FRP-XGBoost: Identification of ferroptosis-related proteins based on multi-view features

作者全名："Lin, Li; Long, Yao; Liu, Jinkai; Deng, Dongliang; Yuan, Yu; Liu, Lubin; Tan, Bin; Qi, Hongbo"

作者地址："[Lin, Li; Yuan, Yu; Liu, Lubin; Qi, Hongbo] Chongqing Med Univ, Dept Obstet & Gynecol, Women & Childrens Hosp, Chongqing 401147, Peoples R China; [Lin, Li; Yuan, Yu; Liu, Lubin; Qi, Hongbo] Chongqing Hlth Ctr Women & Children, Dept Obstet & Gynecol, Chongqing 401147, Peoples R China; [Long, Yao; Liu, Jinkai; Tan, Bin; Qi, Hongbo] Chongqing Med Univ, Chongqing Key Lab Maternal & Fetal Med, Chongqing 400016, Peoples R China; [Long, Yao; Liu, Jinkai; Tan, Bin; Qi, Hongbo] Chongqing Med Univ, Joint Int Res Lab Reprod & Dev, Chinese Minist Educ, Chongqing 400016, Peoples R China; [Long, Yao; Liu, Jinkai; Tan, Bin] Chongqing Med Univ, Dept Obstet, Affiliated Hosp 1, Chongqing 400016, Peoples R China; [Deng, Dongliang] Chongqing Tradit Chinese Med Hosp, Dept Oncol, Chongqing 400021, Peoples R China; [Tan, Bin] Chongqing Med Univ, Affiliated Hosp 1, 1 Youyi Rd, Chongqing 400016, Peoples R China; [Qi, Hongbo] Chongqing Hlth Ctr Women & Children, 120 Longshan Rd, Chongqing 401147, Peoples R China"

通信作者："Tan, B (通讯作者)，Chongqing Med Univ, Affiliated Hosp 1, 1 Youyi Rd, Chongqing 400016, Peoples R China.; Qi, HB (通讯作者)，Chongqing Hlth Ctr Women & Children, 120 Longshan Rd, Chongqing 401147, Peoples R China."

来源：INTERNATIONAL JOURNAL OF BIOLOGICAL MACROMOLECULES

ESI学科分类：BIOLOGY & BIOCHEMISTRY

WOS号：WOS:001187991400001

JCR分区：Q1

影响因子：8.2

年份：2024

卷号：262

期号：　

开始页：　

结束页：　

文献类型：Article

关键词：Ferroptosis-related proteins; Pan -cancer bioinformatics analysis; Machine learning; Multi-view features; Feature selection

摘要："Ferroptosis represents a novel form of programmed cell death. Pan -cancer bioinformatics analysis indicates that identifying and modulating ferroptosis offer innovative approaches for preventing and treating diverse tumor pathologies. However, the precise detection of ferroptosis-related proteins via conventional wet-laboratory techniques remains a formidable challenge, largely due to the constraints of existing methodologies. These traditional approaches are not only labor-intensive but also financially burdensome. Consequently, there is an imperative need for the development of more sophisticated and efficient computational tools to facilitate the detection of these proteins. In this paper, we presented a XGBoost and multi-view features-based machine learning prediction method for predicting ferroptosis-related proteins, which was referred to as FRP-XGBoost. In this study, we explored four types of protein feature extraction methods and evaluated their effectiveness in predicting ferroptosis-related proteins using six of the most commonly used traditional classifiers. To enhance the representational power of the hybrid features, we employed a two-step feature selection technique to identify the optimal subset of features. Subsequently, we constructed a prediction model using the XGBoost algorithm. The FRP-XGBoost achieved an accuracy of 96.74 % in 10-fold cross -validation and a further accuracy of 91.52 % in an independent test. The implementation source code of FRP-XGBoost is available at https://github.com/linli 5417/FRP-XGBoost."

基金机构："National Natural Science Foundation of China [82001573, U21A20346]"

基金资助正文：The work was supported by the National Natural Science Foundation of China for Youth (No. 82001573) and the Joint Funds of the National Natural Science Foundation of China (No. U21A20346).