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Ensemble Machine Learning to Enhance q8 Protein Secondary Structure prediction

Computers, Materials & Continua 2022年第11期73卷 3951-3967页

作者： Moheb R.Girgis Rofida M.Gamal Enas Elgeldawi Computer Science Department Faculty of ScienceMinia University61519MiniaEgypt

Protein structure prediction is one of the most essential objectives practiced by theoretical chemistry and bioinformatics as it is of a vital importance in medicine,biotechnology and *** secondary structure prediction(PSSP)has a significant role in the prediction of protein tertiary structure,as it bridges the gap between the protein primary sequences and tertiary structure *** secondary structures are classified into two categories:3-state category and 8-state *** the 3 states and the 8 states of secondary structures from protein sequences are called the q3 prediction and the q8 prediction problems,*** 8 classes of secondary structures reveal more precise structural information for a variety of applications than the 3 classes of secondary structures,however,q8 prediction has been found to be very challenging,that is why all previous work done in PSSP have focused on q3 *** this paper,we develop an ensemble Machine Learning(ML)approach for q8 PSSP to explore the performance of ensemble learning algorithms compared to that of individual ML algorithms in q8 *** ensemble members considered for constructing the ensemble models are well known classifiers,namely SVM(Support Vector Machines),KNN(K-Nearest Neighbor),DT(Decision Tree),RF(Random Forest),and NB(Naïve Bayes),with two feature extraction techniques,namely LDA(Linear Discriminate Analysis)and PCA(Principal Component Analysis).Experiments have been conducted for evaluating the performance of single models and ensemble models,with PCA and LDA,in q8 *** novelty of this paper lies in the introduction of ensemble learning in q8 PSSP *** experimental results confirmed that ensemble ML models are more accurate than individual ML *** also indicated that features extracted by LDA are more effective than those extracted by PCA.

关键词： Protein secondary structure prediction(PSSP) q3 prediction q8 prediction ensemble machine leaning boosting bagging

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