Call For Paper Volume:6 Issue:2 Feb'2019 |

Classification of Protein Structure using SVM

Publication Date : 29/04/2015

Author(s) :

Caleb Vimal Lal , Archana Singh.

Volume/Issue :
Volume 2
Issue 4
(04 - 2015)

Abstract :

Protein domains are portion block of protein sequence that evolved independent function. Therefore, the classification of protein domain is becoming very important in order to produce new sequence with new function. However the main issue in protein domain classification is to classify the domain correctly into their category since the sequence coincidently classify to both category. Therefore, to overcome this issue, this dissertation proposed a method of functionally classifying genes by using gene expression data from DNA microarray hybridization experiments. The method is based on the theory of support vector machines (SVMs). SVMs are considered a supervised computer learning method because they exploit prior knowledge of gene function to identify unknown genes of similar function from expression data. SVMs avoid several problems associated with unsupervised clustering methods, such as hierarchical clustering and self-organizing maps.  SVMs have many mathematical features that make them attractive for gene expression analysis, including their flexibility in choosing a similarity function, sparseness of solution when dealing with large data sets, the ability to handle large feature spaces, and the ability to identify outliers. We test SVM that use different similarity metrics, as well as some other supervised learning methods and find that the SVM best identified different sets of genes with a common function using expression data. Finally, we use SVM to predict functional roles for uncharacterized yeast (Open Reading Frames) ORF based on their expression data.

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Classification of Protein Structure using SVM

April 16, 2015