Identification of Cancerlectins By Using Cascade Linear Discriminant Analysis and Optimal g-gap Tripeptide Composition

Abstract

Background:: Lectins are a diverse group of glycoproteins or glycoconjugate proteins that can be extracted from plants, invertebrates and higher animals. Cancerlectins, a kind of lectins, which play a key role in the process of tumor cells interacting with each other and are being employed as therapeutic agents. A full understanding of cancerlectins is significant because it provides a tool for the future direction of cancer therapy. Objective:: To develop an accurate and practically useful timesaving tool to identify cancerlectins. A novel sequence-based method is proposed along with a correlative webserver to access the proposed tool. Methods: Firstly, protein features were extracted in a newly feature building way termed, g-gap tripeptide composition. After which a proposed cascade linear discriminant analysis (Cascade LDA) is used to alleviate the high dimensional difficulties with the Analysis Of Variance (ANOVA) as a feature importance criterion. Finally, Support Vector Machine (SVM) is used as the classifier to identify cancerlectins. Results: The proposed method achieved an accuracy of 91.34% with sensitivity of 89.89%, specificity of 92.48% and an 0.8318 Mathew’s correlation coefficient based on only 13 fusion features in jackknife cross validation, the result of which is superior to other published methods in this domain. Conclusion:: In this study, a new method based only on primary structure of protein is proposed and experimental results show that it could be a promising tool to identify cancerlectins. An openaccess webserver is made available in this work to facilitate other related works.

Dysbiosis of Gut Microbiota in Patients With Acute Myocardial Infarction

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iAIPs: Identifying Anti-Inflammatory Peptides Using Random Forest

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IBPred: A sequence-based predictor for identifying ion binding protein in phage

“…Many machine learning algorithms, such as support vector machine (SVM) [10] , [11] , [12] , deep learning (DL) [13] , [14] , [15] , [16] , [17] , [18] , [19] , extreme boosting algorithm (XGBoost) [20] , [21] , [22] , [23] , [24] , and stacking ensemble models [25] , [26] , [27] , [28] , [29] , [30] , etc., have been developed for protein function, structure, subcellular localization, and even other biological processes. Different feature descriptors such as amino acid composition (AAC) [31] , [32] , [33] , reduced amino acid composition [34] , [35] , [36] , g -gap dipeptide composition [37] , [38] , and secondary structure features [39] , etc., were adopted to represent protein sequences. While there is still no computational method to identify IBPs in phages, this study aims to design a novel model for IBP prediction.…”