Search and visualization of gene-drug-disease interactions for pharmacogenomics and precision medicine research using GeneDive.

BACKGROUND: Understanding the relationships between genes, drugs, and disease states is at the core of pharmacogenomics. Two leading approaches for identifying these relationships in medical literature are: human expert led manual curation efforts, and modern data mining based automated approaches. The former generates small amounts of high-quality data, and the latter offers large volumes of mixed quality data. The algorithmically extracted relationships are often accompanied by supporting evidence, such as, confidence scores, source articles, and surrounding contexts (excerpts) from the articles, that can be used as data quality indicators. Tools that can leverage these quality indicators to help the user gain access to larger and high-quality data are needed. APPROACH: We introduce GeneDive, a web application for pharmacogenomics researchers and precision medicine practitioners that makes gene, disease, and drug interactions data easily accessible and usable. GeneDive is designed to meet three key objectives: (1) provide functionality to manage information-overload problem and facilitate easy assimilation of supporting evidence, (2) support longitudinal and exploratory research investigations, and (3) offer integration of user-provided interactions data without requiring data sharing. RESULTS: GeneDive offers multiple search modalities, visualizations, and other features that guide the user efficiently to the information of their interest. To facilitate exploratory research, GeneDive makes the supporting evidence and context for each interaction readily available and allows the data quality threshold to be controlled by the user as per their risk tolerance level. The interactive search-visualization loop enables relationship discoveries between diseases, genes, and drugs that might not be explicitly described in literature but are emergent from the source medical corpus and deductive reasoning. The ability to utilize user's data either in combination with the GeneDive native datasets or in isolation promotes richer data-driven exploration and discovery. These functionalities along with GeneDive's applicability for precision medicine, bringing the knowledge contained in biomedical literature to bear on particular clinical situations and improving patient care, are illustrated through detailed use cases. CONCLUSION: GeneDive is a comprehensive, broad-use biological interactions browser. The GeneDive application and information about its underlying system architecture are available at http://www.genedive.net. GeneDive Docker image is also available for download at this URL, allowing users to (1) import their own interaction data securely and privately; and (2) generate and test hypotheses across their own and other datasets.

The P300 component in patients with Alzheimer's disease and their biological children.

OBJECTIVE: There are few studies examining P300 in the biological children of patients with Alzheimer's disease (AD). In addition to examining P300 in patients with AD, the current study examined the utility of P300 as a preclinical marker in the offspring of AD patients. METHODS: P300 was elicited from an AD group, their biological children, and two age- and gender-matched control groups using the auditory oddball paradigm. Each group consisted of 20 subjects each. ERPs recorded from sites Fz, Cz, and Pz were analysed using analysis of variance. RESULTS: Amplitudes were significantly smaller in the AD group when compared to controls. Both amplitude and latency values in the FH+ group were significantly impaired when compared to its control group. CONCLUSION: These findings replicate previous P300 amplitude abnormalities found in patients with AD. Further, participants with a family history of AD demonstrate possible preclinical evidence at the electrophysiological level. Comparisons with other findings and theoretical implications are discussed.

Sensory gating in patients with Alzheimer's disease and their biological children.

Research has shown that sensory gating is largely modualted by acetylcholine. Diminished levels of acetylcholine and sensory gating deficits have been reported in research involving Alzheimer's disease (AD) patients. However, there has been little investigation into those with a family history (FH+) of AD. The rationale of this study was to determine whether sensory gating impairments could distinguish those with early AD from individuals with increased risk for the disease while replicating previous findings of gating abnormalities in AD patients. Using the paried-click paradigm, evoked potentials were recorded from 4 groups of 20 subjects per group (AD, older controls, FH+, FH-). The results showed that while the AD group demonstrated sensory gating abnormalities, the FH+ group did not when compared to their peers with no family history of the disease (FH-). These results are discussed in relation to previous findings reporting P300 abnormalities in the FH+ group.

Performance of forensic and non-forensic adult psychiatric inpatients on the Test of Memory Malingering.

This study compared performance on the Test of Memory Malingering (TOMM [Tombaugh, T. N. (1996). Test of Memory Malingering (TOMM). New York: Multi Health Systems]) between a Forensic Psychiatric group and a Non-forensic Psychiatric group of 20 men each. It was hypothesized that the Forensic group would perform less well on the TOMM due to greater secondary gain for that population. The Forensic group (age, M=32.65 years; 16/20 were minorities) was composed of inpatients from a forensic psychiatric facility who had been referred for pre-trial evaluations. The Psychiatric group (age, M=41.00 years; 15/20 were Caucasian) were chosen from an inpatient psychiatric facility and had no pending legal involvement. As hypothesized, the Psychiatric group performed significantly better than the Forensic group on all TOMM trials. A TOMM score of below 45 on Trial 2 or the Retention Trial is consistent with probable response bias. Only one member of the Psychiatric group (the same individual) met this criterion, whereas seven members of the Forensic group met this criterion. The TOMM identified patients with pending legal charges as more likely to exert less effort than those with no obvious secondary gain.

GECKO: a complete large-scale gene expression analysis platform.

BACKGROUND: Gecko (Gene Expression: Computation and Knowledge Organization) is a complete, high-capacity centralized gene expression analysis system, developed in response to the needs of a distributed user community. RESULTS: Based on a client-server architecture, with a centralized repository of typically many tens of thousands of Affymetrix scans, Gecko includes automatic processing pipelines for uploading data from remote sites, a data base, a computational engine implementing approximately 50 different analysis tools, and a client application. Among available analysis tools are clustering methods, principal component analysis, supervised classification including feature selection and cross-validation, multi-factorial ANOVA, statistical contrast calculations, and various post-processing tools for extracting data at given error rates or significance levels. On account of its open architecture, Gecko also allows for the integration of new algorithms. The Gecko framework is very general: non-Affymetrix and non-gene expression data can be analyzed as well. A unique feature of the Gecko architecture is the concept of the Analysis Tree (actually, a directed acyclic graph), in which all successive results in ongoing analyses are saved. This approach has proven invaluable in allowing a large (approximately 100 users) and distributed community to share results, and to repeatedly return over a span of years to older and potentially very complex analyses of gene expression data. CONCLUSIONS: The Gecko system is being made publicly available as free software http://sourceforge.net/projects/geckoe. In totality or in parts, the Gecko framework should prove useful to users and system developers with a broad range of analysis needs.