ArticleInnovation (Cambridge (Mass.))2023
Deciphering "the language of nature": A transformer-based language model for deleterious mutations in proteins.
Article in Innovation (Cambridge (Mass.)), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
25 citing papers in PubMed, 33 citations in OpenAlex.
- Why variant effect predictors and multiplexed assays agree and disagree.Nature communications · 2026Article
- Artificial intelligence in biologic drug discovery: A review of methodological evolution and therapeutic applications.Acta pharmaceutica Sinica. B · 2026Review
- Defining the molecular tolerance-to-damage landscape of SMARCA4 helicase genetic alterations.bioRxiv : the preprint server for biology · 2026Article
- A survey on LLM-as-a-judge.Innovation (Cambridge (Mass.)) · 2026Article
- Disease- and gene-specific deep learning for pathogenicity prediction of rare missense variants in cancer predisposition genes.BioData mining · 2026Article
- MissenseHMM: state-based annotations for missense variants through joint modeling of pathogenicity scores.bioRxiv : the preprint server for biology · 2026Article
- Harnessing artificial intelligence for genomic variant prediction: advances, challenges, and future directions.GigaScience · 2026Review
- DBP-CanPred: a machine learning model for predicting cancer-causing mutations in DNA-binding proteins.Frontiers in bioinformatics · 2026Article
- Deciphering the mechanistic landscape of immune checkpoint blockade in ccRCC: from molecular drivers to therapeutic responses.Frontiers in immunology · 2026Review
- Assessing variant effect predictors and disease mechanisms in intrinsically disordered proteins.PLoS computational biology · 2025Article
- Prediction of pathogenic mutations in human transmembrane proteins and their associated diseases via utilizing pre-trained Bio-LLMs.Communications biology · 2025Article
- Ge-SAND: an explainable deep learning-driven framework for disease risk prediction by uncovering complex genetic interactions in parallel.BMC genomics · 2025Article
- Variant effect predictor correlation with functional assays is reflective of clinical classification performance.Genome biology · 2025Article
- XGBMUT: Predicting the Functional Impact of Missense Mutations Using an Extreme Gradient Boost Classifier.ACS omega · 2025Article
- The pharmacogenomic landscape in the Chinese: An analytics of pharmacogenetic variants in 206,640 individuals.Innovation (Cambridge (Mass.)) · 2025Article
- Paying attention to the SARS-CoV-2 dialect : a deep neural network approach to predicting novel protein mutations.Communications biology · 2025Article
- Protein structure prediction via deep learning: an in-depth review.Frontiers in pharmacology · 2025Review
- Language Modelling Techniques for Analysing the Impact of Human Genetic Variation.Bioinformatics and biology insights · 2025Review
- Genetic variant classification by predicted protein structure: A case study on IRF6.Computational and structural biotechnology journal · 2024Article
- Dear-PSM: A deep learning-based peptide search engine enables full database search for proteomics.Smart medicine · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 1 institution in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Various machine-learning models, including deep neural network models, have already been developed to predict deleteriousness of missense (non-synonymous) mutations. Potential improvements to the current state of the art, however, may still benefit from a fresh look at the biological problem using more sophisticated self-adaptive machine-learning approaches. Recent advances in the field of natural language processing show that transformer models-a type of deep neural network-to be particularly powerful at modeling sequence information with context dependence. In this study, we introduce MutFormer, a transformer-based model for the prediction of deleterious missense mutations, which uses reference and mutated protein sequences from the human genome as the primary features. MutFormer takes advantage of a combination of self-attention layers and convolutional layers to learn both long-range and short-range dependencies between amino acid mutations in a protein sequence. We first pre-trained MutFormer on reference protein sequences and mutated protein sequences resulting from common genetic variants observed in human populations. We next examined different fine-tuning methods to successfully apply the model to deleteriousness prediction of missense mutations. Finally, we evaluated MutFormer's performance on multiple testing datasets. We found that MutFormer showed similar or improved performance over a variety of existing tools, including those that used conventional machine-learning approaches. In conclusion, MutFormer considers sequence features that are not explored in previous studies and can complement existing computational predictions or empirically generated functional scores to improve our understanding of disease variants.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.