Artifical Neural Network Based Classification Technique for Biological Databases

Abstract

Species classification from DNA sequences remains as an open challenge in the area of Bioinformatics which deals with the collection processing and analysis of DNA and proteomic sequence Though incorporation of data mining can guide the process to perform well poor definition and heterogeneous nature of gene sequence remains as a barrier In this research work an effective classification technique to identify the organism from its gene sequence is suggested The proposed integrated technique is mainly based on pattern mining and neural network based classification In pattern mining the technique mines nucleotide patterns and their support from selected DNA sequence The high dimension of the mined dataset is reduced using Multilinear Principal Component Analysis MPCA Basically a multilinear principal component analysis MPCA framework for tensor object is nothing but the feature extraction work Objects of the interest in this pattern recognition application are naturally described as tensors or multilinear arrays The proposed framework performs feature extraction by determining a multilinear projection that captures most of the original tonsorial input variation The solution is iterative in nature and it proceeds by decomposing the original problem to a series of multiple projection sub problems In classification a well trained neural network classifies the selected gene sequence and so the organism is identified even from a part of the sequence The proposed technique is evaluated by performing tenfold cross validation a statistical validation measure and the obtained results prove the efficacy of the technique The same technique is extended further to identify the differences in genes The genetic data of diseases are available from research centers and clinical laboratories Clinical diagnosis is done mostly by experienced doctors with expertise in this field In many cases the test results are not effective towards the diagnosis of the disease The particular issue is about the wrong diagnosis which leads to a wrong