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Machine learning (ML) is a technique to develop computer program to access data and to learn knowledge automatically from experience without human interventions and assistance. Machine learning techniques and deep learning algorithms enable the classifier to make use of automatic feature learning technique, to make reasonably complex predictions when the model is being trained on large datasets. It means the algorithm is able to learn based on the dataset alone and can discover ways to integrate numerous features of the input information into one intellectual set of features from which further learning can be done [9]. Machine learning technique uses two different methods to train the model: supervised learning and unsupervised learning method [16]. In subsequent sections we will discuss machine learning models used for supervised learning problems.

In recent years, availability of biological datasets have risen abruptly, this has enabled bioinformatics researchers to make use of these machine learning algorithms. ML techniques has been applied to many biological domains such as Microarrays, Systems biology, Genomics, Proteomics, Stroke diagnosis and Text mining, etc. [8].

 Machine learning in bioinformatics helps to explore, analyze, manage and store data to extract relevant information from biological data.

 Gene identification and nucleotide identification help to understand gene and gene association with disease.

 Machine learning tools are also used to determine genomic sequence and examine gene pattern.

 Gene sequence classification allows us to grasp the concept of nucleic acid and protein sequence.

Advancement of this field can ultimately leads to the development of automated diagnostic tools, personalized and precision medicine, gene therapy, food analysis, biodiversity management and many more which will target Individual’s lifestyle, environment and custom medical treatments considering person’s vulnerability to disease.

There are many machine learning techniques among which Artificial Neural Network is an effective technique for the identification, selection, classification and prediction of the gene in the DNA Sequences.