John Santerre
University of Chicago
17 Papers
28 Citations
John Santerre is an academic researcher from University of Chicago. The author has contributed to research in topics: Internal medicine & Gesture recognition. The author has an hindex of 4, co-authored 14 publications. Previous affiliations of John Santerre include Southern Methodist University.
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Papers
Antimicrobial Resistance Prediction in PATRIC and RAST.
James J. Davis,Sébastien Boisvert,Thomas Brettin,Thomas Brettin,Ronald W. Kenyon,Chunhong Mao,Robert Olson,Robert Olson,Ross Overbeek,John Santerre,Maulik Shukla,Maulik Shukla,Alice R. Wattam,Rebecca Will,Fangfang Xia,Fangfang Xia,Rick Stevens,Rick Stevens +17 more
TL;DR: The PATRIC FTP server is updated to enable access to genomes that are binned by their AMR phenotypes, as well as metadata including minimum inhibitory concentrations, to provide an initial framework for species-specific AMR phenotype and genomic feature prediction in the RAST and PATRIC annotation services.
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PATRIC as a unique resource for studying antimicrobial resistance
Dionysios A. Antonopoulos,Rida Assaf,Ramy K. Aziz,Thomas Brettin,Christopher Bun,Neal Conrad,James J. Davis,Emily M. Dietrich,Terry Disz,Svetlana Gerdes,Ronald W. Kenyon,Dustin Machi,Chunhong Mao,Daniel E. Murphy-Olson,Eric K. Nordberg,Gary J. Olsen,Robert Olson,Ross Overbeek,Bruce Parrello,Gordon D. Pusch,John Santerre,Maulik Shukla,Rick Stevens,Margo VanOeffelen,Veronika Vonstein,Andrew S. Warren,Alice R. Wattam,Fangfang Xia,Hyunseung Yoo +28 more
TL;DR: This work has undertaken a large AMR protein annotation effort, which contains many protein annotations (functional roles) that are unique to PATRIC and RAST and has been manually curated so that it projects stably across genomes.
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Machine Learning for Antimicrobial Resistance
TL;DR: A work in progress case study performing analysis on antimicrobial resistance (AMR) using standard ensemble machine learning techniques and note the successes and pitfalls such work entails.
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sEMG Gesture Recognition with a Simple Model of Attention.
TL;DR: In this paper, a simple and novel attention-based approach was proposed for surface electromyography (sEMG) signal classification, which achieved state-of-the-art results on multiple industry-standard datasets including 53 finger, wrist and grasping motions.
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Forecasting Power Consumption in Pennsylvania During the COVID-19 Pandemic: A SARIMAX Model with External COVID-19 and Unemployment Variables
Jackson Au,Javier Saldaña Jr.,Ben Spanswick,John Santerre +3 more
- 01 Jan 2020
TL;DR: In this paper, the authors analyzed electrical power consumption provided by PPL Electric Utilities, Department of Labor's unemployment claims, and the COVID-19 cases/deaths for the State of Pennsylvania to study the impact of the pandemic on the infrastructure.