Article

Protein Language Model Predicts Mutation Pathogenicity and Clinical Prognosis

2022-10-03

Abstract excerpt

Accurately predicting the effects of mutations in cancer has the potential to improve existing treatments and identify novel therapeutic targets. In this paper, we evidence for the first time that the large-scale pre-trained protein language models (PPLMs) are zero-shot predictors for two clinically relevant tasks: identifying diseasecausing mutations and predicting patient survival rate. Then we benchmark a seri...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: 4bc9ce3b-7205-5f8f-b5c8-45f8a3e41a57
DOI: 10.1101/2022.09.30.510294

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Protein Language Model Predicts Mutation Pathogenicity and Clinical PrognosisDOI 10.1101/2022.09.30.510294Read

Learning sequence to predict gain- or loss-of-function variantsSemantic proximity Read

Genome-wide prediction of disease variants with a deep protein language modelSemantic proximity Read

Disease-specific variant pathogenicity prediction using multimodal biomedical language modelsSemantic proximity Read

Causal Prediction of TP53 Variant Pathogenicity Using a Perturbation-Informed Protein Language Model.Semantic proximity Read

GPN-MSA: an alignment-based DNA language model for genome-wide variant effect predictionSemantic proximity Read

VariPred: Enhancing Pathogenicity Prediction of Missense Variants Using Protein Language ModelsSemantic proximity Read

VariPred: Enhancing Pathogenicity Prediction of Missense Variants Using Protein Language ModelsSemantic proximity Read

Predicting the pathogenicity of protein coding mutations using Natural Language Processing.Semantic proximity Read

Interpreting Protein Language Models: high attention sites predict functional regionsSemantic proximity Read

E-SNPs&GO: Embedding of protein sequence and function improves the annotation of human pathogenic variantsSemantic proximity Read

PATHOS: Predicting Variant Pathogenicity by Combining Protein Language Models and Biological FeaturesSemantic proximity Read

Generalist large language models complement tailor-made predictors for tumor genomics interpretationSemantic proximity Read