A novel Fibonacci hash method for protein family identification by using recurrent neural networks

Abstract

Identification and classification of protein families are one of the most significant problem in bioinformatics and protein studies. It is essential to specify the family of a protein since, proteins are highly used in smart drug therapies, protein functions and in some case, phylogenetic trees. Some sequencing techniques provide researchers to identify the biological similarities of protein families and functions. Yet, determining these families with sequencing applications requires huge amount of time. Thus, it is needed a computer and artificial intelligence based classification system to save the time, and avoid complexity in protein classification process. In order to designate the protein families with computer-aided systems, protein sequences need to be converted to the numerical representations. In this paper, we provide a novel protein mapping method based on Fibonacci numbers and hashing table (FIBHASH). Each amino acid code is assigned to the Fibonacci numbers based on integer representations respectively. Later, these amino acid codes are inserted a hashing table with the size of 20 to be classified with recurrent neural networks. To determine the performance of the proposed mapping method, we used accuracy, f1-score, recall, precision, and AUC evaluation criteria. In addition, the results of evaluation metrics with other protein mapping techniques including EIIP, hydrophobicity, CPNR, Atchley factors, BLOSUM62, PAM250, binary one-hot encoding, and randomly encoded representations are compared. The proposed method showed a promising result with an accuracy of 92.77%, and 0.98 AUC score.

Prediction of viral-host interactions of COVID-19 by computational methods

“…In the FIBHASH method, the researchers proposed a hybrid model using Fibonacci numbers and hash tables and mapped the protein sequences [ 38 ]. In the proposed method, first the protein sequences are sorted alphabetically, and Fibonacci numbers are added to each amino acid in order.…”

Energy Profile Bayes and Thompson Optimized Convolutional Neural Network protein structure prediction

“…However, determining these families with sequencing yet consumes an enormous time. A novel protein mapping method was designed in [ 32 ] based on the Fibonacci numbers and hashing table called (FIBHASH). The Fibonacci number was assigned to each amino acid code based on integer representations.…”

A Novel Protein Mapping Method for Predicting the Protein Interactions in COVID-19 Disease by Deep Learning

“…The main purpose in the development of the method is to eliminate the degeneration problem that occurs in the EIIP method. There are a few studies in the literature performed with the CPNR method [30,31]. The hydrophobicity method was proposed based on the hydrophilic and hydrophobic tendencies of the polypeptide chains of proteins [32].…”