Quantitative structure-activity relationship model for classifying the diverse series of antifungal agents using ratio weighted penalized logistic regression
One of the most challenging issues when facing a Quantitative structure-activity relationship (QSAR) classification model is to deal with the descriptor selection. Penalized methods have been adapted and have gained popularity as a key for simultaneously performing descriptor selection and QSAR clas...
保存先:
主要な著者: | Alharthi, A. M., Lee, M. H., Algamal, Z. Y., Al-Fakih, A. M. |
---|---|
フォーマット: | 論文 |
出版事項: |
Taylor and Francis Ltd.
2020
|
主題: | |
オンライン・アクセス: | http://eprints.utm.my/id/eprint/93839/ https://doi.org/10.1080/1062936X.2020.1782467 |
タグ: |
タグ追加
タグなし, このレコードへの初めてのタグを付けませんか!
|
類似資料
-
Improving penalized logistic regression model with missing values in high-dimensional data
著者:: Alharthi, Aiedh Mrisi, 等
出版事項: (2022) -
A novel molecular descriptor selection method in QSAR classification model based on weighted penalized logistic regression
著者:: Algamal, Z. Y., 等
出版事項: (2017) -
Penalized logistic regression with the adaptive LASSO for gene selection in high-dimensional cancer classification
著者:: Algamal, Zakariya Yahya, 等
出版事項: (2015) -
QSAR classification model for diverse series of antifungal agents based on binary coyote optimization algorithm
著者:: Mohammed Al-Fakih, Abdo, 等
出版事項: (2023) -
High-dimensional QSAR modelling using penalized linear regression model with L1/2-norm
著者:: Algamal, Z. Y., 等
出版事項: (2016)