Elana P. Simon
Harvard University
7 Papers
45 Citations
Elana P. Simon is an academic researcher from Harvard University. The author has contributed to research in topics: Fibrolamellar hepatocellular carcinoma & Fusion protein. The author has an hindex of 6, co-authored 6 publications. Previous affiliations of Elana P. Simon include Rockefeller University.
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Papers
Detection of a Recurrent DNAJB1-PRKACA Chimeric Transcript in Fibrolamellar Hepatocellular Carcinoma
Joshua N. Honeyman,Joshua N. Honeyman,Elana P. Simon,Nicolas Robine,Rachel Chiaroni-Clarke,David G. Darcy,David G. Darcy,Irene Isabel P. Lim,Irene Isabel P. Lim,Caroline E. Gleason,Jennifer M. Murphy,Jennifer M. Murphy,Brad R. Rosenberg,Lydia Teegan,Constantin N. Takacs,Sergio Botero,R.L. Belote,Soren Germer,Anne-Katrin Emde,Vladimir Vacic,Umesh Bhanot,Michael P. LaQuaglia,Sanford M. Simon +22 more
TL;DR: Evidence supporting the presence of the DNAJB1-PRKACA chimeric transcript in 100% of the FL-HCCs examined suggests that this genetic alteration contributes to tumor pathogenesis.
Protein design and variant prediction using autoregressive generative models
Jung-Eun Shin,Adam J. Riesselman,Aaron W. Kollasch,Conor McMahon,Elana P. Simon,Chris Sander,Aashish Manglik,Andrew C. Kruse,Debora S. Marks,Debora S. Marks +9 more
TL;DR: In this article, a deep generative model adapted from natural language processing for prediction and design of diverse functional sequences without the need for alignments is proposed, which performs state-of-the-art prediction of missense and indel effects and successfully design and test a diverse 105-nanobody library.
ChemBERTa-2: Towards Chemical Foundation Models
Walid Ahmad,Elana P. Simon,Seyone Chithrananda,Gabriel Grand,Bharath Ramsundar +4 more
TL;DR: This work builds upon ChemBERTa by optimizing the pretraining process and compares multi-task and self-supervised pretraining by varying hyperparameters and pretraining dataset size, up to 77M compounds from PubChem.
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Accelerating Protein Design Using Autoregressive Generative Models
Adam J. Riesselman,Jung-Eun Shin,Aaron W. Kollasch,Conor McMahon,Elana P. Simon,Chris Sander,Aashish Manglik,Andrew C. Kruse,Debora S. Marks,Debora S. Marks +9 more
TL;DR: This work borrows from recent advances in natural language processing and speech synthesis to develop a generative deep neural network-powered autoregressive model for biological sequences that captures functional constraints without relying on an explicit alignment structure.
Protein Design and Variant Prediction Using Autoregressive Generative Models
Jung-Eun Shin,Adam J. Riesselman,Aaron W. Kollasch,Conor McMahon,Elana P. Simon,Chris Sander,Aashish Manglik,Andrew C. Kruse,Debora S. Marks,Debora S. Marks +9 more
TL;DR: In this article, a deep generative model adapted from natural language processing for prediction and design of diverse functional sequences without the need for alignments is proposed, which performs state-of-the-art prediction of missense and indel effects.