Mapping from Statistical to Biological Proximity

We verify whether the proximity claimed by state-of-the-art statistical similarity measures are indeed biologically appropriate or not. We present some analytical results on it

A Database for TSSs of Human MicroRNAs

MicroRNAs (miRNAs) are small endogeneous non-coding RNAs of about 22nt length. These short RNAs regulate the expression of mRNAs by hybridizing with their 3'-UTRs or by translational repression. They have been shown to take crucial roles in many biological processes. Many of the current studies are focused over how mature miRNAs regulate mRNAs, even though there is very limited knowledge about their transcriptional loci. Primary miRNAs (pri-miRs) are first transcribed from the DNA, followed by the formation of precursor miRNA (pre-miR) by endonucleases activity, which finally produces mature miRNAs. Unfortunately, the identification of the loci of pri-miRs, and the associated information about transcription start sites (TSSs) and promoters is still in progress. This information, even though limited, may be useful for further study on the regulation of miRNAs. In this paper, we provide a novel database of miRNA TSSs (miRT) that might be a valuable resource for advanced research on miRNA regulation

Mining Co-expression Graphs: Applications to MicroRNA Regulation and Disease Analysis

MicroRNAs (miRNAs) are known to translationally repress or post-transcriptionally regulate mRNAs and are responsible for many diseases. We are preparing a comprehensive framework of co-expression analysis to figure out co-expression, differential co-expression and co-expression dynamics within multiple phenotypes in expression profiles. The purpose is to elucidate the disease association of miRNAs via co-regulatory pattern analysis

From Machine Learning to Learning Machines - A Perspective toward Personalized Medicine

We describe how to learn a network using a bottom-up approach by building networks from expression profiles. Then we can analyze these networks with different graph mining approaches and by studying topological behaviors. Finally, how we can achieve personalized medicine from the network biology

Some Concepts of Graph Theory

We present some preliminary concepts of graph theory with interesting and easy examples

Integration of Co-expression Networks for Gene Clustering

Simultaneous overexpression or underexpression of multiple genes, used in various forms as probes in the high-throughput microarray experiments, facilitates the identification of their underlying functional proximity. This kind of functional associativity (or conversely the separability) between the genes can be represented proficiently using co-expression networks. The extensive repository of diversified microarray data encounters a recent problem of multi-experimental data integration for the aforesaid purpose. This paper highlights a novel integration method of gene co-expression networks, based on the search for their consensus network, derived from diverse microarray experimental data for the purpose of clustering. The proposed methodology avoids the bias arising from missing value estimation. The method has been applied on microarray datasets arising from different category of experiments to integrate them. The consensus network, thus produced, reflects robustness based on biological validation

Mining the Largest Quasi-clique in Human Protein Interactome

A clique is a complete subgraph of a graph. Often, a clique is interpreted as a dense module of vertices within a graph. However, in many real-world situations, the classical problem of finding a clique is required to be relaxed. This motivates the problem of finding quasicliques that are almost complete subgraphs of a graph. In sparse and very large scale-free networks, the problem of finding the largest quasi-clique becomes hard to manage with the existing approaches. Here, we propose a heuristic algorithm in this paper for locating the largest quasi-clique from the human protein-protein interaction networks. The results show promise in computational biology research by the exploration of significant protein modules