Author

A. P. Amini

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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Bioengineering· 3

Signature Assays, Organisms & Methods

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Research Focus:benchmarking· 3 Assay:protein sequence· 3 Approach:deep learning· 2 Approach:benchmark study· 1 Approach:diffusion model· 1 Research Focus:protein design· 1 Research Focus:sequence-to-function modeling· 1 Approach:statistical model· 1

Frequent Co-Authors

K. K. Yang· 3 shared A. X. Lu· 2 shared A. Moses· 1 shared B. Strome· 1 shared F.-Z. Li· 1 shared K. P. Greenman· 1 shared N. Fusi· 1 shared N. Tenenholtz· 1 shared

Activity by Year

2023-2024 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 31 mutual co-author links

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3 shared papersBioengineering

K. K. Yang

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3 papers

Recent·Most cited

Feature Reuse and Scaling: Understanding Transfer Learning with Protein Language Models

F.-Z. Li, A. P. Amini, Y. Yue, K. K. Yang, A. X. Lu

This study analyzes transfer learning in protein language models, finding that downstream task performance often relies on early pretraining features rather than scaling with pretraining duration.

bioRxiv · Feb 5, 2024 · PreprintBioengineering

Protein generation with evolutionary diffusion: sequence is all you need

S. Alamdari, N. Thakkar, R. van den Berg, N. Tenenholtz, B. Strome, A. Moses, A. X. Lu, N. Fusi, A. P. Amini, K. K. Yang

This paper introduces EvoDiff, a diffusion model that generates diverse, high-fidelity proteins in sequence space, overcoming limitations of structure-based models.

bioRxiv · Sep 11, 2023 · PreprintBioengineering

Benchmarking Uncertainty Quantification for Protein Engineering

K. P. Greenman, A. P. Amini, K. K. Yang

This study benchmarks deep learning uncertainty quantification methods on protein sequence-function regression tasks to improve biological sequence design.

bioRxiv · Apr 17, 2023Bioengineering