ILipo-PseAAC: Identification of Lipoylation Sites Using Statistical Moments and General PseAAC  

作　　者：Talha Imtiaz Baig Yaser Daanial Khan Talha Mahboob Alam Bharat Biswal Hanan Aljuaid Durdana Qaiser Gillani 

机构地区：[1]School of Science and Technology,University of Management and Technology,Lahore,Pakistan [2]Department of Computer Science and Information Technology,Virtual University of Pakistan,Lahore,Pakistan [3]Department of Biomedical Engineering,New Jersey Institute of Technology,Newark,NJ,USA [4]Department of Computer Sciences,College of Computer and Information Sciences,Princess Nourah bint Abdul Rahman University(PNU),Riyadh,Saudi Arabia [5]Department of Economics,University of Lahore,Lahore,Pakistan

出　　处：《Computers, Materials & Continua》2022年第4期215-230,共16页计算机、材料和连续体（英文）

摘　　要：Lysine Lipoylation is a protective and conserved Post Translational Modification(PTM)in proteomics research like prokaryotes and eukaryotes.It is connected with many biological processes and closely linked with many metabolic diseases.To develop a perfect and accurate classification model for identifying lipoylation sites at the protein level,the computational methods and several other factors play a key role in this purpose.Usually,most of the techniques and different traditional experimental models have a very high cost.They are time-consuming;so,it is required to construct a predictor model to extract lysine lipoylation sites.This study proposes a model that could predict lysine lipoylation sites with the help of a classification method known as Artificial Neural Network(ANN).The ANN algorithm deals with the noise problem and imbalance classification in lipoylation sites dataset samples.As the result shows in ten-fold cross-validation,a brilliant performance is achieved through the predictor model with an accuracy of 99.88%,and also achieved 0.9976 as the highest value of MCC.So,the predictor model is a very useful and helpful tool for lipoylation sites prediction.Some of the residues around lysine lipoylation sites play a vital part in prediction,as demonstrated during feature analysis.The wonderful results reported through the evaluation and prediction of this model can provide an informative and relative explanation for lipoylation and its molecular mechanisms.

关 键 词：Lipoylation lysine feature vector post translational modification amino acid Mathew’s correlation coefficient neural network 

分 类 号：Q782[生物学—分子生物学]