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Background: Glucocorticoids are potent anti-inflammatory agents used for the treatment of diseases such as rheumatoid arthritis, asthma, inflammatory bowel disease and psoriasis. Unfortunately, usage is limited because of metabolic sideeffects, e.g. insulin resistance, glucose intolerance and diabetes. To gain more insight into the mechanisms behind glucocorticoid induced insulin resistance, it is important to understand which genes play a role in the development of insulin resistance and which genes are affected by glucocorticoids. Medline abstracts contain many studies about insulin resistance and the molecular effects of glucocorticoids and thus are a good resource to study these effects. Results: We developed CoPubGene a method to automatically identify gene-disease associations in Medline abstracts. We used this method to create a literature network of genes related to insulin resistance and to evaluate the importance of the genes in this network for glucocorticoid induced metabolic side effects and anti-inflammatory processes. With this approach we found several genes that already are considered markers of GC induced IR, such as phosphoenolpyruvate carboxykinase (PCK) and glucose-6- phosphatase, catalytic subunit (G6PC). In addition, we found genes involved in steroid synthesis that have not yet been recognized as mediators of GC induced IR. Conclusions: With this approach we are able to construct a robust informative literature network of insulin resistance related genes that gave new insights to better understand the mechanisms behind GC induced IR. The method has been set up in a generic way so it can be applied to a wide variety of disease networks. [ABSTRACT FROM AUTHOR]

A lack of mature domain knowledge and well-established guidelines makes the medical diagnosis of skeletal dysplasias (a group of rare genetic disorders) a very complex process. Machine learning techniques can facilitate objective interpretation of medical observations for the purposes of decision support. However, building decision support models using such techniques is highly problematic in the context of rare genetic disorders, because it depends on access to mature domain knowledge. This paper describes an approach for developing a decision support model in medical domains that are underpinned by relatively sparse knowledge bases. We propose a solution that combines association rule mining with the Dempster-Shafer theory (DST) to compute probabilistic associations between sets of clinical features and disorders, which can then serve as support for medical decision making (e.g., diagnosis). We show, via experimental results, that our approach is able to provide meaningful outcomes even on small datasets with sparse distributions, in addition to outperforming other Machine Learning techniques and behaving slightly better than an initial diagnosis by a clinician. [ABSTRACT FROM AUTHOR]

Background: The incidence of congenital heart disease (CHD) is continuously increasing among infants born alive nowadays, making it one of the leading causes of infant morbidity worldwide. Various studies suggest that both genetic and environmental factors lead to CHD, and therefore identifying its candidate genes and disease-markers has been one of the central topics in CHD research. By using the high-throughput genomic data of CHD which are available recently, network-based methods provide powerful alternatives of systematic analysis of complex diseases and identification of dysfunctional modules and candidate disease genes. Results: In this paper, by modeling the information flow from source disease genes to targets of differentially expressed genes via a context-specific protein-protein interaction network, we extracted dysfunctional modules which were then validated by various types of measurements and independent datasets. Network topology analysis of these modules revealed major and auxiliary pathways and cellular processes in CHD, demonstrating the biological usefulness of the identified modules. We also prioritized a list of candidate CHD genes from these modules using a guilt-by-association approach, which are well supported by various kinds of literature and experimental evidence. Conclusions: We provided a network-based analysis to detect dysfunctional modules and disease genes of CHD by modeling the information transmission from source disease genes to targets of differentially expressed genes. Our method resulted in 12 modules from the constructed CHD subnetwork. We further identified and prioritized candidate disease genes of CHD from these dysfunctional modules. In conclusion, module analysis not only revealed several important findings with regard to the underlying molecular mechanisms of CHD, but also suggested the distinct network properties of causal disease genes which lead to identification of candidate CHD genes. [ABSTRACT FROM AUTHOR]

Hypertrophic cardiomyopathy (HCM) is a common autosomal dominant condition with an increased risk of sudden cardiac death. Although clinical genetic testing can be used for confirmation of a clinical diagnosis as well as a predictive test, based on our clinical experience it is underutilized. Therefore, we developed and administered a questionnaire to assess potential determinants of parental interest in this testing. Of the 30 adult caregivers who participated, 80% had heard of genetic testing, whereas only 30% knew about genetic testing specifically for HCM. Once informed of the availability, 62% said they would consider testing in the future and 28% would consider it in the next year. Participants’ younger age, higher education level, knowledge of carrier testing, and positive view of genetic testing were significantly associated with the participant considering HCM genetic testing for their child ( p ≤ 0.05). Based on a logistic regression model, age, education level, and knowing that HCM is an inherited disease were the best predictors of who would consider genetic testing. This study provides healthcare providers with a framework to understand caregivers’ knowledge and views of genetic testing, which can be used to improve clinical care for pediatric HCM patients. [ABSTRACT FROM AUTHOR]

Molecular techniques for identifying pathogens associated with cancer continue to be developed, including one reported recently in BMC Medical Genomics. Identifying a causal infectious agent helps in understanding the biology of these cancers and can lead ultimately to the development of antimicrobial drugs and vaccines for their treatment and prevention. [ABSTRACT FROM AUTHOR]

Focuses on the renaissance phase of electrocardiography (ECG). Other phases of the evolution of ECG; Invasive diagnostic techniques that were introduce prior to the phase; Event in the 1990s that initiated the renaissance in ECG; Genetic disorders that can be diagnosed through ECG.

The article provides information on the open online platform to facilitate the testing and comparison of genome analysis methods.

Describes research which gives the positions of over 30,000 genes along the human DNA molecule. Number of genes in known positions; Use of radiation hybrids for the human genome project; Linear order of genes established; Importance of knowing where a gene is located for studying diseases; Significance of the gene map; Causative genes in common single-gene disorders; Map a signpost in human DNA. INSET: Radiation hybrids and gene order.