Author

Edilberto Amorim

San Francisco General HospitalORCID 0000-0001-6972-5622Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Bioinformatics· 2

Signature Assays, Organisms & Methods

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Research Focus:benchmarking· 2 Approach:benchmark study· 1 Research Focus:database / resource· 1 Organism:human· 1 Approach:machine learning (classical)· 1 Research Focus:software / tool· 1

Frequent Co-Authors

Abel Torres EspiniD· 1 shared Adam R. FergusoniD· 1 shared Alex Handler WagneriD· 1 shared Andrew E. WilliamsiD· 1 shared Andrew Lofgreen· 1 shared Austin ChouiD· 1 shared Bhavesh J. PateliD· 1 shared Brian J. GowiD· 1 shared

Activity by Year

2022-2024 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 20 mutual co-author links

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1 shared paperBioinformatics

Abel Torres EspinORCID

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2 papers

Recent·Most cited

AI-readiness criteria for biomedical data

Timothy Clark, John Harry Caufield, Jillian A. Parker, Mohammad Sadnan Al Manir, Edilberto Amorim, James A. Eddy, Nayoon Gim, Brian J. Gow, Wesley Goar, Jan N. Hansen, Nomi Harris, Henning Hermjakob, Marcin Joachimiak, Gianna Jordan, In‐Hee Lee, , Monica C. Muñoz-Torres

The paper defines AI-readiness criteria for biomedical data, encompassing seven dimensions, and evaluates them across flagship datasets to ensure ethical and explainable AI practices.

bioRxiv · Oct 23, 2024 · PreprintBioinformatics

Expert-integrated automated machine learning uncovers hemodynamic predictors in spinal cord injury

Austin Chou, Abel Torres Espin, Nikos Kyritsis, John Russell Huie, Sarah Khatry, Jeremy Funk, Jennifer Hay, Andrew Lofgreen, Rajiv Ratn Shah, Chandler McCann, Lisa U. Pascual, Edilberto Amorim, Philip R. Weinstein, Geoffrey T Manley, Sanjay S. Dhall, , the TRACK-SCI Investigators

This paper develops an AutoML framework with explainable AI tools to identify hemodynamic predictors of spinal cord injury outcomes.

PLoS ONE · Apr 7, 2022Peer-reviewedBioinformatics