Increasing Confidence of Protein Interactomes

Participants: Jin Chen, Hon Nian Chua, Wynne Hsu, Mong Li Lee, Haiquan Li, Jinyan Li, Guimei Liu, See-Kiong Ng, Wing-Kin Sung, Chris Tan, Limsoon Wong


Progress in high-throughput experimental techniques in the past decade has resulted in a rapid accumulation of protein-protein interaction (PPI) data. However, recent surveys reveal that interaction data obtained by the popular high-throughput assays such as yeast-two-hybrid experiments may contain as much as 50% false positives and false negatives. As a result, further carefully-focused small-scale experiments are often needed to complement the large-scale methods to validate the detected interactions. However, the vast interactomes require much more scalable and inexpensive approaches.

Thus it would be useful if the list of protein-protein interactions detected by such high-throughput assays could be prioritized in some way. Advances in computational techniques for assessing the reliability of protein-protein interactions detected by such high-throughput methods are explored in this project, especially those rely only on topological information of the protein interaction network derived from such high-throughput experiments.


In this project, we have the following goals:

At the end of the project, we expect to have developed a robust and powerful system to postprocessing results of high-throughput PPI assays, yielding a more reliable protein interactome.

Selected Publications


Selected Presentations


This project is supported in part by a A*STAR AGS scholarship (Chua: 8/03 - 7/07), and the I2R-SOC Joint Lab on Knowledge Discovery from Clinical Data (7/03 - 6/07).

Last updated: 2/2/09, Limsoon Wong.