Plurigon: Three Dimensional Visualization and Classification of High-Dimensionality Data.

Plurigon: three dimensional visualization and classification of high-dimensionality data.

Front Physiol. 2013; 4: 190
Martin B, Chen H, Daimon CM, Chadwick W, Siddiqui S, Maudsley S

High-dimensionality data is rapidly becoming the norm for biomedical sciences and many other analytical disciplines. Not only is the collection and processing time for such data becoming problematic, but it has become increasingly difficult to form a comprehensive appreciation of high-dimensionality data. Though data analysis methods for coping with multivariate data are well-documented in technical fields such as computer science, little effort is currently being expended to condense data vectors that exist beyond the realm of physical space into an easily interpretable and aesthetic form. To address this important need, we have developed Plurigon, a data visualization and classification tool for the integration of high-dimensionality visualization algorithms with a user-friendly, interactive graphical interface. Unlike existing data visualization methods, which are focused on an ensemble of data points, Plurigon places a strong emphasis upon the visualization of a single data point and its determining characteristics. Multivariate data vectors are represented in the form of a deformed sphere with a distinct topology of hills, valleys, plateaus, peaks, and crevices. The gestalt structure of the resultant Plurigon object generates an easily-appreciable model. User interaction with the Plurigon is extensive; zoom, rotation, axial and vector display, feature extraction, and anaglyph stereoscopy are currently supported. With Plurigon and its ability to analyze high-complexity data, we hope to see a unification of biomedical and computational sciences as well as practical applications in a wide array of scientific disciplines. Increased accessibility to the analysis of high-dimensionality data may increase the number of new discoveries and breakthroughs, ranging from drug screening to disease diagnosis to medical literature mining. HubMed – drug

Towards identification of individual etiologies by resolving genomic and biological conundrums in patients with autism spectrum disorders.

Mol Syndromol. 2013 Jun; 4(5): 213-26
Poot M

Recent genomic research into autism spectrum disorders (ASD) has revealed a remarkably complex genetic architecture. Large numbers of common variants, copy number variations and single nucleotide variants have been identified, yet each of them individually afforded only a small phenotypic impact. A polygenic model in which multiple genes interact either in an additive or a synergistic way appears the most plausible for the majority of ASD patients. Based on recently identified ASD candidate genes, transgenic mouse models for neuroligins/neurorexins and genes such as Cntnap2, Cntn5, Tsc1, Tsc2, Akt3, Cyfip1, Scn1a, En2, Slc6a4, and Bckdk have been generated and studied with respect to behavioral and neuroanatomical phenotypes and sensitivity to drug treatments. From these models, a few clues for potential pharmacologic intervention emerged. The Fmr1, Shank2 and Cntn5 knockout mice exhibited alterations of glutamate receptors, which may become a target for pharmacologic modulation. Some of the phenotypes of Mecp2 knockout mice can be ameliorated by administering IGF1. In the near future, comprehensive genotyping of individual patients and siblings combined with the novel insights generated from the transgenic animal studies may provide us with personalized treatment options. Eventually, autism may indeed turn out to be a phenotypically heterogeneous group of disorders (‘autisms’) caused by combinations of changes in multiple possible candidate genes, being different in each patient and requiring for each combination of mutations a distinct, individually tailored treatment. HubMed – drug

Potential modification of the UKPDS risk engine and evaluation of macrovascular event rates in controlled clinical trials.

Diabetes Metab Syndr Obes. 2013; 6: 247-56
Yang F, Ye J, Pomerantz K, Stewart M

The aim of this study was to evaluate a modified UKPDS risk engine in order to establish a risk prediction benchmark for the general diabetes population.Data sources were summary demographic and risk factor data from the major type 2 diabetes mellitus outcomes studies, including ACCORD, ADVANCE, VADT, RECORD, PROactive, ADOPT, and BARI 2D. Patients in these studies spanned a wide spectrum of disease, from drug-naïve to insulin-dependent. Cardiovascular events/major adverse coronary events (CVE/MACE) were primary or safety end points. Overall observed rates for cardiovascular events/MACE were summarized, and the observed annualized event rates were calculated using linear approximation. Simulation studies were then conducted using original (cardiovascular history excluded) and modified (cardiovascular history included) United Kingdom Prospective Diabetes Study (UKPDS) models; the predicted event rates were then compared with the observed event rates for all studies. The consistency of the predicted rates derived from each model was then evaluated using descriptive statistics and linear regression.The original UKPDS model tended to overestimate event rates across studies. The ratio of predicted events versus observed MACE ranged from 0.9 to 2.0, with mean of 1.5 ± 0.4 and a coefficient of variation of 26% (R(2) = 0.80). However, cardiovascular risk predictions were more precise using a modified UKPDS model; the ratio of predicted versus observed MACE events ranged from 1.8 to 2.4, with a mean of 2.1 ± 0.25 and a coefficient of variation of 13% (R(2) = 0.94).A modified UKPDS model which includes adjustments for prior cardiovascular history has the potential for use as a tool for benchmarking and may be useful for predicting cardiovascular rates in clinical studies. This modification could be further evaluated, recalibrated, and validated using patient-level information derived from prospective clinical studies to yield greater predictability. HubMed – drug