Author

Evan Trop

Carnegie Mellon University

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· 2 Genomics· 2

Signature Assays, Organisms & Methods

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Research Focus:benchmarking· 3 Approach:transformer / foundation model· 2 Assay:whole-genome sequencing· 2 Research Focus:clustering· 1 Approach:deep learning· 1 Research Focus:dimensionality reduction· 1 Research Focus:gene regulatory network inference· 1 Organism:human· 1

Frequent Co-Authors

Bernardo P. de AlmeidaiD· 3 shared Thomas PierrotiD· 3 shared Hugo Dalla-Torre· 2 shared Javier Mendoza‐RevillaiD· 2 shared Karim BeguiriD· 2 shared Liam Gonzalez· 2 shared Marcin J. SkwarkiD· 2 shared Nicolás López CarranzaiD· 2 shared

Activity by Year

2024-2025 · peak 3/yr

Co-Author Collaboration Network

10 top collaborators · 44 mutual co-author links

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3 shared papersGenomics

Bernardo P. de AlmeidaORCID

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4 papers

Recent·Most cited

HEIMDALL: Disentangling tokenizer design for robust transfer in single-cell foundation models

Ellie Haber, Shahul Alam, Nicholas Ho, Renming Liu, Evan Trop, Shaoheng Liang, Muyu Yang, Spencer Krieger, Jian Ma

This paper introduces HO_SCPLOWEIMDALLC_SCPLOW, a framework to analyze tokenizer design in scFMs, showing that specific tokenization choices improve generalization under distribution shift.

bioRxiv · Nov 9, 2025 · PreprintBioinformatics

The Nucleotide Transformer: Building and Evaluating Robust Foundation Models for Human Genomics

Hugo Dalla-Torre, Liam Gonzalez, Javier Mendoza‐Revilla, Nicolás López Carranza, Adam Grzywaczewski, Francesco Oteri, Christian Dallago, Evan Trop, Bernardo P. de Almeida, Hassan Sirelkhatim, Guillaume Richard, Marcin J. Skwark, Karim Beguir, M. Lopez, Thomas Pierrot

The paper introduces the Nucleotide Transformer, a foundation model for genomics, showing it can predict molecular phenotypes from DNA sequences with transfer learning across tasks and organisms.

Nature Methods · Nov 28, 2024Peer-reviewedGenomics

Are Genomic Language Models All You Need? Exploring Genomic Language Models on Protein Downstream Tasks

Sam Boshar, Evan Trop, Bernardo P. de Almeida, Liviu Copoiu, Thomas Pierrot

This study evaluates genomic language models on protein tasks, showing they are competitive with proteomic models and benefit from joint genomic-proteomic training and optimized tokenization.

Bioinformatics · Aug 30, 2024Peer-reviewedBioinformatics

A Foundational Large Language Model for Edible Plant Genomes

Javier Mendoza‐Revilla, Evan Trop, Liam Gonzalez, Maša Roller, Hugo Dalla-Torre, Bernardo P. de Almeida, Guillaume Richard, Jonathan Caton, Nicolás López Carranza, Marcin J. Skwark, Alexandre Laterre, Karim Beguir, Thomas Pierrot, Marie Lopez

This paper introduces a large language model for plant genomics that provides state-of-the-art predictions for regulatory annotations and variant prioritization, along with a benchmark dataset for deep learning methods.

Communications Biology · Jul 9, 2024Peer-reviewedGenomics