Rapid discrimination between deleterious and benign missense mutations in the CAGI 6 experiment.
Hum Genomics
; 18(1): 89, 2024 Aug 27.
Article
em En
| MEDLINE
| ID: mdl-39192324
ABSTRACT
We describe the machine learning tool that we applied in the CAGI 6 experiment to predict whether single residue mutations in proteins are deleterious or benign. This tool was trained using only single sequences, i.e., without multiple sequence alignments or structural information. Instead, we used global characterizations of the protein sequence. Training and testing data for human gene mutations was obtained from ClinVar (ncbi.nlm.nih.gov/pub/ClinVar/), and for non-human gene mutations from Uniprot (www.uniprot.org). Testing was done on post-training data from ClinVar. This testing yielded high AUC and Matthews correlation coefficient (MCC) for well trained examples but low generalizability. For genes with either sparse or unbalanced training data, the prediction accuracy is poor. The resulting prediction server is available online at http//www.mamiris.com/Shoni.cagi6.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Mutação de Sentido Incorreto
/
Aprendizado de Máquina
Limite:
Humans
Idioma:
En
Revista:
Hum Genomics
/
Hum. genomics (Online)
/
Human genomics (Online)
Assunto da revista:
GENETICA
Ano de publicação:
2024
Tipo de documento:
Article
País de afiliação:
Estados Unidos
País de publicação:
Reino Unido