AI Medical Compendium Topic:
Mutation

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DDMut: predicting effects of mutations on protein stability using deep learning.

Nucleic acids research
Understanding the effects of mutations on protein stability is crucial for variant interpretation and prioritisation, protein engineering, and biotechnology. Despite significant efforts, community assessments of predictive tools have highlighted ongo...

DeepAlloDriver: a deep learning-based strategy to predict cancer driver mutations.

Nucleic acids research
Driver mutations can contribute to the initial processes of cancer, and their identification is crucial for understanding tumorigenesis as well as for molecular drug discovery and development. Allostery regulates protein function away from the functi...

Mutate and observe: utilizing deep neural networks to investigate the impact of mutations on translation initiation.

Bioinformatics (Oxford, England)
MOTIVATION: The primary regulatory step for protein synthesis is translation initiation, which makes it one of the fundamental steps in the central dogma of molecular biology. In recent years, a number of approaches relying on deep neural networks (D...

Recent application of artificial intelligence on histopathologic image-based prediction of gene mutation in solid cancers.

Briefings in bioinformatics
PURPOSE: Evaluation of genetic mutations in cancers is important because distinct mutational profiles help determine individualized drug therapy. However, molecular analyses are not routinely performed in all cancers because they are expensive, time-...

Predicting the pathogenicity of missense variants using features derived from AlphaFold2.

Bioinformatics (Oxford, England)
MOTIVATION: Missense variants are a frequent class of variation within the coding genome, and some of them cause Mendelian diseases. Despite advances in computational prediction, classifying missense variants into pathogenic or benign remains a major...

Cryptic mutations of PLC family members in brain disorders: recent discoveries and a deep-learning-based approach.

Brain : a journal of neurology
Phospholipase C (PLC) is an essential isozyme involved in the phosphoinositide signalling pathway, which maintains cellular homeostasis. Gain- and loss-of-function mutations in PLC affect enzymatic activity and are therefore associated with several d...

AD-Syn-Net: systematic identification of Alzheimer's disease-associated mutation and co-mutation vulnerabilities via deep learning.

Briefings in bioinformatics
Alzheimer's disease (AD) is one of the most challenging neurodegenerative diseases because of its complicated and progressive mechanisms, and multiple risk factors. Increasing research evidence demonstrates that genetics may be a key factor responsib...

Code-free machine learning for classification of central nervous system histopathology images.

Journal of neuropathology and experimental neurology
Machine learning (ML), an application of artificial intelligence, is currently transforming the analysis of biomedical data and specifically of biomedical images including histopathology. The promises of this technology contrast, however, with its cu...

Combined molecular subtyping, grading, and segmentation of glioma using multi-task deep learning.

Neuro-oncology
BACKGROUND: Accurate characterization of glioma is crucial for clinical decision making. A delineation of the tumor is also desirable in the initial decision stages but is time-consuming. Previously, deep learning methods have been developed that can...

Predicting colorectal cancer tumor mutational burden from histopathological images and clinical information using multi-modal deep learning.

Bioinformatics (Oxford, England)
MOTIVATION: Tumor mutational burden (TMB) is an indicator of the efficacy and prognosis of immune checkpoint therapy in colorectal cancer (CRC). In general, patients with higher TMB values are more likely to benefit from immunotherapy. Though whole-e...