AIMC Topic: Mutation

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Clustering cell nuclei on microgrooves for disease diagnosis using deep learning.

Scientific reports
Various diseases including laminopathies and certain types of cancer are associated with abnormal nuclear mechanical properties that influence cellular and nuclear deformations in complex environments. Recently, microgroove substrates designed to mim...

Functional Characterization of Variants of Unknown Significance of Fibroblast Growth Factor Receptors 1-4 and Comparison With AI Model-Based Prediction.

JCO precision oncology
PURPOSE: Fibroblast growth factor receptors (FGFRs; FGFR1, FGFR2, FGFR3, FGFR4) are frequently mutated oncogenes in solid cancers. The oncogenic potential of FGFR rearrangements and few hotspot point mutations is well established, but the majority of...

Massively parallel genetic perturbation suggests the energetic structure of an amyloid-β transition state.

Science advances
Amyloid aggregates are pathological hallmarks of many human diseases, but how soluble proteins nucleate to form amyloids is poorly understood. Here, we use combinatorial mutagenesis, a kinetic selection assay, and machine learning to massively pertur...

MFDSMC: Accurate Identification of Cancer-Driver Synonymous Mutations Using Multiperspective Feature Representation Learning.

Journal of chemical information and modeling
Synonymous mutations do not change amino acid sequences, but they can drive cancer by influencing splicing, mRNA structure, translation efficiency, and other molecular mechanisms. Although driver synonymous mutations are significantly outnumbered by ...

A radiogenomics study on F-FDG PET/CT in endometrial cancer by a novel deep learning segmentation algorithm.

BMC cancer
OBJECTIVE: To create an automated PET/CT segmentation method and radiomics model to forecast Mismatch repair (MMR) and TP53 gene expression in endometrial cancer patients, and to examine the effect of gene expression variability on image texture feat...

Designing diverse and high-performance proteins with a large language model in the loop.

PLoS computational biology
We present a protein engineering approach to directed evolution with machine learning that integrates a new semi-supervised neural network fitness prediction model, Seq2Fitness, and an innovative optimization algorithm, biphasic annealing for diverse...

Shining Light on DNA Mutations through Machine Learning-Augmented Vibrational Spectroscopy.

Analytical chemistry
A method to directly predict the number of nucleic acid bases in a single-stranded DNA (ssDNA) or a genomic DNA has been proposed with a combination of Raman spectroscopy and an Artificial Neural Network (ANN) algorithm. In this work, the algorithm w...

GNNMutation: a heterogeneous graph-based framework for cancer detection.

BMC bioinformatics
BACKGROUND: When genes are translated into proteins, mutations in the gene sequence can lead to changes in protein structure and function as well as in the interactions between proteins. These changes can disrupt cell function and contribute to the d...

Prediction of Lymph Node Metastasis in Non-Small Cell Lung Carcinoma Using Primary Tumor Somatic Mutation Data.

JCO clinical cancer informatics
PURPOSE: Lymph node metastasis (LNM) significantly affects prognosis and treatment strategies in non-small cell lung cancer (NSCLC). Current diagnostic methods, including imaging and histopathology, have limited sensitivity and specificity. This stud...

The Role of PANoptosis-Related Genes in Predicting Breast Cancer Survival and Immune Prospect.

BioMed research international
The function of PANoptosis in breast cancer (BC) remains indistinct. We constructed a nomogram model to predict the prognosis of BC to identify high-risk patients and help them receive more accurate treatment. We used Cox regression and least absol...