Author

Rachel S. Schibler

Harvey Mudd College

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

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Bioinformatics· 1 Neuroscience· 1

Signature Assays, Organisms & Methods

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Assay:electronic health records· 1 Approach:experimental assay· 1 Approach:machine learning (classical)· 1 Assay:protein sequence· 1 Research Focus:sequence-to-function modeling· 1

Frequent Co-Authors

Adam C. SnyderiD· 1 shared Chan Hong· 1 shared Ian Kerman· 1 shared Matthew A. SmithiD· 1 shared Naim MatasciiD· 1 shared Reza SadeghiiD· 1 shared Richard JohnstoniD· 1 shared Thomas DoughertyiD· 1 shared

Activity by Year

2020-2022 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 24 mutual co-author links

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1 shared paperNeuroscience

Adam C. SnyderORCID

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2 papers

Recent·Most cited

NeuroscienceeNeuroPeer-reviewedMay 1, 2022

EEG signals index a global signature of arousal embedded in neuronal population recordings

Richard Johnston, Adam C. Snyder, Rachel S. Schibler, Matthew A. Smith

The paper links EEG signals to a global arousal signature in neuronal population recordings during a change detection task.

16 citations

electronic health recordsexperimental assay

𝕏 PostBluesky

BioinformaticsbioRxivPreprintJun 18, 2020

Predicting Antibody Developability from Sequence using Machine Learning

Xingyao Chen, Thomas Dougherty, Chan Hong…·Ian Kerman

This paper develops a machine learning pipeline to predict antibody developability from sequence, achieving high sensitivity and specificity on a dataset of 2400 antibodies.

26 citations

protein sequencemachine learning (classical)

𝕏 PostBluesky