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A Comprehensive Review on Machine Learning Techniques for Protein Family Prediction

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Abstract

Proteomics is a field dedicated to the analysis of proteins in cells, tissues, and organisms, aiming to gain insights into their structures, functions, and interactions. A crucial aspect within proteomics is protein family prediction, which involves identifying evolutionary relationships between proteins by examining similarities in their sequences or structures. This approach holds great potential for applications such as drug discovery and functional annotation of genomes. However, current methods for protein family prediction have certain limitations, including limited accuracy, high false positive rates, and challenges in handling large datasets. Some methods also rely on homologous sequences or protein structures, which introduce biases and restrict their applicability to specific protein families or structures. To overcome these limitations, researchers have turned to machine learning (ML) approaches that can identify connections between protein features and simplify complex high-dimensional datasets. This paper presents a comprehensive survey of articles that employ various ML techniques for predicting protein families. The primary objective is to explore and improve ML techniques specifically for protein family prediction, thus advancing future research in the field. Through qualitative and quantitative analyses of ML techniques, it is evident that multiple methods utilizing a range of classifiers have been applied for protein family prediction. However, there has been limited focus on developing novel classifiers for protein family classification, highlighting the urgent need for improved approaches in this area. By addressing these challenges, this research aims to enhance the accuracy and effectiveness of protein family prediction, ultimately facilitating advancements in proteomics and its diverse applications.

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Data Availability

Data will be made available based on the request.

Abbreviations

AUC:

Area Under Curve

AUPR:

Area Under Precision Recall Curve

BLAST:

Basic Local Alignment Search Tool

DT:

Decision Tree

ELM:

Extreme Learning Machines

FN:

False Negatives

FP:

False Positives

FPR:

False Positive Rate

GO:

Gene Ontology

GPCR:

G-protein Coupled Receptors

GRU:

Gate Recurrent Unit

HMMs:

Hidden Markov Models

KNN:

K- Nearest Neighbor

MCC:

Mathew’s Correlation Coefficient

ML:

Machine Learning

MLP:

Multilayer Perceptron

NB:

Naive Bayes

Net Go:

Network information based Go

NMR:

Nuclear Magnetic Resonance

PPI:

Protein-Protein interaction

PseAAC:

Pseudo Amino Acid Composition

RF:

Random Forest

SVM:

Support Vector Machine

TN:

True Negatives

TP:

True Positives

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Manonmaniam Sundaranar University, Tirunelveli, TamilNadu, India

    T. Idhaya & A. Suruliandi

  2. School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, TamilNadu, India

    S. P. Raja

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Contributions

“T. Idhaya had done the writing and drafting. A. Suruliandi had done the supervision. S.P. Raja had done the implementation. All authors are aware of the submission. All authors read and agreed on the final version of the manuscript. All authors reviewed the manuscript.”

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Correspondence to T. Idhaya.

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Idhaya, T., Suruliandi, A. & Raja, S.P. A Comprehensive Review on Machine Learning Techniques for Protein Family Prediction. Protein J (2024). https://doi.org/10.1007/s10930-024-10181-5

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