Protein interaction predictions from diverse sources.

@article{citeulike:2805244, abstract = {Protein-protein interactions play an important role in many cellular processes. The availability of a comprehensive and accurate list of protein interactions can facilitate drug target discovery. Recent advances in high-throughput experimental technologies have generated enormous amounts of data and provided valuable resources for studying protein interactions. However, these technologies suffer from high error rates because of their inherent limitations. Therefore, computational approaches capable of incorporating multiple data sources are needed to fully take advantage of the rapid accumulation of data. In this review, we focus on the computational methods that integrate multiple data sources by combining direct measurements on protein interactions from diverse organisms, and by integrating different types of indirect information from various genomic and proteomic approaches.}, address = {Department of Neurobiology and Anatomy, University of Texas Medical School at Houston, 6431 Fannin Street, Houston, TX 77030, USA.}, author = {Liu, Y. and Kim, I. and Zhao, H. }, citeulike-article-id = {2805244}, doi = {10.1016/j.drudis.2008.01.005}, issn = {1359-6446}, journal = {Drug discovery today}, month = {May}, number = {9-10}, pages = {409--416}, posted-at = {2008-05-16 14:04:05}, priority = {2}, title = {Protein interaction predictions from diverse sources.}, url = {http://dx.doi.org/10.1016/j.drudis.2008.01.005}, volume = {13}, year = {2008} }

TY - JOUR ID - citeulike:2805244 L3 - citeulike-article-id:2805244 TI - Protein interaction predictions from diverse sources. JF - Drug discovery today VL - 13 IS - 9-10 SP - 409 EP - 416 AD - Department of Neurobiology and Anatomy, University of Texas Medical School at Houston, 6431 Fannin Street, Houston, TX 77030, USA. SN - 1359-6446 N2 - Protein-protein interactions play an important role in many cellular processes. The availability of a comprehensive and accurate list of protein interactions can facilitate drug target discovery. Recent advances in high-throughput experimental technologies have generated enormous amounts of data and provided valuable resources for studying protein interactions. However, these technologies suffer from high error rates because of their inherent limitations. Therefore, computational approaches capable of incorporating multiple data sources are needed to fully take advantage of the rapid accumulation of data. In this review, we focus on the computational methods that integrate multiple data sources by combining direct measurements on protein interactions from diverse organisms, and by integrating different types of indirect information from various genomic and proteomic approaches. AU - Liu, Y AU - Kim, I AU - Zhao, H PY - 2008/05// UR - http://dx.doi.org/10.1016/j.drudis.2008.01.005 ER -