By Topic

Systems Biology via Redescription and Ontologies (III): Protein Classification Using Malaria Parasite's Temporal Transcriptomic Profiles

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

5 Author(s)
Mitrofanova, A. ; Comput. Sci. Dept., New York Univ., New York, NY ; Kleinberg, S. ; Carlton, J. ; Kasif, S.
more authors

This paper addresses the protein classification problem, andexplores how its accuracy can be improved by using information fromtime-course gene expression data. The methods are tested on datafrom the most deadly species of the parasite responsible for malariainfections, Plasmodium falciparum. Even though avaccination for Malaria infections has been under intense study formany years, more than half of Plasmodiumproteins still remain uncharacterized and therefore are exemptedfrom clinical trials. The task is further complicated by arapid life cycle of the parasite, thus making precisetargeting of the appropriate proteins for vaccination a technicalchallenge. We propose to integrate protein-protein interactions (PPIs),sequence similarity, metabolic pathway, andgene expression, to produce a suitable set of predicted proteinfunctions for P.falciparum. Further,we treat gene expression data withrespect to various changes that occur during the five phases of theintraerythrocytic developmental cycle (IDC) (as determinedby our segmentation algorithm) ofP.falciparum and show that this analysis yields asignificantly improved protein function prediction, e.g., whencompared to analysis based on Pearson correlation coefficients seenin the data. The algorithm is able to assign ``meaningful''functions to 628 out of 1439 previously unannotated proteins, whichare first-choice candidates for experimental vaccine research.

Published in:

Bioinformatics and Biomedicine, 2008. BIBM '08. IEEE International Conference on

Date of Conference:

3-5 Nov. 2008