Ternary classification models for predicting hormonal activities of chemicals via nuclear receptors
| Autor: | Huazhou Ying, Lu Yan, Feng Huang, Wen-Wen Nie, Chunqi Hu, Xiaowu Dong, Quan Zhang, Mei-Rong Zhao |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
0301 basic medicine
Cart Thyroid hormone receptor General Physics and Astronomy Computational biology Computational toxicology Biology Linear discriminant analysis Androgen receptor Support vector machine 03 medical and health sciences 030104 developmental biology Nuclear receptor Physical and Theoretical Chemistry Hormone |
| Zdroj: | Chemical Physics Letters. 706:360-366 |
| ISSN: | 0009-2614 |
| DOI: | 10.1016/j.cplett.2018.06.022 |
| Popis: | Endocrine disrupting chemicals (EDCs) can exhibit adverse effects by increasing or blocking hormonal activities as agonists or antagonists through nuclear receptors. Computational toxicology research provides a fast and automated screening tool for determining the potential effects of EDCs. Here, we collected a large dataset of known hormonal activities to develop ternary classification models of androgen receptor (AR) and thyroid hormone receptor (TR), in combination linear discriminant analysis (LDA), classification and regression trees (CART), and support vector machines (SVM). The optimum model for classifying AR and TR activities was SVM. These newly developed models constitute a rapidly systematic early-warning technical system for identifying different hormone activities. |
| Databáze: | OpenAIRE |
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