On Wesner's method of searching for chaos on low frequency

An alternative to Wesner's method of detecting deterministic behavior and chaos in small sample sets is presented. This new method is applied to analyze the dynamics of several stock prices.

Factors related to genetic testing in adults at risk for Huntington disease: the prospective Huntington at-risk observational study (PHAROS)

Huntington disease (HD) is a late onset ultimately fatal neurodegenerative disorder caused by a cytosine-adenine-guanine ( CAG) triplet repeat expansion in the Huntingtin gene which was discovered in 1993. The PHAROS study is a unique observational study of 1001 individuals at risk for HD who had not been previously tested for HD and who had no plans to do so. In this cohort, 104 (10%) individuals changed their minds and chose to be tested during the course of the study but outside of the study protocol. Baseline behavioral scores, especially apathy, were more strongly associated with later genetic testing than motor and chorea scores, particularly among subjects with expanded CAG repeat length. In the CAG expanded group, those choosing to be tested were older and had more chorea and higher scores on the behavioral section of the unified Huntington's disease rating scale at baseline than those not choosing to be tested. Following genetic testing, 56% of subjects with CAG < 37 had less depression when compared to prior to testing, but depression generally stayed the same or increased for 64% of subjects in the expanded group. This finding suggests that approaches to testing must continue to be cautious, with appropriate medical, psychological and social support

Two Decades of Huntington Disease Testing: Patient’s Demographics and Reproductive Choices

Predictive testing for Huntington disease (HD) has been available in the United States (US) since 1987, and the Indiana University Predictive Testing Program has been providing this testing since 1990. To date there has been no published description of those who present for such testing in the US. Here we describe demographics of 141 individuals and reproductive decision making of a subset of 16 of those individuals who underwent predictive HD testing between 1990 and 2010 at one site in the US. This study is a retrospective chart review of the “Personal History Questionnaire” participants completed prior to testing. As seen in other studies, most participants were female (64.5 %), in their mid-30s (mean = 34), and had at least one child prior to testing (54 %). Multiple demographic datum points are described, and the reproductive decision making of these at-risk individuals was analyzed using Fisher’s Exact Tests. Of those women who had children before learning of their risk to inherit HD, those who attended church more frequently, had three or more children total, or whose mother was affected with HD were more likely to be comfortable with their choice to have children. We conclude that these demographic factors influence the reproductive decision-making of individuals at risk for HD. Psychologists, clinical geneticists, and genetic counselors may be able to use this information to help counsel at-risk patients regarding current or past reproductive decision making

GeneMANIA: a real-time multiple association network integration algorithm for predicting gene function

Abstract Background: Most successful computational approaches for protein function prediction integrate multiple genomics and proteomics data sources to make inferences about the function of unknown proteins. The most accurate of these algorithms have long running times, making them unsuitable for real-time protein function prediction in large genomes. As a result, the predictions of these algorithms are stored in static databases that can easily become outdated. We propose a new algorithm, GeneMANIA, that is as accurate as the leading methods, while capable of predicting protein function in real-time. Results: We use a fast heuristic algorithm, derived from ridge regression, to integrate multiple functional association networks and predict gene function from a single process-specific network using label propagation. Our algorithm is efficient enough to be deployed on a modern webserver and is as accurate as, or more so than, the leading methods on the MouseFunc I benchmark and a new yeast function prediction benchmark; it is robust to redundant and irrelevant data and requires, on average, less than ten seconds of computation time on tasks from these benchmarks. Conclusion: GeneMANIA is fast enough to predict gene function on-the-fly while achieving state-of-the-art accuracy. A prototype version of a GeneMANIA-based webserver is available at http://morrislab.med.utoronto.ca/prototype

Reconstructing evolutionary trajectories of mutation signature activities in cancer using TrackSig

The type and genomic context of cancer mutations depend on their causes. These causes have been characterized using signatures that represent mutation types that co-occur in the same tumours. However, it remains unclear how mutation processes change during cancer evolution due to the lack of reliable methods to reconstruct evolutionary trajectories of mutational signature activity. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, which aggregated whole-genome sequencing data from 2658 cancers across 38 tumour types, we present TrackSig, a new method that reconstructs these trajectories using optimal, joint segmentation and deconvolution of mutation type and allele frequencies from a single tumour sample. In simulations, we find TrackSig has a 3-5% activity reconstruction error, and 12% false detection rate. It outperforms an aggressive baseline in situations with branching evolution, CNA gain, and neutral mutations. Applied to data from 2658 tumours and 38 cancer types, TrackSig permits pan-cancer insight into evolutionary changes in mutational processes

Walking the talk for dementia: a unique immersive, embodied, and multi‐experiential initiative

Coping with dementia requires an integrated approach encompassing personal, health, research, and community domains. Here we describe “Walking the Talk for Dementia,” an immersive initiative aimed at empowering people with dementia, enhancing dementia understanding, and inspiring collaborations. This initiative involved 300 participants from 25 nationalities, including people with dementia, care partners, clinicians, policymakers, researchers, and advocates for a 4-day, 40 km walk through the Camino de Santiago de Compostela, Spain. A 2-day symposium after the journey provided novel transdisciplinary and horizontal structures, deconstructing traditional hierarchies. The innovation of this initiative lies in its ability to merge a physical experience with knowledge exchange for diversifying individuals' understanding of dementia. It showcases the transformative potential of an immersive, embodied, and multi-experiential approach to address the complexities of dementia collaboratively. The initiative offers a scalable model to enhance understanding, decrease stigma, and promote more comprehensive and empathetic dementia care and research

Neutral tumor evolution?

No abstract available

The GeneMANIA prediction server: biological network integration for gene prioritization and predicting gene function

GeneMANIA (http://www.genemania.org) is a flexible, user-friendly web interface for generating hypotheses about gene function, analyzing gene lists and prioritizing genes for functional assays. Given a query list, GeneMANIA extends the list with functionally similar genes that it identifies using available genomics and proteomics data. GeneMANIA also reports weights that indicate the predictive value of each selected data set for the query. Six organisms are currently supported (Arabidopsis thaliana, Caenorhabditis elegans, Drosophila melanogaster, Mus musculus, Homo sapiens and Saccharomyces cerevisiae) and hundreds of data sets have been collected from GEO, BioGRID, Pathway Commons and I2D, as well as organism-specific functional genomics data sets. Users can select arbitrary subsets of the data sets associated with an organism to perform their analyses and can upload their own data sets to analyze. The GeneMANIA algorithm performs as well or better than other gene function prediction methods on yeast and mouse benchmarks. The high accuracy of the GeneMANIA prediction algorithm, an intuitive user interface and large database make GeneMANIA a useful tool for any biologist

A critical assessment of Mus musculus gene function prediction using integrated genomic evidence

Background: Several years after sequencing the human genome and the mouse genome, much remains to be discovered about the functions of most human and mouse genes. Computational prediction of gene function promises to help focus limited experimental resources on the most likely hypotheses. Several algorithms using diverse genomic data have been applied to this task in model organisms; however, the performance of such approaches in mammals has not yet been evaluated. Results: In this study, a standardized collection of mouse functional genomic data was assembled; nine bioinformatics teams used this data set to independently train classifiers and generate predictions of function, as defined by Gene Ontology (GO) terms, for 21,603 mouse genes; and the best performing submissions were combined in a single set of predictions. We identified strengths and weaknesses of current functional genomic data sets and compared the performance of function prediction algorithms. This analysis inferred functions for 76% of mouse genes, including 5,000 currently uncharacterized genes. At a recall rate of 20%, a unified set of predictions averaged 41% precision, with 26% of GO terms achieving a precision better than 90%. Conclusion: We performed a systematic evaluation of diverse, independently developed computational approaches for predicting gene function from heterogeneous data sources in mammals. The results show that currently available data for mammals allows predictions with both breadth and accuracy. Importantly, many highly novel predictions emerge for the 38% of mouse genes that remain uncharacterized