Computational Technique for an Efficient Classification of Protein Sequences With Distance‐Based Sequence Encoding Algorithm

Abstract

Machine learning is being implemented in bioinformatics and computational biology to solve challenging problems emerged in the analysis and modeling of biological data such as DNA, RNA, and protein. The major problems in classifying protein sequences into existing families/superfamilies are the following: the selection of a suitable sequence encoding method, the extraction of an optimized subset of features that possesses significant discriminatory information, and the adaptation of an appropriate learning algorithm that classifies protein sequences with higher classification accuracy. The accurate classification of protein sequence would be helpful in determining the structure and function of novel protein sequences. In this article, we have proposed a distance‐based sequence encoding algorithm that captures the sequence's statistical characteristics along with amino acids sequence order information. A statistical metric‐based feature selection algorithm is then adopted to identify the reduced set of features to represent the original feature space. The performance of the proposed technique is validated using some of the best performing classifiers implemented previously for protein sequence classification. An average classification accuracy of 92% was achieved on the yeast protein sequence data set downloaded from the benchmark UniProtKB database.

Classification of Biological Data using Deep Learning Technique

“…The length of the sequences is from few amino acids to thousands of amino acids. There are many methods for protein sequences encoding out there including distance based encoding [4], [16] that captures statistical characteristics of protein sequences. In our work we transformed the labels in to one hot vector representation using LabelBinarizer from sklearn.preprocessing.…”

“…However, it is very expensive to characterize functions for biological experiments and also, it is really necessary to find the association between the information of datasets to create and improve medical tools. For classification purpose, several classification techniques were developed [3], [4]. These techniques can be divided in two parts: Sequence alignment and Machine learning algorithms.…”

Network Intrusion Detection Based on Amino Acid Sequence Structure Using Machine Learning

“…There are 20 essential Amino acids (Figure 1) in nature. Each Amino acid has a unique chemical structure that gives it specific properties [3]. The Amino acid signatures represent encoded instances of network transactions using essential Amino acid labels that encode numerical structural properties using a Vigesimal numbering system to map ASCII codes to Amino acids.…”