Quantitative Structure-Activity Relationships of Estrogen Receptor Alpha Based on Molecular Descriptors Selection and Extreme Gradient Boosting-Reference-Cited by-同舟云学术

Quantitative Structure-Activity Relationships of Estrogen Receptor Alpha Based on Molecular Descriptors Selection and Extreme Gradient Boosting

Published:2023-01-05 Issue: Volume: Page:
ISSN:
Container-title:2023 The 7th International Conference on Machine Learning and Soft Computing (ICMLSC)
language:
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Author:

Liu Shaotong¹^ORCID,Xu Zhewei¹^ORCID,Ye Dongsheng¹^ORCID

Affiliation:

1. Beijing Institute of Computer Technology and Applications, China

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3583788.3583807

Reference15 articles.

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2. Leo Breiman . 2001. Random forests. Machine learning 45, 1 ( 2001 ), 5–32. Leo Breiman. 2001. Random forests. Machine learning 45, 1 (2001), 5–32.

3. XGBoost

4. SimBoost: a read-across approach for predicting drug–target binding affinities using gradient boosting machines

5. Min Jia Karin Dahlman-Wright and Jan-Åke Gustafsson. 2015. Estrogen receptor alpha and beta in health and disease. Best practice & research Clinical endocrinology & metabolism 29 4(2015) 557–568. Min Jia Karin Dahlman-Wright and Jan-Åke Gustafsson. 2015. Estrogen receptor alpha and beta in health and disease. Best practice & research Clinical endocrinology & metabolism 29 4(2015) 557–568.