BACKGROUND: The significant advances in microarray and proteomics analyses have resulted in an exponential increase in potential new targets and have promised to shed light on the identification of disease markers and cellular pathways. We aim to collect and decipher the HCC-related genes at the systems level. RESULTS: Here, we build an integrative platform, the Encyclopedia of Hepatocellular Carcinoma genes Online, dubbed EHCO http://ehco.iis.sinica.edu.tw, to systematically collect, organize and compare the pileup of unsorted HCC-related studies by using natural language processing and softbots. Among the eight gene set collections, ranging across PubMed, SAGE, microarray, and proteomics data, there are 2,906 genes in total; however, more than 77% genes are only included once, suggesting that tremendous efforts need to be exerted to characterize the relationship between HCC and these genes. Of these HCC inventories, protein binding represents the largest proportion (~25%) from Gene Ontology analysis. In fact, many differentially expressed gene sets in EHCO could form interaction networks (e.g. HBV-associated HCC network) by using available human protein-protein interaction datasets. To further highlight the potential new targets in the inferred network from EHCO, we combine comparative genomics and interactomics approaches to analyze 120 evolutionary conserved and overexpressed genes in HCC. 47 out of 120 queries can form a highly interactive network with 18 queries serving as hubs. CONCLUSION: This architectural map may represent the first step toward the attempt to decipher the hepatocarcinogenesis at the systems level. Targeting hubs and/or disruption of the network formation might reveal novel strategy for HCC treatment.

MOTIVATION: The explosion of microarray studies has promised to shed light on the temporal expression patterns of thousands of genes simultaneously. However, available methods are far from adequate in efficiently extracting useful information to aid in a greater understanding of transcriptional regulatory network. Biological systems have been modeled as dynamic systems for a long history, such as genetic networks and cell regulatory network. This study evaluated if the stochastic differential equation (SDE), which is prominent for modeling dynamic diffusion process originating from the irregular Brownian motion, can be applied in modeling the transcriptional regulatory network in Saccharomyces cerevisiae. RESULTS: To model the time-continuous gene-expression datasets, a model of SDE is applied to depict irregular patterns. Our goal is to fit a generalized linear model by combining putative regulators to estimate the transcriptional pattern of a target gene. Goodness-of-fit is evaluated by log-likelihood and Akaike Information Criterion. Moreover, estimations of the contribution of regulators and inference of transcriptional pattern are implemented by statistical approaches. Our SDE model is basic but the test results agree well with the observed dynamic expression patterns. It implies that advanced SDE model might be perfectly suited to portray transcriptional regulatory networks. AVAILABILITY: The R code is available on request. CONTACT: [email protected] SUPPLEMENTARY INFORMATION: http://www.csie.ntu.edu.tw/~b89x035/yeast/

One possible path towards understanding the biological function of a target protein is through the discovery of how it interfaces within protein-protein interaction networks. The goal of this study was to create a virtual protein-protein interaction model using the concepts of orthologous conservation (or interologs) to elucidate the interacting networks of a particular target protein. POINT (the prediction of interactome database) is a functional database for the prediction of the human protein-protein interactome based on available orthologous interactome datasets. POINT integrates several publicly accessible databases, with emphasis placed on the extraction of a large quantity of mouse, fruit fly, worm and yeast protein-protein interactions datasets from the Database of Interacting Proteins (DIP), followed by conversion of them into a predicted human interactome. In addition, protein-protein interactions require both temporal synchronicity and precise spatial proximity. POINT therefore also incorporates correlated mRNA expression clusters obtained from cell cycle microarray databases and subcellular localization from Gene Ontology to further pinpoint the likelihood of biological relevance of each predicted interacting sets of protein partners.