Ava P. Amini

1.1k total citations · 2 hit papers
12 papers, 277 citations indexed

About

Ava P. Amini is a scholar working on Molecular Biology, Artificial Intelligence and Computational Theory and Mathematics. According to data from OpenAlex, Ava P. Amini has authored 12 papers receiving a total of 277 indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Molecular Biology, 2 papers in Artificial Intelligence and 2 papers in Computational Theory and Mathematics. Recurrent topics in Ava P. Amini's work include Machine Learning in Bioinformatics (4 papers), Protein Structure and Dynamics (4 papers) and Single-cell and spatial transcriptomics (3 papers). Ava P. Amini is often cited by papers focused on Machine Learning in Bioinformatics (4 papers), Protein Structure and Dynamics (4 papers) and Single-cell and spatial transcriptomics (3 papers). Ava P. Amini collaborates with scholars based in United States, United Kingdom and China. Ava P. Amini's co-authors include Sangeeta N. Bhatia, Wilko Schwarting, Daniela Rus, Ava P. Soleimany, Kevin Yang, Alex X. Lu, Sarah Alamdari, James Zou, Rianne van den Berg and Kevin Wu and has published in prestigious journals such as Cell, Nature Communications and Bioinformatics.

In The Last Decade

Ava P. Amini

11 papers receiving 266 citations

Hit Papers

Protein structure generation via folding diffusion 2024 2026 2025 2024 2025 25 50 75
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. Protein structure generation via folding diffusion
    2024 76 citations Kevin Wu, Kevin Yang et al. Nature Communications profile →
  2. Zero-shot evaluation reveals limitations of single-cell foundation models
    2025 18 citations Lorin Crawford, Ava P. Amini et al. Genome biology profile →

Peers

Ava P. Amini
Zan Armstrong United States
R. Clyde White United Kingdom
Andrew McNutt United States
Po‐Ting Lai Taiwan
Gamze Gürsoy United States
Szymon Wąsik Poland
Hongbin Ye China
Zhuoru Li China
Shiori Sagawa United States
Kurt De Grave Belgium
Zan Armstrong United States
Ava P. Amini
Citations per year, relative to Ava P. Amini Ava P. Amini (= 1×) peers Zan Armstrong

Countries citing papers authored by Ava P. Amini

Since Specialization
Citations

This map shows the geographic impact of Ava P. Amini's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Ava P. Amini with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ava P. Amini more than expected).

Fields of papers citing papers by Ava P. Amini

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Ava P. Amini. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Ava P. Amini. The network helps show where Ava P. Amini may publish in the future.

Co-authorship network of co-authors of Ava P. Amini

This figure shows the co-authorship network connecting the top 25 collaborators of Ava P. Amini. A scholar is included among the top collaborators of Ava P. Amini based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Ava P. Amini. Ava P. Amini is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

12 of 12 papers shown
1.
Alamdari, Sarah, et al.. (2025). ProtNote: a multimodal method for protein–function annotation. Bioinformatics. 41(5). 1 indexed citations
2.
Yang, Kevin & Ava P. Amini. (2025). Simplifying protein engineering with deep learning. Cell. 188(17). 4477–4479.
3.
Greenman, Kevin P., Ava P. Amini, & Kevin Yang. (2025). Benchmarking uncertainty quantification for protein engineering. PLoS Computational Biology. 21(1). e1012639–e1012639. 3 indexed citations
4.
DenAdel, Alan, Andrew W. Navia, Alex K. Shalek, et al.. (2025). Artificial variables help to avoid over-clustering in single-cell RNA sequencing. The American Journal of Human Genetics. 112(4). 940–951. 1 indexed citations
5.
Crawford, Lorin, et al.. (2025). Zero-shot evaluation reveals limitations of single-cell foundation models. Genome biology. 26(1). 101–101. 18 indexed citations breakdown →
6.
Alamdari, Sarah, et al.. (2025). Toward deep learning sequence–structure co-generation for protein design. Current Opinion in Structural Biology. 91. 103018–103018. 3 indexed citations
7.
Wu, Kevin, Kevin Yang, Rianne van den Berg, et al.. (2024). Protein structure generation via folding diffusion. Nature Communications. 15(1). 1059–1059. 76 indexed citations breakdown →
8.
Amini, Ava P., et al.. (2024). Deeper evaluation of a single-cell foundation model. Nature Machine Intelligence. 6(12). 1443–1446. 9 indexed citations
9.
Bhattacharya, Nicholas, et al.. (2023). Deep self-supervised learning for biosynthetic gene cluster detection and product classification. PLoS Computational Biology. 19(5). e1011162–e1011162. 25 indexed citations
10.
Amini, Ava P., Jesse D. Kirkpatrick, Cathy S. Wang, et al.. (2022). Multiscale profiling of protease activity in cancer. Nature Communications. 13(1). 5745–5745. 32 indexed citations
11.
Amini, Ava P., Jesse D. Kirkpatrick, Cathy S. Wang, et al.. (2022). Multiscale profiling of protease activity in cancer. Zenodo (CERN European Organization for Nuclear Research). 1 indexed citations
12.
Amini, Ava P., Ava P. Soleimany, Wilko Schwarting, Sangeeta N. Bhatia, & Daniela Rus. (2019). Uncovering and Mitigating Algorithmic Bias through Learned Latent Structure. DSpace@MIT (Massachusetts Institute of Technology). 289–295. 108 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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