Publication:
Interaction prediction of PDZ domains using a machine learning approach

Placeholder

School / College / Institute

Organizational Unit

Program

KU Authors

Co-Authors

N/A

Publication Date

Language

Embargo Status

Journal Title

Journal ISSN

Volume Title

Alternative Title

Abstract

Protein interaction domains play crucial roles in many complex cellular pathways. PDZ domains are one of the most common protein interaction domains. Prediction of binding specificity of PDZ domains by a computational manner could eliminate unnecessary, time-consuming experiments. In this study, interactions of PDZ domains are predicted by using a machine learning approach in which only primary sequences of PDZ domains and peptides are used. In order to encode feature vectors for each interaction, trigram frequencies of primary sequences of PDZ domains and corresponding peptides are calculated. After construction of numerical interaction dataset, we compared different classifiers and ended up with Random Forest (RF) algorithm which gave the top performance. We obtained very high prediction accuracy (91.4%) for binary interaction prediction which outperforms all previous similar methods.

Source

Publisher

IEEE

Subject

Biology, Computer engineering, Bioinformatics

Citation

Has Part

Source

2010 5th International Symposium on Health Informatics and Bioinformatics, HIBIT 2010

Book Series Title

Edition

DOI

10.1109/HIBIT.2010.5478896

item.page.datauri

Link

Rights

Copyrights Note

Endorsement

Review

Supplemented By

Referenced By

0

Views

0

Downloads

View PlumX Details