HypertenGene: extracting key hypertension genes from biomedical literature with position and automatically-generated template features

<p>Abstract</p> <p>Background</p> <p>The genetic factors leading to hypertension have been extensively studied, and large numbers of research papers have been published on the subject. One of hypertension researchers' primary research tasks is to locate key hypertension-related genes in abstracts. However, gathering such information with existing tools is not easy: (1) Searching for articles often returns far too many hits to browse through. (2) The search results do not highlight the hypertension-related genes discovered in the abstract. (3) Even though some text mining services mark up gene names in the abstract, the key genes investigated in a paper are still not distinguished from other genes. To facilitate the information gathering process for hypertension researchers, one solution would be to extract the key hypertension-related genes in each abstract. Three major tasks are involved in the construction of this system: (1) gene and hypertension named entity recognition, (2) section categorization, and (3) gene-hypertension relation extraction.</p> <p>Results</p> <p>We first compare the retrieval performance achieved by individually adding template features and position features to the baseline system. Then, the combination of both is examined. We found that using position features can almost double the original AUC score (0.8140vs.0.4936) of the baseline system. However, adding template features only results in marginal improvement (0.0197). Including both improves AUC to 0.8184, indicating that these two sets of features are complementary, and do not have overlapping effects. We then examine the performance in a different domain--diabetes, and the result shows a satisfactory AUC of 0.83.</p> <p>Conclusion</p> <p>Our approach successfully exploits template features to recognize true hypertension-related gene mentions and position features to distinguish key genes from other related genes. Templates are automatically generated and checked by biologists to minimize labor costs. Our approach integrates the advantages of machine learning models and pattern matching. To the best of our knowledge, this the first systematic study of extracting hypertension-related genes and the first attempt to create a hypertension-gene relation corpus based on the GAD database. Furthermore, our paper proposes and tests novel features for extracting key hypertension genes, such as relative position, section, and template features, which could also be applied to key-gene extraction for other diseases.</p

Sandwich-Cultured Hepatocytes as a Tool to Study Drug Disposition and Drug-Induced Liver Injury

Sandwich-cultured hepatocytes (SCH) are metabolically competent and have proper localization of basolateral and canalicular transporters with functional bile networks. Therefore, this cellular model is a unique tool that can be used to estimate biliary excretion of compounds. SCH have been used widely to assess hepatobiliary disposition of endogenous and exogenous compounds and metabolites. Mechanistic modeling based on SCH data enables estimation of metabolic and transporter-mediated clearances, which can be employed to construct physiologically-based pharmacokinetic models for prediction of drug disposition and drug-drug interactions in humans. In addition to pharmacokinetic studies, SCH also have been employed to study cytotoxicity and perturbation of biological processes by drugs and hepatically-generated metabolites. Human SCH can provide mechanistic insights underlying clinical drug-induced liver injury (DILI). In addition, data generated in SCH can be integrated into systems pharmacology models to predict potential DILI in humans. In this review, applications of SCH in studying hepatobiliary drug disposition and bile acid-mediated DILI are discussed. An example is presented to show how data generated in the SCH model was used to establish a quantitative relationship between intracellular bile acids and cytotoxicity, and how this information was incorporated into a systems pharmacology model for DILI prediction