Author

Sanjay S. Dhall

San Francisco General HospitalORCID 0000-0002-6891-2722Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Neuroscience· 2 Bioinformatics· 1

Signature Assays, Organisms & Methods

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Organism:human· 3 Research Focus:differential expression· 2 Approach:statistical model· 2 Research Focus:benchmarking· 1 Assay:bulk RNA-seq· 1 Approach:machine learning (classical)· 1 Organism:multiple species· 1 Assay:proteomics· 1

Frequent Co-Authors

Abel Torres EspiniD· 2 shared Adam R. FergusoniD· 2 shared Austin ChouiD· 2 shared Jacqueline C. BresnahaniD· 2 shared John Russell HuieiD· 2 shared Jonathan Z. PaniD· 2 shared Lisa U. PascualiD· 2 shared Michael S. BeattieiD· 2 shared

Activity by Year

2020-2022 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 45 mutual co-author links

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2 shared papersBioinformatics

Abel Torres EspinORCID

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Author profile →

3 papers

Recent·Most cited

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

Proteomic portraits reveal evolutionarily conserved and divergent responses to spinal cord injury

Michael A. Skinnider, Jason C. Rogalski, Seth Tigchelaar, Neda Manouchehri, Anna Prudova, Angela Jackson, Karina Nielsen, Jaihyun Jeong, Shalini Chaudhary, Katelyn Shortt, Ylonna Gallagher-Kurtzke, Kitty So, Allan Fong, Rishab Gupta, Elena B. Okon, , Brian K. Kwon

The paper identifies protein biomarkers for spinal cord injury severity and recovery using proteomic analysis of human and porcine samples, showing high predictive accuracy.

Molecular & Cellular Proteomics · Jan 1, 2021Peer-reviewedNeuroscience

Blood RNA Profiles are Diagnostic for Severity in Human Acute Spinal Cord Injury

Nikos Kyritsis, Abel Torres Espin, Patrick G. Schupp, John Russell Huie, Austin Chou, Xuan Duong Fernandez, Leigh H. Thomas, Rachel E. Tsolinas, Debra D. Hemmerle, Lisa U. Pascual, Vineeta Singh, Jonathan Z. Pan, Jason F. Talbott, William D. Whetstone, John F. Burke, , Michael S. Beattie

The paper identifies 197 genes in peripheral blood cells whose expression correlates with acute spinal cord injury severity, achieving 72.7% accuracy in predicting injury grades.

The Journal of Experimental Medicine · Dec 23, 2020Peer-reviewedNeuroscience