Peter S. Kim

Molecular Biology · 2021–2023

A researcher in the Science corpus: 3 papers; trusts 15 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Molecular Biology — 60 citations into it
• Computational Biology & Bioinformatics — 19 citations into it
• Representation & Self-Supervised Learning — 10 citations into it
• Epidemiology — 8 citations into it
• Immunology — 5 citations into it

Whom Peter S. Kim trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. S. Harrison
2. Leland McInnes
3. Tristan Bepler
4. F. Alexander Wolf

Papers from Peter S. Kim's perspective

Ordered by how strongly the researchers Peter S. Kim trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep generative models of genetic variation capture the effects of mutations
2. UniRef: comprehensive and non-redundant UniProt reference clusters
3. UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction
4. Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences
5. Dimensionality reduction for visualizing single-cell data using UMAP
6. The Pfam protein families database in 2019
7. SCANPY: large-scale single-cell gene expression data analysis
8. Evaluating Protein Transfer Learning with TAPE
9. RNA velocity of single cells (Nature)
10. Language models enable zero-shot prediction of the effects of mutations on protein function

Their papers

• Evolutionary velocity with protein language models (2021) — 151 citations
• Efficient evolution of human antibodies from general protein language models (2023) — 410 citations
• Human sperm TMEM95 binds eggs and facilitates membrane fusion (2022) — 25 citations