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1,280 claims from 801 papers are on the record. 46 have been checked so far; the other 1,234 have no check with a result yet.

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Status: Unchecked ×Keyword: remote homology ×Clear all

7 claims from 3 papers

1. Biochemistry, Genetics and Molecular Biology › Machine Learning in Bioinformatics

   Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences

   Rives, Meier, Sercu et al. · Proceedings of the National Academy of Sciences · 2021

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   1. Unchecked“We find that without prior knowledge, information emerges in the learned representations on fundamental properties of proteins such as secondary structure, contacts, and biological activity.”
   2. Unchecked“Unsupervised representation learning enables state-of-the-art supervised prediction of mutational effect and secondary structure and improves state-of-the-art features for long-range contact prediction.”

2. Biochemistry, Genetics and Molecular Biology › Machine Learning in Bioinformatics

   Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences

   Rives, Meier, Sercu et al. · bioRxiv (Cold Spring Harbor Laboratory) · 2019

   A deep language model trained without labels on 250 million protein sequences learns representations that encode biological properties, structure and function, and support state-of-the-art predictions.

   Unchecked2 claims
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   1. UncheckedA protein language model's internal representations are organised across scales, from amino acid chemistry up to distant evolutionary relationships between proteins.“The learned representation space has a multi-scale organization reflecting structure from the level of biochemical properties of amino acids to remote homology of proteins.”
   2. Unchecked“Information about secondary and tertiary structure is encoded in the representations and can be identified by linear projections.”

3. Biochemistry, Genetics and Molecular Biology › Protein Structure and Dynamics

   Clustering predicted structures at the scale of the known protein universe

   Barrio‐Hernandez, Yeo, Jänes et al. · bioRxiv (Cold Spring Harbor Laboratory) · 2023

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   1. Unchecked“Using this method we have clustered all structures in AFDB, identifying 2.27M non-singleton structural clusters, of which 31% lack annotations representing likely novel structures.”
   2. Unchecked“Clusters without annotation tend to have few representatives covering only 4% of all proteins in the AFDB.”
   3. Unchecked“Evolutionary analysis suggests that most clusters are ancient in origin but 4% seem species specific, representing lower quality predictions or examples of de-novo gene birth.”