Grant Goldman

Molecular Biology · 2022–2023

A researcher in the Science corpus: 2 papers; trusts 5 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Molecular Biology — 40 citations into it
• Computational Biology & Bioinformatics — 22 citations into it

Whom Grant Goldman trusts overall

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

1. Dan Ofer
2. Nadav Brandes
3. Pauline Crystal Ng
4. Ivan A. Adzhubei
5. Philipp Rentzsch
6. Steven Henikoff

Papers from Grant Goldman's perspective

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

1. Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences
2. Highly accurate protein structure prediction with AlphaFold
3. Language models enable zero-shot prediction of the effects of mutations on protein function
4. The language of proteins: NLP, machine learning & protein sequences (Computational and Structural Biotechnology Journal)
5. Human Gene Mutation Database (HGMD®): 2003 update
6. SIFT: predicting amino acid changes that affect protein function
7. Quantifying and understanding the fitness effects of protein mutations: Laboratory versus nature
8. ProteinBERT: a universal deep-learning model of protein sequence and function (bioRxiv)
9. Attention is All you Need (Neural Information Processing Systems)
10. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)

Their papers

• Genome-wide prediction of disease variant effects with a deep protein language model (2023) — 348 citations
• Genome-wide prediction of disease variants with a deep protein language model (2022) — 19 citations