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1,144 claims from 719 papers are on the record. 42 have been checked so far; the other 1,102 have no check with a result yet.

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Topic: Machine Learning in Bioinformatics ×Clear all

16 claims from 10 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

   ProtTrans: Toward Understanding the Language of Life Through Self-Supervised Learning

   Elnaggar, Heinzinger, Dallago et al. · IEEE Transactions on Pattern Analysis and Machine Intelligence · 2021

   The authors trained six language models on huge protein sequence sets and showed their embeddings, used alone, could predict protein structure and location, with the best beating methods that need sequence alignments.

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   1. UncheckedSimplifying protein language model embeddings from unlabelled sequences showed they captured some biophysical features of proteins.“Dimensionality reduction revealed that the raw pLM-embeddings from unlabeled data captured some biophysical features of protein sequences.”
   2. UncheckedProtein language model embeddings alone, used as input, predicted secondary structure, cell location and membrane status with the stated accuracies.“We validated the advantage of using the embeddings as exclusive input for several subsequent tasks: (1) a per-residue (per-token) prediction of protein secondary structure (3-state accuracy Q3=81%-87%); (2) per-protein (pooling) predictions of protein sub-cellular location (ten-state accuracy: Q10=…”
   3. Unchecked“For secondary structure, the most informative embeddings (ProtT5) for the first time outperformed the state-of-the-art without multiple sequence alignments (MSAs) or evolutionary information thereby bypassing expensive database searches.”

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

   DeepLoc 2.0: multi-label subcellular localization prediction using protein language models

   Thumuluri, Armenteros, Johansen, Nielsen and Winther · Nucleic Acids Research · 2022

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   1. Unchecked“We achieve state-of-the-art performance in DeepLoc 2.0 by using a pre-trained protein language model.”
   2. Unchecked“We find that the attention output correlates well with the position of sorting signals.”

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

   Language models enable zero-shot prediction of the effects of mutations on protein function

   Meier, Rao, Verkuil, Liu, Sercu and Rives · bioRxiv (Cold Spring Harbor Laboratory) · 2021

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   1. Unchecked“We show that using only zero-shot inference, without any supervision from experimental data or additional training, protein language models capture the functional effects of sequence variation, performing at state-of-the-art.”

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

   Modeling aspects of the language of life through transfer-learning protein sequences

   Heinzinger, Elnaggar, Wang et al. · BMC Bioinformatics · 2019

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   1. Unchecked“At the per-residue level, secondary structure (Q3 = 79% ± 1, Q8 = 68% ± 1) and regions with intrinsic disorder (MCC = 0.59 ± 0.03) were predicted significantly better than through one-hot encoding or through Word2vec-like approaches.”
   2. Unchecked“Overall, the important novelty is speed: where the lightning-fast HHblits needed on average about two minutes to generate the evolutionary information for a target protein, SeqVec created embeddings on average in 0.03 s.”

6. 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

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   1. Unchecked“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.”

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

   MSA Transformer

   Rao, Liu, Verkuil et al. · bioRxiv (Cold Spring Harbor Laboratory) · 2021

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   1. Unchecked“The performance of the model surpasses current state-of-the-art unsupervised structure learning methods by a wide margin, with far greater parameter efficiency than prior state-of-the-art protein language models.”

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

   BERTology Meets Biology: Interpreting Attention in Protein Language Models

   Vig, Madani, Varshney, Xiong, Socher and Rajani · bioRxiv (Cold Spring Harbor Laboratory) · 2020

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   1. Unchecked“We show that attention: (1) captures the folding structure of proteins, connecting amino acids that are far apart in the underlying sequence, but spatially close in the three-dimensional structure, (2) targets binding sites, a key functional component of pro…

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

   Evaluating Protein Transfer Learning with TAPE

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

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   1. Unchecked“We find that self-supervised pretraining is helpful for almost all models on all tasks, more than doubling performance in some cases.”

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

    Feature Reuse and Scaling: Understanding Transfer Learning with Protein Language Models

    Li, Amini, Yue, Yang and Lu · bioRxiv (Cold Spring Harbor Laboratory) · 2024

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    1. Unchecked“We observe that while almost all down-stream tasks do benefit from pretrained models compared to naive sequence representations, for the majority of tasks performance does not scale with pretraining, and instead relies on low-level features learned early in…