Literature DB >> 23486446

The liver toxicity knowledge base: a systems approach to a complex end point.

M Chen1, J Zhang, Y Wang, Z Liu, R Kelly, G Zhou, H Fang, J Borlak, W Tong.   

Abstract

Drug-induced liver injury (DILI) is a major concern in public health management, drug development, and regulatory implementation. The Liver Toxicity Knowledge Base (LTKB) was developed with the specific aim of enhancing our understanding of DILI. It seeks to achieve improvement in DILI prediction through integrated analysis of diverse sources of drug-elicited data. The project has also produced a centralized resource of data as well as predictive models that will be useful for research and drug regulation.

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Year:  2013        PMID: 23486446     DOI: 10.1038/clpt.2013.16

Source DB:  PubMed          Journal:  Clin Pharmacol Ther        ISSN: 0009-9236            Impact factor:   6.875


  13 in total

1.  A testing strategy to predict risk for drug-induced liver injury in humans using high-content screen assays and the 'rule-of-two' model.

Authors:  Minjun Chen; Chun-Wei Tung; Qiang Shi; Lei Guo; Leming Shi; Hong Fang; Jürgen Borlak; Weida Tong
Journal:  Arch Toxicol       Date:  2014-06-11       Impact factor: 5.153

2.  Integrating Drug's Mode of Action into Quantitative Structure-Activity Relationships for Improved Prediction of Drug-Induced Liver Injury.

Authors:  Leihong Wu; Zhichao Liu; Scott Auerbach; Ruili Huang; Minjun Chen; Kristin McEuen; Joshua Xu; Hong Fang; Weida Tong
Journal:  J Chem Inf Model       Date:  2017-04-10       Impact factor: 4.956

3.  Machine Learning Models for Predicting Liver Toxicity.

Authors:  Jie Liu; Wenjing Guo; Sugunadevi Sakkiah; Zuowei Ji; Gokhan Yavas; Wen Zou; Minjun Chen; Weida Tong; Tucker A Patterson; Huixiao Hong
Journal:  Methods Mol Biol       Date:  2022

4.  An Algorithm Framework for Drug-Induced Liver Injury Prediction Based on Genetic Algorithm and Ensemble Learning.

Authors:  Bowei Yan; Xiaona Ye; Jing Wang; Junshan Han; Lianlian Wu; Song He; Kunhong Liu; Xiaochen Bo
Journal:  Molecules       Date:  2022-05-12       Impact factor: 4.927

Review 5.  Advances in Engineered Liver Models for Investigating Drug-Induced Liver Injury.

Authors:  Christine Lin; Salman R Khetani
Journal:  Biomed Res Int       Date:  2016-09-20       Impact factor: 3.411

Review 6.  Drug-induced liver injury: Do we know everything?

Authors:  Tamara Alempijevic; Simon Zec; Tomica Milosavljevic
Journal:  World J Hepatol       Date:  2017-04-08

7.  Modeling of xenobiotic transport and metabolism in virtual hepatic lobule models.

Authors:  Xiao Fu; James P Sluka; Sherry G Clendenon; Kenneth W Dunn; Zemin Wang; James E Klaunig; James A Glazier
Journal:  PLoS One       Date:  2018-09-13       Impact factor: 3.240

8.  A systems approach for analysis of high content screening assay data with topic modeling.

Authors:  Halil Bisgin; Minjun Chen; Yuping Wang; Reagan Kelly; Hong Fang; Xiaowei Xu; Weida Tong
Journal:  BMC Bioinformatics       Date:  2013-10-09       Impact factor: 3.169

9.  A transcriptomics data-driven gene space accurately predicts liver cytopathology and drug-induced liver injury.

Authors:  Pekka Kohonen; Juuso A Parkkinen; Egon L Willighagen; Rebecca Ceder; Krister Wennerberg; Samuel Kaski; Roland C Grafström
Journal:  Nat Commun       Date:  2017-07-03       Impact factor: 14.919

10.  The Development of a Database for Herbal and Dietary Supplement Induced Liver Toxicity.

Authors:  Jieqiang Zhu; Ji-Eun Seo; Sanlong Wang; Kristin Ashby; Rodney Ballard; Dianke Yu; Baitang Ning; Rajiv Agarwal; Jürgen Borlak; Weida Tong; Minjun Chen
Journal:  Int J Mol Sci       Date:  2018-09-28       Impact factor: 5.923

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