V
Vahid Jalili
Researcher at Oregon Health & Science University
Publications - 6
Citations - 285
Vahid Jalili is an academic researcher from Oregon Health & Science University. The author has contributed to research in topics: Deep learning & Transformation (function). The author has an hindex of 2, co-authored 6 publications receiving 99 citations. Previous affiliations of Vahid Jalili include Broad Institute.
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Journal ArticleDOI
The Galaxy platform for accessible, reproducible and collaborative biomedical analyses: 2020 update.
Vahid Jalili,Enis Afgan,Qiang Gu,Dave Clements,Daniel Blankenberg,Jeremy Goecks,James Taylor,Anton Nekrutenko +7 more
TL;DR: Key advances in Galaxy's user interface include enhancements for analyzing large dataset collections as well as interactive tools for exploratory data analysis and support for federated identity and access management and increased ability to distribute analysis jobs to remote resources.
Journal ArticleDOI
Galaxy-ML: An accessible, reproducible, and scalable machine learning toolkit for biomedicine.
Qiang Gu,Anup Kumar,Simon Bray,Allison L. Creason,Alireza Khanteymoori,Vahid Jalili,Björn Grüning,Jeremy Goecks +7 more
TL;DR: Galaxy-ML as discussed by the authors is a biomedical computational workbench used by tens of thousands of scientists across the world, with a suite of tools for all aspects of supervised machine learning for biomedical data analysis.
Posted ContentDOI
Accessible, Reproducible, and Scalable Machine Learning for Biomedicine
Qiang Gu,Anup Kumar,Simon Bray,Allison L. Creason,Alireza Khanteymoori,Vahid Jalili,Björn Grüning,Jeremy Goecks +7 more
TL;DR: Galaxy-ML is a platform that enables scientists to perform end-to-end reproducible machine learning analyses at large scale using only a web browser.
Posted ContentDOI
Adding software to package management systems can increase their citation by 280
TL;DR: The results suggest that there is significant evidence that the scholarly papers’ citation count increases after their respective software was published to package management systems, and may motivate scientists to invest in disseminating their software via package management system.
Posted ContentDOI
Rescuing Biologically Relevant Consensus Regions Across Replicated Samples
TL;DR: MSPC as discussed by the authors exploits replicates to lower the threshold required to identify a binding site while keeping a low false positive rate, which can be extended to call consensus regions across any number of replicated samples, accounting for differences between biological and technical replicates.