Oncotarget

Research Papers:

Development of estrogen receptor beta binding prediction model using large sets of chemicals

Sugunadevi Sakkiah, Chandrabose Selvaraj, Ping Gong, Chaoyang Zhang, Weida Tong and Huixiao Hong _

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Oncotarget. 2017; 8:92989-93000. https://doi.org/10.18632/oncotarget.21723

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Abstract

Sugunadevi Sakkiah1, Chandrabose Selvaraj1, Ping Gong2, Chaoyang Zhang3, Weida Tong1 and Huixiao Hong1

1Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR, USA

2Environmental Laboratory, U.S. Army Engineer Research and Development Center, Vicksburg, MS, USA

3School of Computer Science, University of Southern Mississippi, Hattiesburg, MS, USA

Correspondence to:

Huixiao Hong, email: [email protected]

Keywords: decision forest, estrogen receptor, QSAR, Mold2, predictive model

Received: July 20, 2017    Accepted: August 27, 2017    Published: October 10, 2017

ABSTRACT

We developed an ERβ binding prediction model to facilitate identification of chemicals specifically bind ERβ or ERα together with our previously developed ERα binding model. Decision Forest was used to train ERβ binding prediction model based on a large set of compounds obtained from EADB. Model performance was estimated through 1000 iterations of 5-fold cross validations. Prediction confidence was analyzed using predictions from the cross validations. Informative chemical features for ERβ binding were identified through analysis of the frequency data of chemical descriptors used in the models in the 5-fold cross validations. 1000 permutations were conducted to assess the chance correlation. The average accuracy of 5-fold cross validations was 93.14% with a standard deviation of 0.64%. Prediction confidence analysis indicated that the higher the prediction confidence the more accurate the predictions. Permutation testing results revealed that the prediction model is unlikely generated by chance. Eighteen informative descriptors were identified to be important to ERβ binding prediction. Application of the prediction model to the data from ToxCast project yielded very high sensitivity of 90-92%. Our results demonstrated ERβ binding of chemicals could be accurately predicted using the developed model. Coupling with our previously developed ERα prediction model, this model could be expected to facilitate drug development through identification of chemicals that specifically bind ERβ or ERα.


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