![]() ![]() ![]() In this study, we applied a novel approach that grafts text mining and the main path analysis into Swanson`s ABC model for expanding intermediate concepts to multi-levels and extracting the most significant path. The chains can help us uncover publicly unknown knowledge that would develop as empirical studies for investigating the cause of pancreatic cancer. This study aims to infer the gene-protein `brings_about` chains of pancreatic cancer which were referred to in the pancreatic cancer related researches by constructing the gene-protein interaction network of pancreatic cancer. A particular medical application stems from the concept of synthetic lethality in cancer and how it could open up new opportunities for personalized cancer therapies. The real data examples underscore the need for developing more advanced data mining approaches for extracting the full information from the high-throughput screens. We review here the state-of-the-art statistical scoring approaches used in the prediction of drug–target interactions, and illustrate their operation using publicly available data from yeast chemical-genomic profiling studies. ![]() High-throughput screening is increasingly being used to test new drug compounds and to infer their cellular targets, but these quantitative screens result in high-dimensional datasets with many inherent sources of noise. An important prerequisite for the development of safe and effective chemical compounds is the identification of their cellular targets. The recent decrease in the rate that new cancer therapies are being translated into clinical use is mainly due to the lack of therapeutic efficacy and clinical safety or toxicology of the candidate drug compounds. ![]()
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