FRP-XGBoost : Identification of ferroptosis-related proteins based on multi-view features
Copyright © 2024 Elsevier B.V. All rights reserved..
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/linli5417/FRP-XGBoost.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:262 |
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| Enthalten in: |
International journal of biological macromolecules - 262(2024), Pt 2 vom: 26. März, Seite 130180 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Lin, Li [VerfasserIn] |
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| Links: |
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| Themen: |
Feature selection |
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| Anmerkungen: |
Date Completed 27.03.2024 Date Revised 27.03.2024 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1016/j.ijbiomac.2024.130180 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| 520 | |a Copyright © 2024 Elsevier B.V. All rights reserved. | ||
| 520 | |a 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/linli5417/FRP-XGBoost | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Feature selection | |
| 650 | 4 | |a Ferroptosis-related proteins | |
| 650 | 4 | |a Machine learning | |
| 650 | 4 | |a Multi-view features | |
| 650 | 4 | |a Pan-cancer bioinformatics analysis | |
| 700 | 1 | |a Long, Yao |e verfasserin |4 aut | |
| 700 | 1 | |a Liu, Jinkai |e verfasserin |4 aut | |
| 700 | 1 | |a Deng, Dongliang |e verfasserin |4 aut | |
| 700 | 1 | |a Yuan, Yu |e verfasserin |4 aut | |
| 700 | 1 | |a Liu, Lubin |e verfasserin |4 aut | |
| 700 | 1 | |a Tan, Bin |e verfasserin |4 aut | |
| 700 | 1 | |a Qi, Hongbo |e verfasserin |4 aut | |
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