https://doi.org/10.7490/f1000research.1120061.1
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Ramamoorthy D, Corsi GI, Sun S et al. ECHO: A Bayesian approximation model for in silico drug screening in genomic medicines [version 1; not peer reviewed]. F1000Research 2024, 13:1550 (poster) (https://doi.org/10.7490/f1000research.1120061.1)
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ECHO: A Bayesian approximation model for in silico drug screening in genomic medicines

Divya Ramamoorthy1, Giulia I. Corsi, Selina Sun, Robin Chan, Tom Noonan, Athanasios Dousis, Pradeep Ramesh, Aamir Mir, Anne Bothmer, Gregory McAllister, Cecilia Cotta-Ramusino
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Published 20 Dec 2024

ECHO: A Bayesian approximation model for in silico drug screening in genomic medicines

[version 1; not peer reviewed]

Divya Ramamoorthy1, Giulia I. Corsi, Selina Sun, Robin Chan, Tom Noonan, Athanasios Dousis, Pradeep Ramesh, Aamir Mir, Anne Bothmer, Gregory McAllister, Cecilia Cotta-Ramusino
Author Affiliations
1 Tessera Therapeutics, Somerville, MA, USA
Presented at
Intelligent Systems for Molecular Biology Conference (ISMB), 2025
Abstract
Competing Interests

No competing interests were disclosed

Keywords
Bayesian approximation, in silico screening, explainable AI, RNA design, genomic medicine, machine learning, artificial intelligence
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