Machine Learning in Bioinformatics: A Novel Approach for DNA Sequencing

TLDR: This paper gives a review on the mechanisms of gene sequence classification using Machine Learning techniques, which includes a brief detail on bioinformatics, literature survey and key issues in DNA Sequencing using Machine learning.

Abstract: Machine learning is the adaptive process that makes computers improve from experience, by example, and by analogy. So It is a discipline of methodologies that provides, in one form or another, intelligent information processing capabilities for handling real life. Bioinformatics is one of the application of Machine Learning. Bioinformatics is the interdisciplinary science of interpreting biological data using information technology and computer science. Machine learning (ML) focuses on automatic learning from data set. Machine learning includes the learning speed, the guarantee of convergence, and how the data can be learned incrementally. We usually refer to methods like Artificial Neural Networks (ANNs), Genetic algorithms (GAs), and Fuzzy systems along with hybrid methods including a combination of some of these methods. One of the major problems is to classify the normal genes and the invalid genes which are infected by some kind of diseases. In genomic research, classifying DNA sequences into existing categories is used to learn the functions of a new protein. So, it is important to identify those genes and classify them. In order to identify the infected genes and the normal genes with the use of classification methods here we use the machine learning techniques. This paper gives a review on the mechanisms of gene sequence classification using Machine Learning techniques, which includes a brief detail on bioinformatics, literature survey and key issues in DNA Sequencing using Machine Learning.