GORT

Reviews

Entprise-X: Predicting Disease-Associated Frameshift And Nonsense Mutations

Di: Everly

Here, we extend the machine learning based approach ENTPRISE developed for predicting the disease association of missense mutations to frameshift and nonsense

Receiver operating characteristic curves of ENTPRISE-X

(PDF) Gene characteristics predicting missense, nonsense and frameshift ...

Here, we extend the machine learning based approach ENTPRISE developed for predicting the disease association of missense mutations to frameshift and nonsense mutations. The new

The problem of how to distinguish between neutral and potentially disease-associated frameshift and non-sense mutations remains under-researched. RESULTS. We built a Transformer

Motivation: Protein structure can be severely disrupted by frameshift and non-sense mutations at specific positions in the protein sequence. Frameshift and non-sense

Genome-wide | Frameshift Mutation, Missense Mutation and Mutation | ResearchGate, the professional network for scientists. Fig 3 – available via license: Creative Commons Attribution

The new approach, ENTPRISE-X, is shown to outperform the state-of-the-art methods VEST-indel and DDIG-in for predicting the disease association of germline frameshift

Here, we develop a boosted tree regression machine-learning approach to predict human disease-associated amino acid variations by utilizing a comprehensive combination of

Missense, Nonsense and Frameshift Mutations: A Genetic Guide

Europe PMC is an archive of life sciences journal literature.

Here, we extend the machine learning based approach ENTPRISE developed for predicting the disease association of missense mutations to frameshift and nonsense mutations.

to the onset of a disease. Even so, it might not be the only cause of the disease. A mutation, in particular frameshift and nonsense ones, could result in a loss or gain of function of the protein.

Here, we develop a boosted tree regression machine-learning approach to predict human disease-associated amino acid variations by utilizing a comprehensive combination of protein

The new approach, ENTPRISE-X, is shown to outperform the state-of-the-art methods VEST-indel and DDIG-in for predicting the disease association of germline frameshift mutations in

A method to distinguish neutral and potentially disease-associated frameshift and nonsense mutations is of practical and fundamental importance. It would allow researchers to rapidly screen

Here, we extend the machine learning based approach ENTPRISE developed for predicting the disease association of missense mutations to frameshift and nonsense mutations. The new approach, ENTPRISE-X, is

Here, we extend the machine learning based approach ENTPRISE developed for predicting the disease association of missense mutations to frameshift and nonsense mutations. The new

ENTPRISE-X: Predicting disease-associated frameshift and nonsense mutations

Here, we develop a boosted tree regression machine-learning approach to predict human disease-associated amino acid variations by utilizing a comprehensive combination of

Here, we extend the machine learning based approach ENTPRISE developed for predicting the disease association of missense mutations to frameshift and nonsense mutations.

The new approach, ENTPRISE-X, is shown to outperform the state-of-the-art methods VEST-indel and DDIG-in for predicting the disease association of germline frameshift

Here, we extend the machine learning based approach ENTPRISE developed for predicting the disease association of missense mutations to frameshift and nonsense mutations. The new

The new approach, ENTPRISE-X, is shown to outperform the state-of-the-art methods VEST-indel and DDIG-in for predicting the disease association of germline frameshift

In 10-fold cross-validation and independent blind test set, TransPPMP showed good robust performance and absolute advantages in all evaluation metrics compared with four

ENTPRISE: An Algorithm for Predicting Human Disease-Associated Amino Acid Substitutions from Sequence Entropy and Predicted Protein Structures. Zhou H, Gao M, Skolnick J. PLoS

The new approach, ENTPRISE-X, is shown to outperform the state-of-the-art methods VEST-indel and DDIG-in for predicting the disease association of germline frameshift

An algorithm for predicting human disease-associated frameshift & nonsense mutations. This server will fast retrieve pre-computed mutation scores (score > 0.5 is disease-associated).

ENTPRISE: An Algorithm for Predicting Human Disease-Associated Amino Acid Substitutions from Sequence Entropy and Predicted Protein Structures. Zhou H, Gao M,

Explore millions of resources from scholarly journals, books, newspapers, videos and more, on the ProQuest Platform.