Biomedical subjects

Jake Yue Chen

Publications and source records attributed to Jake Yue Chen.

3 recordsLinked to original sources

PubMed2006

A statistical framework to discover true associations from multiprotein complex pull-down proteomics data sets.

Experimental processes to collect and process proteomics data are increasingly complex, and the computational methods to assess the quality and significance of these data remain unsophisticated. These challenges have led to many biological oversights and computational misconceptions. We developed an empirical Bayes model to analyze multiprotein complex (MPC) proteomics data derived from peptide mass spectrometry detections of purified protein complex pull-down experiments. Using our model and two yeast proteomics data sets, we estimated that there should be an average of about 20 true associations per MPC, almost 10 times as high as was previously estimated. For data sets generated to mimic a real proteome, our model achieved on average 80% sensitivity in detecting true associations, as compared with the 3% sensitivity in previous work, while maintaining a comparable false discovery rate of 0.3%. Cross-examination of our results with protein complexes confirmed by various experimental techniques demonstrates that many true associations that cannot be identified by previous approach are identified by our method.

Algorithms↗

PubMed2006

Mining Alzheimer disease relevant proteins from integrated protein interactome data.

Huge unrealized post-genome opportunities remain in the understanding of detailed molecular mechanisms for Alzheimer Disease (AD). In this work, we developed a computational method to rank-order AD-related proteins, based on an initial list of AD-related genes and public human protein interaction data. In this method, we first collected an initial seed list of 65 AD-related genes from the OMIM database and mapped them to 70 AD seed proteins. We then expanded the seed proteins to an enriched AD set of 765 proteins using protein interactions from the Online Predicated Human Interaction Database (OPHID). We showed that the expanded AD-related proteins form a highly connected and statistically significant protein interaction sub-network. We further analyzed the sub-network to develop an algorithm, which can be used to automatically score and rank-order each protein for its biological relevance to AD pathways(s). Our results show that functionally relevant AD proteins were consistently ranked at the top: among the top 20 of 765 expanded AD proteins, 19 proteins are confirmed to belong to the original 70 AD seed protein set. Our method represents a novel use of protein interaction network data for Alzheimer disease studies and may be generalized for other disease areas in the future.

Algorithms↗

PubMed2005

Discover true association rates in multi-protein complex proteomics data sets.

Experimental processes to collect and process proteomics data are increasingly complex, while the computational methods to assess the quality and significance of these data remain unsophisticated. These challenges have led to many biological oversights and computational misconceptions. We developed a complete empirical Bayes model to analyze multi-protein complex (MPC) proteomics data derived from peptide mass spectrometry detections of purified protein complex pull-down experiments. Our model considers not only bait-prey associations, but also prey-prey associations missed in previous work. Using our model and a yeast MPC proteomics data set, we estimated that there should be an average of 28 true associations per MPC, almost ten times as high as was previously estimated. For data sets generated to mimic a real proteome, our model achieved on average 80% sensitivity in detecting true associations, as compared with the 3% sensitivity in previous work, while maintaining a comparable false discovery rate of 0.3%.

Algorithms↗