Accurately identifying positive and negative regulation of apoptosis using fusion features and machine learning methods

IF 2.6 4区 生物学 Q2 BIOLOGY Computational Biology and Chemistry Pub Date : 2024-09-11 DOI:10.1016/j.compbiolchem.2024.108207
Cheng-Yan Wu , Zhi-Xue Xu , Nan Li , Dan-Yang Qi , Zhi-Hong Hao , Hong-Ye Wu , Ru Gao , Yan-Ting Jin
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Abstract

Apoptotic proteins play a crucial role in the apoptosis process, ensuring a balance between cell proliferation and death. Thus, further elucidating the regulatory mechanisms of apoptosis will enhance our understanding of their functions. However, the development of computational methods to accurately identify positive and negative regulation of apoptosis remains a significant challenge. This work proposes a machine learning model based on multi-feature fusion to effectively identify the roles of positive and negative regulation of apoptosis. Initially, we constructed a reliable benchmark dataset containing 200 positive regulation of apoptosis and 241 negative regulation of apoptosis proteins. Subsequently, we developed a classifier that combines the support vector machine (SVM) with pseudo composition of k-spaced amino acid pairs (PseCKSAAP), composition transition distribution (CTD), dipeptide deviation from expected mean (DDE), and PSSM-composition to identify these proteins. Analysis of variance (ANOVA) was employed to select optimized features that could yield the maximum prediction performance. Evaluating the proposed model on independent data revealed and achieved an accuracy of 0.781 with an AUROC of 0.837, demonstrating our model's potent capabilities.

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利用融合特征和机器学习方法准确识别细胞凋亡的正向和负向调控
凋亡蛋白在细胞凋亡过程中发挥着至关重要的作用,确保了细胞增殖和死亡之间的平衡。因此,进一步阐明细胞凋亡的调控机制将增进我们对其功能的了解。然而,开发计算方法以准确识别细胞凋亡的正负调控仍是一项重大挑战。本研究提出了一种基于多特征融合的机器学习模型,以有效识别细胞凋亡正负调控的作用。首先,我们构建了一个可靠的基准数据集,其中包含 200 个凋亡正调控蛋白和 241 个凋亡负调控蛋白。随后,我们开发了一种分类器,将支持向量机(SVM)与k间隔氨基酸对的伪组成(PseCKSAAP)、组成转换分布(CTD)、二肽与预期平均值的偏差(DDE)和PSSM-组成相结合来识别这些蛋白质。采用方差分析(ANOVA)来选择能产生最大预测性能的优化特征。在独立数据上对所提出的模型进行评估后发现,该模型的准确率达到了 0.781,AUROC 为 0.837,这证明了我们模型的强大功能。
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来源期刊
Computational Biology and Chemistry
Computational Biology and Chemistry 生物-计算机:跨学科应用
CiteScore
6.10
自引率
3.20%
发文量
142
审稿时长
24 days
期刊介绍: Computational Biology and Chemistry publishes original research papers and review articles in all areas of computational life sciences. High quality research contributions with a major computational component in the areas of nucleic acid and protein sequence research, molecular evolution, molecular genetics (functional genomics and proteomics), theory and practice of either biology-specific or chemical-biology-specific modeling, and structural biology of nucleic acids and proteins are particularly welcome. Exceptionally high quality research work in bioinformatics, systems biology, ecology, computational pharmacology, metabolism, biomedical engineering, epidemiology, and statistical genetics will also be considered. Given their inherent uncertainty, protein modeling and molecular docking studies should be thoroughly validated. In the absence of experimental results for validation, the use of molecular dynamics simulations along with detailed free energy calculations, for example, should be used as complementary techniques to support the major conclusions. Submissions of premature modeling exercises without additional biological insights will not be considered. Review articles will generally be commissioned by the editors and should not be submitted to the journal without explicit invitation. However prospective authors are welcome to send a brief (one to three pages) synopsis, which will be evaluated by the editors.
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