AIMC Topic: Mutation

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EVOLVE: A Web Platform for AI-Based Protein Mutation Prediction and Evolutionary Phase Exploration.

Journal of chemical information and modeling
While predicting structure-function relationships from sequence data is fundamental in biophysical chemistry, identifying prospective single-point and collective mutation sites in proteins can help us stay ahead in understanding their potential effec...

Endometrial tumorigenesis involves epigenetic plasticity demarcating non-coding somatic mutations and 3D-genome alterations.

Genome biology
BACKGROUND: The incidence and mortality of endometrial cancer (EC) is on the rise. Eighty-five percent of ECs depend on estrogen receptor alpha (ERα) for proliferation, but little is known about its transcriptional regulation in these tumors.

Using machine learning models to predict the impact of template mismatches on polymerase chain reaction assay performance.

Scientific reports
Molecular assays are critical tools for the diagnosis of infectious diseases. These assays have been extremely valuable during the COVID pandemic, used to guide both patient management and infection control strategies. Sustained transmission and unhi...

The Role of Artificial Intelligence in Identifying Gene Variants and Improving Diagnosis.

Genes
Neurofibromatosis type 1 (NF1) is an autosomal dominant disorder caused by mutations in the gene, typically diagnosed during early childhood and characterized by significant phenotypic heterogeneity. Despite advancements in next-generation sequencin...

[Exploration of the Predictive Value of Peripheral Blood-related Indicators for EGFR 
Mutations and Prognosis in Non-small Cell Lung Cancer Using Machine Learning].

Zhongguo fei ai za zhi = Chinese journal of lung cancer
BACKGROUND: Epidermal growth factor receptor (EGFR) sensitive mutation is one of the effective targets of targeted therapy for non-small cell lung cancer (NSCLC). However, due to the difficulty of obtaining some primary tissues and the economic facto...

Using minor variant genomes and machine learning to study the genome biology of SARS-CoV-2 over time.

Nucleic acids research
In infected individuals, viruses are present as a population consisting of dominant and minor variant genomes. Most databases contain information on the dominant genome sequence. Since the emergence of SARS-CoV-2 in late 2019, variants have been sele...

Predicting Diabetic Retinopathy Using a Machine Learning Approach Informed by Whole-Exome Sequencing Studies.

Biomedical and environmental sciences : BES
OBJECTIVE: To establish and validate a novel diabetic retinopathy (DR) risk-prediction model using a whole-exome sequencing (WES)-based machine learning (ML) method.

SCREEN: A Graph-based Contrastive Learning Tool to Infer Catalytic Residues and Assess Enzyme Mutations.

Genomics, proteomics & bioinformatics
The accurate identification of catalytic residues contributes to our understanding of enzyme functions in biological processes and pathways. The increasing number of protein sequences necessitates computational tools for the automated prediction of c...

Deep Learning-Based SD-OCT Layer Segmentation Quantifies Outer Retina Changes in Patients With Biallelic RPE65 Mutations Undergoing Gene Therapy.

Investigative ophthalmology & visual science
PURPOSE: To quantify outer retina structural changes and define novel biomarkers of inherited retinal degeneration associated with biallelic mutations in RPE65 (RPE65-IRD) in patients before and after subretinal gene augmentation therapy with voretig...