Protein Local Structure Prediction through Improved Clustering Support Vector Machines (ICSVM)
Main Article Content
Abstract
Accurate protein secondary structure prediction plays an important role in direct tertiary structure modeling, and can also significantly enhance sequence analysis and sequence-structure threading for structure and function determination. Understanding the sequence-to-structure relationship is a central task in bioinformatics. Adequate knowledge about this can improve the accuracy for protein structure prediction. The conventional algorithm such as clustering and SVM can not reveal the complex nonlinear relationship adequately on a huge amount of data individually. So the model CSVMs (Clustering Support Vector Machines) was designed by merging the concept of both clustering and SVM. It has been seen that the generalization power for CSVMs is strong enough to recognize the complicated pattern of sequence-to-structure relationships. CSVMs can only predict the protein local structure with which it is being trained. But if a new type of protein segment comes then it may happen that the CSVMs will fail. That is, none of the cluster will treat it as a positive sample. So in this paper we introduced another robust method called Improved Support Vector Machines (ICSVM) which can handle the unseen example efficiently.
Keywords: Clustering algorithm, SVM, Protein structure prediction, CSVMs, ICSVM.
Downloads
Article Details
COPYRIGHT
Submission of a manuscript implies: that the work described has not been published before, that it is not under consideration for publication elsewhere; that if and when the manuscript is accepted for publication, the authors agree to automatic transfer of the copyright to the publisher.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work
- The journal allows the author(s) to retain publishing rights without restrictions.
- The journal allows the author(s) to hold the copyright without restrictions.