Title

Authors

Affiliation

¹Department of Science, Systems and Models, Roskilde University, DK-4000 Roskilde, Denmark; ²School of Computer Engineering, PDCC, Nanyang Technological University, Singapore-639798

Email

prash@bioclues.org; *Corresponding author

Article Type

Hypothesis

Date

Received December 11, 2011; Accepted December 20, 2011; Published January 06, 2012

Hypothetical protein [HP] annotation poses a great challenge especially when the protein is putatively linked or mapped to another protein. With protein interaction networks (PIN) prevailing, many visualizers still remain unsupported to the HP annotation. Through this work, we propose a six-point classification system to validate protein interactions based on diverse features. The HP data-set was used as a training data-set to find putative functional interaction partners to the remaining proteins that are waiting to be interacting. A Total Reliability Score (TRS) was calculated based on the six-point classification which was evaluated using machine learning algorithm on a single node. We found that multilayer perceptron of neural network yielded 81.08% of accuracy in modelling TRS whereas feature selection algorithms confirmed that all classification features are implementable. Furthermore statistical results using variance and co-variance analyses confirmed the usefulness of these classification metrics. It has been evaluated that of all the classification features, subcellular location (sorting signals) makes higher impact in predicting the function of HPs.

Keywords

hypothetical proteins, protein interaction networks, total reliability score

Citation

Suravajhala & Sundararajan, Bioinformation 8(1): 034-039 (2012)
 

Edited by

P Kangueane

ISSN

0973-2063

Publisher

Biomedical Informatics

License

This is an Open Access article which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. This is distributed under the terms of the Creative Commons Attribution License.