2Gram Features Based Prediction of Membrane Protein Types Using Ensemble Classifiers Methods

18 Pages Posted: 25 Nov 2020 Last revised: 4 Dec 2020

See all articles by AnjnaJayant Deen

AnjnaJayant Deen

Maulana Azad National Institute of Technology Bhopal

Manasi Gayanchandani

Maulana Azad National Institute of Technology

Date Written: November 19, 2020

Abstract

Membrane proteins are an essential type of protein used in a cell as receptors, channels, and energy transducers. Moreover, they do many of the functions imperative to the survival of the cell. Therefore, it is necessary to build up machine learning methods to correctly identify membrane protein types to recognize protein function, disease occurrence, and drug therapy design. So the function prediction of membrane protein performs a key task. In this paper, membrane protein types are detected by using 2-gram and sequence length feature. The predictor ensemble classifiers used to predict a membrane protein, with sequence length encodes single descriptor value and 2-gram exchange group, encodes multilabel feature types. Were, 2-gram special kinds of feature type focus on its different modes of protein information to emulate the fundamental functions that are enormously unknown in complex protein sequences and find a relation between residues. The proposed method avoids biasing among the differentiation between types of membrane proteins. Ensemble classifiers well handle the imbalanced protein datasets.

Keywords: Membrane Protein; SVM; AdaBoost; RUSBoost; Random Forest; Extratree; 2Gram Exchange Group.

Suggested Citation

Deen, AnjnaJayant and Gayanchandani, Manasi, 2Gram Features Based Prediction of Membrane Protein Types Using Ensemble Classifiers Methods (November 19, 2020). Proceedings of the 2nd International Conference on IoT, Social, Mobile, Analytics & Cloud in Computational Vision & Bio-Engineering (ISMAC-CVB 2020), Available at SSRN: https://ssrn.com/abstract=3733554 or http://dx.doi.org/10.2139/ssrn.3733554

AnjnaJayant Deen (Contact Author)

Maulana Azad National Institute of Technology Bhopal ( email )

Bhopal

Manasi Gayanchandani

Maulana Azad National Institute of Technology ( email )

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