D
Dennis P. Wall
Researcher at Stanford University
Publications - 187
Citations - 8592
Dennis P. Wall is an academic researcher from Stanford University. The author has contributed to research in topics: Autism & Autism spectrum disorder. The author has an hindex of 43, co-authored 158 publications receiving 6789 citations. Previous affiliations of Dennis P. Wall include Harvard University & Beth Israel Deaconess Medical Center.
Papers
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Journal ArticleDOI
Use of the nuclear gene glyceraldehyde 3-phosphate dehydrogenase for phylogeny reconstruction of recently diverged lineages in Mitthyridium (Musci: Calymperaceae).
TL;DR: GPd was found to hold great promise not only for improving resolution of chloroplast-derived phylogenies, but also for phylogenetic reconstruction of recent, diversifying lineages.
Journal ArticleDOI
COSMOS: Python library for massively parallel workflows
Erik Gafni,Lovelace J. Luquette,Alex K. Lancaster,Jared B. Hawkins,Jae-Yoon Jung,Yassine Souilmi,Dennis P. Wall,Peter J. Tonellato +7 more
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.
Patent
Enhancing diagnosis of disorder through artificial intelligence and mobile health technologies without compromising accuracy
TL;DR: In this article, a computer system for generating a diagnostic tool by applying artificial intelligence to an instrument for diagnosis of a disorder, such as autism, is described, where the instrument can be a caregiver-directed set of questions designed for an autism classification tool or an observation of the subject in a video, video conference, or in person and associated sets of questions about behavior that are designed for use in a separate autism classifier.
Journal ArticleDOI
Collaborative text-annotation resource for disease-centered relation extraction from biomedical text
TL;DR: This work has developed an annotation schema and an annotation tool which can be widely adopted so that the resulting annotated corpora from a multitude of disease studies could be assembled into a unified benchmark dataset.
Proceedings ArticleDOI
A practical approach to real-time neutral feature subtraction for facial expression recognition
TL;DR: A simple, real-time method which is robust to class imbalance and in principal works over a wide class of feature choices is proposed, which is tested on feature extraction techniques that lead to high baseline accuracy without neutral subtraction.