期刊
IEEE ACCESS
卷 9, 期 -, 页码 12473-12490出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2021.3051006
关键词
Proteins; Support vector machines; Artificial neural networks; Kernel; Databases; Machine learning; Tagging; Artificial neural network; machine learning; protein-protein interaction; support vector machine
资金
- Mediterranea University of Reggio Calabria, Italy
Protein-Protein Interaction (PPI) is a crucial network in biology that requires fast, accurate, and critical analysis, with Support Vector Machine (SVM) being an effective tool for solving complex classification problems.
Protein-Protein Interaction (PPI) is a network of protein interconnections which regulates most of the biological methods. A sound state of biota largely depends on synchronized interactions between protein molecules, and any aberrant interactions between protein molecules may lead to complications, including cervical leukemia, tuberculosis, and other neural disorders. In PPI investigation, a plethora of computational methods have been developed over the years to analyze and predict PPI conclusively; however, a majority of these techniques proved to be strenuous and expensive. Therefore, the need for faster, accurate, and critical analysis of PPI warrants the adoption of Machine Learning (ML) methods such as Support Vector Machine (SVM), Artificial Neural Network (ANN), and Random Forest Model (RFM). These classifiers are useful in PPI unfolding in terms of amino acid sequence data. The SVM classifier, in particular, is serviceable in solving a majority of complex classification problems producing robust results in a reasonable time frame. This publication summarizes some state-of-art SVM based PPI investigations and challenges incurred in the application of the SVM method.
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