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

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 explicitly require a negative training set, which could often be noisy or difficult to derive for most biological problems, hence making our method more applicable than classification-based data mining methods in gen...

• Type: Journal
• Year: 2010
• Where: BMCBI
• Authors: Qi Liu, Juan Cui, Qiang Yang, Ying Xu
• Keywords: Blood-secretory Human Proteins, Blood-secretory Proteins, BMCBI 2010, Protein