Showing papers by "Dennis P. Wall published in 2014"
Harvard University1, Yale University2, Broad Institute3, Baylor College of Medicine4, Beth Israel Deaconess Medical Center5, Wellcome Trust Sanger Institute6, Icahn School of Medicine at Mount Sinai7, University of Texas Health Science Center at Houston8, University of Illinois at Chicago9, University of Pennsylvania10, Vanderbilt University11, University of Pittsburgh12, Carnegie Mellon University13
TL;DR: This model is used to identify ∼1,000 genes that are significantly lacking in functional coding variation in non-ASD samples and are enriched for de novo loss-of-function mutations identified in ASD cases, suggesting that the role of de noVO mutations in ASDs might reside in fundamental neurodevelopmental processes.
Abstract: Mark Daly and colleagues present a statistical framework to evaluate the role of de novo mutations in human disease by calibrating a model of de novo mutation rates at the individual gene level. The mutation probabilities defined by their model and list of constrained genes can be used to help identify genetic variants that have a significant role in disease.
952 citations
TL;DR: The accuracy of the observation-based classifier (OBC) is demonstrated and reductions in the process of detecting and monitoring autism are suggested to be possible.
Abstract: Current approaches for diagnosing autism have high diagnostic validity but are time consuming and can contribute to delays in arriving at an official diagnosis. In a pilot study, we used machine learning to derive a classifier that represented a 72% reduction in length from the gold-standard Autism Diagnostic Observation Schedule-Generic (ADOS-G), while retaining >97% statistical accuracy. The pilot study focused on a relatively small sample of children with and without autism. The present study sought to further test the accuracy of the classifier (termed the observation-based classifier (OBC)) on an independent sample of 2616 children scored using ADOS from five data repositories and including both spectrum (n=2333) and non-spectrum (n=283) individuals. We tested OBC outcomes against the outcomes provided by the original and current ADOS algorithms, the best estimate clinical diagnosis, and the comparison score severity metric associated with ADOS-2. The OBC was significantly correlated with the ADOS-G (r=−0.814) and ADOS-2 (r=−0.779) and exhibited >97% sensitivity and >77% specificity in comparison to both ADOS algorithm scores. The correspondence to the best estimate clinical diagnosis was also high (accuracy=96.8%), with sensitivity of 97.1% and specificity of 83.3%. The correlation between the OBC score and the comparison score was significant (r=−0.628), suggesting that the OBC provides both a classification as well as a measure of severity of the phenotype. These results further demonstrate the accuracy of the OBC and suggest that reductions in the process of detecting and monitoring autism are possible.
81 citations
TL;DR: The results indicate that it is possible to achieve high classification accuracy, sensitivity, and specificity as well as clinically acceptable inter-rater reliability with nonclinical personnel and further suggests that at least a percentage of the effort associated with detection and monitoring of autism may be mobilized and moved outside of traditional clinical environments.
Abstract: Autism is on the rise, with 1 in 88 children receiving a diagnosis in the United States, yet the process for diagnosis remains cumbersome and time consuming. Research has shown that home videos of children can help increase the accuracy of diagnosis. However the use of videos in the diagnostic process is uncommon. In the present study, we assessed the feasibility of applying a gold-standard diagnostic instrument to brief and unstructured home videos and tested whether video analysis can enable more rapid detection of the core features of autism outside of clinical environments. We collected 100 public videos from YouTube of children ages 1–15 with either a self-reported diagnosis of an ASD (N = 45) or not (N = 55). Four non-clinical raters independently scored all videos using one of the most widely adopted tools for behavioral diagnosis of autism, the Autism Diagnostic Observation Schedule-Generic (ADOS). The classification accuracy was 96.8%, with 94.1% sensitivity and 100% specificity, the inter-rater correlation for the behavioral domains on the ADOS was 0.88, and the diagnoses matched a trained clinician in all but 3 of 22 randomly selected video cases. Despite the diversity of videos and non-clinical raters, our results indicate that it is possible to achieve high classification accuracy, sensitivity, and specificity as well as clinically acceptable inter-rater reliability with nonclinical personnel. Our results also demonstrate the potential for video-based detection of autism in short, unstructured home videos and further suggests that at least a percentage of the effort associated with detection and monitoring of autism may be mobilized and moved outside of traditional clinical environments.
63 citations
TL;DR: COSMOS, a Python library for workflow management that allows formal description of pipelines and partitioning of jobs that includes a user interface for tracking the progress of jobs, abstraction of the queuing system and fine-grained control over the workflow.
Abstract: Summary: Efficient workflows to shepherd clinically generated genomic data through the multiple stages of a next-generation sequencing pipeline are of critical importance in translational biomedical science. Here we present COSMOS, a Python library for workflow management that allows formal description of pipelines and partitioning of jobs. In addition, it includes a user interface for tracking the progress of jobs, abstraction of the queuing system and fine-grained control over the workflow. Workflows can be created on traditional computing clusters as well as cloud-based services. Availability and implementation: Source code is available for academic non-commercial research purposes. Links to code and documentation are provided at http://lpm.hms.harvard.edu and http://
32 citations
TL;DR: A novel publication search tool to find target articles, specifically focused on links between disorders and genotypes, that can search through the entirety of PubMed to extract the main gene findings for human diseases rapidly and accurately is implemented.
17 citations
01 Nov 2014
TL;DR: The mission of PSB is to provide a forum for the best emerging science in Biocomputing, providing both formal and informal mechanisms for scientific communication—with an emphasis on work in the pacific rim.
Abstract: 2015 marks the 20th Pacific Symposium on Biocomputing (PSB)! PSB was founded by Larry Hunter and Teri Klein, who had previously organized a Biocomputing session at HICSS (Hawaii International Conference on Systems Sciences) which grew too big for its host conference. They decided to split the Biocomputing track and created PSB—in the early years they recruited Russ Altman and Keith Dunker as co-organizers, and were pleased to add Marylyn Ritchie more recently. The mission of PSB is to provide a forum for the best emerging science in Biocomputing, providing both formal and informal mechanisms for scientific communication—with an emphasis on work in the pacific rim. In addition to being published by World Scientific and indexed in PubMED, the proceedings from all PSB meetings are available online at http://psb.stanford.edu/psb-online/. PSB has published more than 800 papers. These papers are often cited in journal articles that emerge early in the growth of a new subfield, and many papers have achieved hundreds of citations. The Twitter handle PSB 2015 is @PacSymBiocomp and the hashtag this year will be #psb15.