Frontiers | Tox21 Challenge to Build Predictive Models of Nuclear Receptor and Stress Response Pathways as Mediated by Exposure to Environmental Toxicants and Drugs

Tox21 Challenge to Build Predictive Models of Nuclear Receptor and Stress Response Pathways as Mediated by Exposure to Environmental Toxicants and Drugs

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EDITORIAL

Published on 25 Jan 2017

Editorial: Tox21 Challenge to Build Predictive Models of Nuclear Receptor and Stress Response Pathways As Mediated by Exposure to Environmental Toxicants and Drugs

Ruili Huang
Menghang Xia

doi 10.3389/fenvs.2017.00003

4,221 views
38 citations

ORIGINAL RESEARCH

Published on 15 Aug 2016

Identifying Biological Pathway Interrupting Toxins Using Multi-Tree Ensembles

Gergo Barta

doi 10.3389/fenvs.2016.00052

2,595 views
20 citations

ORIGINAL RESEARCH

Published on 08 Mar 2016

Predictive Modeling of Estrogen Receptor Binding Agents Using Advanced Cheminformatics Tools and Massive Public Data

Kathryn Ribay
Marlene T. Kim
Wenyi Wang
Daniel Pinolini
Hao Zhu

doi 10.3389/fenvs.2016.00012

3,957 views
40 citations

ORIGINAL RESEARCH

Published on 02 Mar 2016

Predictive Toxicology: Modeling Chemical Induced Toxicological Response Combining Circular Fingerprints with Random Forest and Support Vector Machine

Alexios Koutsoukas
Joseph St. Amand
Meenakshi Mishra
Jun Huan

doi 10.3389/fenvs.2016.00011

6,187 views
28 citations

ORIGINAL RESEARCH

Published on 15 Feb 2016

Rigorous Selection of Random Forest Models for Identifying Compounds that Activate Toxicity-Related Pathways

Yoshihiro Uesawa

doi 10.3389/fenvs.2016.00009

3,058 views
16 citations

ORIGINAL RESEARCH

Published on 04 Feb 2016

Consensus Modeling for HTS Assays Using In silico Descriptors Calculates the Best Balanced Accuracy in Tox21 Challenge

Ahmed Abdelaziz
Hilde Spahn-Langguth
Karl-Werner Schramm
Igor V. Tetko

doi 10.3389/fenvs.2016.00002

13,434 views
53 citations

ORIGINAL RESEARCH

Published on 04 Feb 2016

QSAR Modeling of Tox21 Challenge Stress Response and Nuclear Receptor Signaling Toxicity Assays

Stephen J. Capuzzi
Regina Politi
Olexandr Isayev
Sherif Farag
Alexander Tropsha

doi 10.3389/fenvs.2016.00003

7,443 views
69 citations

ORIGINAL RESEARCH

Published on 02 Feb 2016

DeepTox: Toxicity Prediction using Deep Learning

Andreas Mayr
Günter Klambauer
Thomas Unterthiner
Sepp Hochreiter

doi 10.3389/fenvs.2015.00080

76,019 views
756 citations

ORIGINAL RESEARCH

Published on 14 Jan 2016

Tox21Challenge to Build Predictive Models of Nuclear Receptor and Stress Response Pathways as Mediated by Exposure to Environmental Chemicals and Drugs

Ruili Huang
Menghang Xia
Dac-Trung Nguyen
Tongan Zhao
Srilatha Sakamuru
Jinghua Zhao
Sampada A. Shahane
Anna Rossoshek
Anton Simeonov

doi 10.3389/fenvs.2015.00085

8,078 views
136 citations

ORIGINAL RESEARCH

Published on 01 Dec 2015

Prediction of Compounds Activity in Nuclear Receptor Signaling and Stress Pathway Assays Using Machine Learning Algorithms and Low-Dimensional Molecular Descriptors

Filip Stefaniak

doi 10.3389/fenvs.2015.00077

3,313 views
32 citations

ORIGINAL RESEARCH

Published on 30 Jul 2015

Molecular similarity-based predictions of the Tox21 screening outcome

Malgorzata Natalia Drwal
Vishal Babu Siramshetty
Priyanka Banerjee
Andrean Goede
Robert Preissner
Mathias Dunkel

doi 10.3389/fenvs.2015.00054

6,387 views
32 citations

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Frontiers in Environmental Science

Environmental Informatics and Remote Sensing

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Articles

Editorial: Tox21 Challenge to Build Predictive Models of Nuclear Receptor and Stress Response Pathways As Mediated by Exposure to Environmental Toxicants and Drugs

Identifying Biological Pathway Interrupting Toxins Using Multi-Tree Ensembles

Predictive Modeling of Estrogen Receptor Binding Agents Using Advanced Cheminformatics Tools and Massive Public Data

Predictive Toxicology: Modeling Chemical Induced Toxicological Response Combining Circular Fingerprints with Random Forest and Support Vector Machine

Rigorous Selection of Random Forest Models for Identifying Compounds that Activate Toxicity-Related Pathways

Consensus Modeling for HTS Assays Using In silico Descriptors Calculates the Best Balanced Accuracy in Tox21 Challenge

QSAR Modeling of Tox21 Challenge Stress Response and Nuclear Receptor Signaling Toxicity Assays

DeepTox: Toxicity Prediction using Deep Learning

Tox21Challenge to Build Predictive Models of Nuclear Receptor and Stress Response Pathways as Mediated by Exposure to Environmental Chemicals and Drugs

Prediction of Compounds Activity in Nuclear Receptor Signaling and Stress Pathway Assays Using Machine Learning Algorithms and Low-Dimensional Molecular Descriptors

Molecular similarity-based predictions of the Tox21 screening outcome

Frontiers in Environmental Science

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