Genic Disorder Identification and Protein Analysis Using Soft Computing Methods

Abstract

The field of Omics [1] has produced a large amount of research data, which is desirable for processing and estimating the discriminant classes and disordered sequences, usually the gene and protein play an vital role in controlling the biological process of the human body, with the use of genic data one can easily able to find the mutated gene causing disease and by the use of protein data the intrinsic disorder protein causing defective parts activity can be traced out. This paper brings out the soft computational machine learning research efforts in the genomic [2] and proteomic [3] data, thus providing easier machine intelligence disease classifier [4] with discriminant feature selection. Then the disease features are effective in selecting the optimal disorder enzyme causing protein [5], so that the relevant biological process activities [6] affected due to the various protein enzyme causing effects can be effectively comprehended.