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1,390 claims from 864 papers are on the record. 46 have been checked so far; the other 1,344 have no check with a result yet.

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Status: Unchecked ×Subfield: Genetics ×Clear all

5 claims from 2 papers

1. Biochemistry, Genetics and Molecular Biology › Genomics and Rare Diseases

   Accurate proteome-wide missense variant effect prediction with AlphaMissense

   Cheng, Novati, Pan et al. · Science · 2023

   Unchecked3 claims
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   1. Unchecked“By combining structural context and evolutionary conservation, our model achieves state-of-the-art results across a wide range of genetic and experimental benchmarks, all without explicitly training on such data.”
   2. Unchecked“The average pathogenicity score of genes is also predictive for their cell essentiality, capable of identifying short essential genes that existing statistical approaches are underpowered to detect.”
   3. Unchecked“As a resource to the community, we provide a database of predictions for all possible human single amino acid substitutions and classify 89% of missense variants as either likely benign or likely pathogenic.”

2. Biochemistry, Genetics and Molecular Biology › Evolution and Genetic Dynamics

   Informed training set design enables efficient machine learning-assisted directed protein evolution

   Wittmann, Yue and Arnold · Cell Systems · 2021

   The authors tested and optimised a machine learning protocol that screens full combinatorial protein libraries in silico, and it found the best variant far more often than single-step greedy optimisation.

   Unchecked2 claims
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   1. UncheckedReducing "holes", variants with zero or very low fitness, in training data was the most important factor for machine learning-assisted directed protein evolution.“In particular, we evaluate the importance of different protein encoding strategies, training procedures, models, and training set design strategies on MLDE outcome, finding the most important consideration to be the implementation of strategies that reduce inclusion of minimally informative "holes"…”
   2. UncheckedOn one epistatic, hole-filled four-site fitness landscape, the optimised ML protocol reached the best variant up to 81 times more often than greedy optimisation.“When applied to an epistatic, hole-filled, four-site combinatorial fitness landscape, our optimized protocol achieved the global fitness maximum up to 81-fold more frequently than single-step greedy optimization.”