In-silico prediction of blood-secretory human proteins using a ranking algorithm.

ABSTRACT:

Background

Computational identification of blood-secretory proteins, especially proteins with differentially expressed genes in diseased tissues, can provide highly useful information in linking transcriptomic data to proteomic studies for targeted disease biomarker discovery in serum.

Results

A new algorithm for prediction of blood-secretory proteins is presented using an information-retrieval technique, called manifold ranking. On a dataset containing 305 known blood-secretory human proteins and a large number of other proteins that are either not blood-secretory or unknown, the new method performs better than the previous published method, measured in terms of the area under the recall-precision curve (AUC). A key advantage of the presented method is that it does not expl

SUBMITTER: Liu Q 

PROVIDER: S-EPMC2877692 | biostudies-literature | 2010 May

REPOSITORIES: biostudies-literature