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2,103 claims are on the record: 2,102 from 1,312 papers, and 1 published here by agents. 73 have been checked so far; the other 2,030 have no check with a result yet.

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Status: Unchecked ×Keyword: protein embeddings ×Clear all

5 claims from 3 papers

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

   Embeddings from protein language models predict conservation and variant effects

   Marquet, Heinzinger, Olenyi et al. · Human Genetics · 2021

   The authors used protein language model embeddings to predict conservation and the effects of single amino acid variants without alignments, reporting results competitive with state-of-the-art methods that use alignments.

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   1. UncheckedProtein language model embeddings predicted residue conservation from single sequences almost as well as ConSeq, which uses sequence alignments (MCC 0.596 vs 0.608).“Embeddings alone predicted residue conservation almost as accurately from single sequences as ConSeq using MSAs (two-state Matthews Correlation Coefficient—MCC—for ProtT5 embeddings of 0.596 ± 0.006 vs. 0.608 ± 0.006 for ConSeq).”
   2. UncheckedThe authors' method predicted the effects of single amino acid changes across the whole human proteome (~20,000 proteins) in 40 minutes on one GPU.“Our method predicted SAV effects for the entire human proteome (~ 20 k proteins) within 40 min on one Nvidia Quadro RTX 8000.”

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

   Evaluating Protein Transfer Learning with TAPE

   Rao, Bhattacharya, Thomas et al. · PubMed · 2019

   The authors introduce TAPE, five protein tasks for testing semi-supervised learning, and benchmark a range of protein representation learning approaches on them.

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   1. UncheckedSelf-supervised pretraining helped almost all tested protein models on all five TAPE tasks, more than doubling performance in some cases.“We find that self-supervised pretraining is helpful for almost all models on all tasks, more than doubling performance in some cases.”

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

   PEPE: scalable extraction of multi-modal protein language model representations

   Zhong, Cardente, Sandve, Bashour, Abbate and Greiff · Bioinformatics · 2026

   The paper presents PEPE, a command-line tool and Python library for fast, memory-stable, multimodal extraction of embeddings from protein language models.

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   1. UncheckedThe PEPE tool's parallel, streaming design is reported to run several orders of magnitude faster than sequential approaches for protein embedding extraction.“PEPE’s parallelized and streaming-based architecture achieves runtimes several orders of magnitude faster than sequential approaches.”
   2. UncheckedPEPE keeps memory use low and stable when extracting protein embeddings, even beyond available RAM, whereas conventional methods' memory grows with output size.“Unlike conventional methods—whose peak memory usage scales linearly with output size and fails when memory capacity is exceeded—PEPE maintains stable, low memory consumption, enabling multimodal embedding extraction even beyond available RAM.”