Author

Daniel Peñaherrera

University of Pittsburgh

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 Genetic & Genomic Medicine· 1 Synthetic Biology· 1

Signature Assays, Organisms & Methods

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Organism:human· 2 Assay:protein sequence· 2 Assay:bulk RNA-seq· 1 Research Focus:clustering· 1 Approach:deep learning· 1 Research Focus:differential expression· 1 Approach:machine learning (classical)· 1 Assay:protein structure· 1

Frequent Co-Authors

Adrienne HellandiD· 1 shared Ahmet A. Kurdoglu· 1 shared Ajai ChariiD· 1 shared Alex Blanski· 1 shared Andrea Donnelly· 1 shared Andrzej J. JakubowiakiD· 1 shared Austin W. Christofferson· 1 shared Barbara Zaugg· 1 shared

Activity by Year

2020-2023 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 45 mutual co-author links

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1 shared paperGenetic & Genomic Medicine

Adrienne HellandORCID

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Author profile →

3 papers

Recent·Most cited

Structure-Infused Protein Language Models

Daniel Peñaherrera, David Ryan Koes

This paper shows that incorporating protein structure information into protein language models improves their performance on structure-related tasks without requiring structure access during inference.

bioRxiv · Dec 13, 2023 · PreprintSynthetic Biology

Genomic Basis of Multiple Myeloma Subtypes from the MMRF CoMMpass Study

Sheri Skerget, Daniel Peñaherrera, Ajai Chari, Sundar Jagannath, David Samuel DiCapua Siegel, Ravi Vij, Gregory Joshua Orloff, Andrzej J. Jakubowiak, Rubén Niesvizky, Darla Liles, Jesús G. Berdeja, Moshe Yair Levy, Jeffrey Lee Wolf, Saad Z. Usmani, The MMRF CoMMpass Network, , Jonathan J. Keats

The paper identifies genomic subtypes of multiple myeloma using the MMRF CoMMpass study and finds that 25.5% of patients transition to a high-risk expression subtype at progression, which expresses immunotherapy targets.

medRxiv · Aug 2, 2021 · PreprintGenetic & Genomic Medicine

Rhapsody: Pathogenicity prediction of human missense variants based on protein sequence, structure and dynamics

Luca Ponzoni, Daniel Peñaherrera, Zoltán N. Oltvai, İvet Bahar

The paper introduces Rhapsody, a tool that predicts the pathogenicity of human missense variants by integrating sequence, structure, and dynamics data, outperforming existing methods.

Bioinformatics · Feb 21, 2020Peer-reviewedBioinformatics