Application of Machine Learning Algorithms in Bioinformatics

Bioinformatics is a developing domain that discourses the necessity to achieve and understand the data that in the past era was enormously produced by genomic investigation. This domain signifies the convergence of genomics, biotechnology and involves examination and understanding of data, displaying of biological occurrences, which is one of the most significant field for the study of biological schemes today and expansion of algorithms and statistics. The paper objectives at a relative study of Machine learning algorithms on a breast cancer dataset. The algorithms used for comparison of ANN, ANFIS, ADT, BN, BFT, C4.5, CART, CBR, DT, DESVM, EP, GP, GA, GRU-SVM, IBk, J48, kNN, K*, KMSVM, kNN+ICA, kNNGA, LB, LMT, LR, MDR, MLP, NNge, NB+kNN, NNANF, NB, PART, RBFNN, RF, SVM, SMO, SL, SOM, SR, SMO-SVM, ZeroR– are supervised learning algorithms used extensively for classification purposes and are chosen for their variety. The performance of those algorithms are compared by using the different performance criterions based on confusion matrix such as Recall /Sensitivity, Specificity, Precision, Accuracy, F-Score and Mathews Correlation Coefficient. Based on analysis of this data, Artificial Neural Networks (ANN) is better at classification with 99.5% accuracy and 0.9901 Mathews correlation coefficient among others classifiers.