Gradient Boosting Based Classification of Ion Channels

被引：3

作者：

Agrawal, Divyansh ^{[1
]}

Minocha, Sachin ^{[1
]}

Goel, Amit Kumar ^{[1
]}

机构：

[1] Galgotias Univ, Sch Comp Sci & Engn, Greater Noida, India

来源：

2021 IEEE INTERNATIONAL CONFERENCE ON COMPUTING, COMMUNICATION, AND INTELLIGENT SYSTEMS (ICCCIS) | 2021年

关键词：

Ion channels; classification; machine learning; feature engineering; proteins; biological science; KNN; gradient boosting; LightGBM; XGBoost; CatBoost;

D O I：

10.1109/ICCCIS51004.2021.9397161

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Target-based drugs are one of the growing research areas to identify potential drugs for a particular target body part. The number of ion channels open at a particular time could be used to determine the effect of the drug. In some cases it may also help to determine the effects of the drugs at different concentrations and dosages, helping in developing target-based drugs. Thus by classifying the number of open ion channels at a particular time, it is possible to quantify the drug effect and explain varying effects of a drug based on the ions which are passing through those open channels. This paper works on the classification of ion channels by using gradient boosting algorithms including Light Gradient Boosting, eXtreme Gradient Boosting, and Categorical Boosting. The analysis of gradient boosting algorithms and comparison with the KNN classifier by using various metrics shows the significance of the work.

引用

页码：102 / 107

页数：6

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