Supervised maximum-likelihood weighting of composite protein networks for complex prediction

10.1186/1752-0509-6-S2-S13BMC Systems Biology6SUPPL.2

Methods for protein complex prediction and their contributions towards understanding the organization, function and dynamics of complexes

Complexes of physically interacting proteins constitute fundamental functional units responsible for driving biological processes within cells. A faithful reconstruction of the entire set of complexes is therefore essential to understand the functional organization of cells. In this review, we discuss the key contributions of computational methods developed till date (approximately between 2003 and 2015) for identifying complexes from the network of interacting proteins (PPI network). We evaluate in depth the performance of these methods on PPI datasets from yeast, and highlight challenges faced by these methods, in particular detection of sparse and small or sub- complexes and discerning of overlapping complexes. We describe methods for integrating diverse information including expression profiles and 3D structures of proteins with PPI networks to understand the dynamics of complex formation, for instance, of time-based assembly of complex subunits and formation of fuzzy complexes from intrinsically disordered proteins. Finally, we discuss methods for identifying dysfunctional complexes in human diseases, an application that is proving invaluable to understand disease mechanisms and to discover novel therapeutic targets. We hope this review aptly commemorates a decade of research on computational prediction of complexes and constitutes a valuable reference for further advancements in this exciting area.Comment: 1 Tabl

Discovering Dynamic Protein Complexes from Static Interacomes: Three Challenges

Ph.DDOCTOR OF PHILOSOPH

U.S. Quantitative Easing Policy Effect on TAIEX Futures Market Efficiency

This paper examines the market efficiency of the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) Futures after the announcement of Quantitative Easing (QE) policy. Order imbalance is used to explore the relationship between return and order imbalance. We find that under the unconditional OLS model, lagged order imbalances almost have no significantly positive predictive power for current return. Nonetheless, on the trading day after the announcement of QE 1 policy, one-minute interval data show that the lagged order imbalance has predictive power for current return. Under the conditional OLS model, the reversed relation between current return and lagged order imbalance is not universal; on the other hand, after the announcement of QE 2 policy, the reversed relation between current return and lagged order imbalance is more common. Moreover, under volatility-GARCH (1, 1), one-minute interval data shows significantly positive relation between order imbalance and volatility

Decomposing PPI networks for complex discovery

<p>Abstract</p> <p>Background</p> <p>Protein complexes are important for understanding principles of cellular organization and functions. With the availability of large amounts of high-throughput protein-protein interactions (PPI), many algorithms have been proposed to discover protein complexes from PPI networks. However, existing algorithms generally do not take into consideration the fact that not all the interactions in a PPI network take place at the same time. As a result, predicted complexes often contain many spuriously included proteins, precluding them from matching true complexes.</p> <p>Results</p> <p>We propose two methods to tackle this problem: (1) The localization GO term decomposition method: We utilize cellular component Gene Ontology (GO) terms to decompose PPI networks into several smaller networks such that the proteins in each decomposed network are annotated with the same cellular component GO term. (2) The hub removal method: This method is based on the observation that hub proteins are more likely to fuse clusters that correspond to different complexes. To avoid this, we remove hub proteins from PPI networks, and then apply a complex discovery algorithm on the remaining PPI network. The removed hub proteins are added back to the generated clusters afterwards. We tested the two methods on the yeast PPI network downloaded from BioGRID. Our results show that these methods can improve the performance of several complex discovery algorithms significantly. Further improvement in performance is achieved when we apply them in tandem.</p> <p>Conclusions</p> <p>The performance of complex discovery algorithms is hindered by the fact that not all the interactions in a PPI network take place at the same time. We tackle this problem by using localization GO terms or hubs to decompose a PPI network before complex discovery, which achieves considerable improvement.</p