research-article

Prediction of anti-breast cancer compound activity based on Gradient Boosting Decision Tree ensemble learning

Authors:

Haoyun Yuan,

Yang Liu,

Jingze Du,

Ziqian ZhouAuthors Info & Claims

ISAIMS '21: Proceedings of the 2nd International Symposium on Artificial Intelligence for Medicine Sciences

Pages 509 - 513

https://doi.org/10.1145/3500931.3501018

Published: 22 December 2021 Publication History

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Abstract

The number of breast cancer cases worldwide has risen sharply in the past 20 years. In 2000, the total number of cases was 1.05 million. By 2018, the number had increased to 2.09 million, an increase of 99.05 percent, with an average annual growth rate of more than 5 percent. Breast cancer has gradually become the most common cancer for women, so the development of anti-breast cancer drugs has become a hot topic in the current medical field. In this paper, a series of compound descriptors and their biological activity data were collected for ERα, a target associated with breast cancer, and compound activity was predicted by establishing a compound activity prediction model. According to the compound descriptors, more important features were selected. The Quantitative structure-activity Relationship (QSAR) model of compounds was constructed, and then the GBDT algorithm was used to predict the model. Finally, through comparative analysis of results, the method was fast and accurate. It plays a key role in drug research.

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https://doi.org/10.1109/TCBB.2024.3425644

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Prediction of anti-breast cancer compound activity based on Gradient Boosting Decision Tree ensemble learning

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ISAIMS '21: Proceedings of the 2nd International Symposium on Artificial Intelligence for Medicine Sciences

October 2021

593 pages

ISBN:9781450395588

DOI:10.1145/3500931

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 22 December 2021

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ISAIMS 2021

ISAIMS 2021: 2nd International Symposium on Artificial Intelligence for Medicine Sciences

October 29 - 31, 2021

Beijing, China

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Overall Acceptance Rate 53 of 112 submissions, 47%

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Fu XDuan HZang XLiu CLi XZhang QZhang ZZou QCui F(2024)Hyb_SEnc: An Antituberculosis Peptide Predictor Based on a Hybrid Feature Vector and Stacked Ensemble LearningIEEE/ACM Transactions on Computational Biology and Bioinformatics10.1109/TCBB.2024.342564421:6(1897-1910)Online publication date: Nov-2024
https://doi.org/10.1109/TCBB.2024.3425644

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