase inhibitors.

Authors:  Garri Chilingaryan; Narek Abelyan; Arsen Sargsyan; Karen Nazaryan; Andre Serobian; Hovakim Zakaryan
Journal:  Sci Rep       Date:  2021-06-01       Impact factor: 4.379

8.  Decrypting Strong and Weak Single-Walled Carbon Nanotubes Interactions with Mitochondrial Voltage-Dependent Anion Channels Using Molecular Docking and Perturbation Theory.

Authors:  Michael González-Durruthy; Adriano V Werhli; Vinicius Seus; Karina S Machado; Alejandro Pazos; Cristian R Munteanu; Humberto González-Díaz; José M Monserrat
Journal:  Sci Rep       Date:  2017-10-16       Impact factor: 4.379

9.  Improving Docking Performance Using Negative Image-Based Rescoring.

Authors:  Sami T Kurkinen; Sanna Niinivehmas; Mira Ahinko; Sakari Lätti; Olli T Pentikäinen; Pekka A Postila
Journal:  Front Pharmacol       Date:  2018-03-26       Impact factor: 5.810

Review 10.  Structure-Based Virtual Screening: From Classical to Artificial Intelligence.

Authors:  Eduardo Habib Bechelane Maia; Letícia Cristina Assis; Tiago Alves de Oliveira; Alisson Marques da Silva; Alex Gutterres Taranto
Journal:  Front Chem       Date:  2020-04-28       Impact factor: 5.221

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