MOTIVATION: Cancer development is a complex and heterogeneous process. It is estimated that 5-10% of human genes probably contribute to oncogenesis, whereas current experimentally validated cancer genes only cover 1% of the human genome. Thus hundreds of cancer genes may still remain to be identified. To search for new genes that play roles in carcinogenesis and facilitate cancer research, we developed a systematic workflow to use information saved in a previously established tumor-associated gene (TAG) database. RESULTS: By exploiting the information of conserved protein domains from the TAG, we identified 183 potential new TAGs. As a proof-of-concept, one predicted oncogene, fyn-related kinase (FRK), which shows an aberrant digital expression pattern in liver cancer cells, was selected for further investigation. Using 68 paired hepatocellular carcinoma samples, we found that FRK was up-regulated in 52% of cases (P < 0.001). Tumorigenic assays performed in Hep3B and HepG2 cell lines revealed a significant correlation between the level of FRK expression and invasiveness, suggesting that FRK is a positive regulator of invasiveness in liver cancer cells. CONCLUSION: These findings implied that FRK is a multitalented signal transduction molecule that produces diverse biological responses in different cell types in various microenvironments. In addition, our data demonstrated the accuracy of computational prediction and suggested that other predicted TAGs can be potential targets for future cancer research. AVAILABILITY: The TAG database is available online at the Bioinformatics Center website: http://www.binfo.ncku.edu.tw/TAG/.

MOTIVATION: Transcriptional regulatory networks, which consist of linkages between transcription factors (TF) and target genes (TGene), control the expression of a genome and play important roles in all aspects of an organism's life cycle. Accurate prediction of transcriptional regulatory networks is critical in providing useful information for biologists to determine what to do next. Currently, there is a substantial amount of fragmented gene regulation information described in the medical literature. However, current related text analysis methods designed to identify protein-protein interactions are not entirely suitable for finding transcriptional regulatory networks. RESULT: In this article, we propose an automatic regulatory network inference method that uses bootstrapping of description patterns to predict the relationship between a TF and its TGenes. The proposed method differs from other regulatory network generators in that it makes use of both positive and negative patterns for different vector combinations in a sentence. Moreover, the positive pattern learning process can be fully automatic. Furthermore, patterns for active and passive voice sentences are learned separately. The experiments use 609 HIF-1 expert-tagged articles from PubMed as the gold standard. The results show that the proposed method can automatically generate a predicted regulatory network for a transcription factor. Our system achieves an F-measure of 72.60%. AVAILABILITY: The software, training/test datasets and learned patterns are available at http://140.116.99.138/∼hcw0901/PubMedSearch.php.