AIMC Topic: Mutation

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[Prevalence of transmitted drug resistance in HIV-infected treatment-naive patients in Chile].

Revista medica de Chile
BACKGROUND: Transmitted drug resistance (TDR) occurs in patients with HIV infection who are not exposed to antiretroviral drugs but who are infected with a virus with mutations associated with resistance.

Prediction of clinically actionable genetic alterations from colorectal cancer histopathology images using deep learning.

World journal of gastroenterology
BACKGROUND: Identifying genetic mutations in cancer patients have been increasingly important because distinctive mutational patterns can be very informative to determine the optimal therapeutic strategy. Recent studies have shown that deep learning-...

Prediction of survival rate and effect of drugs on cancer patients with somatic mutations of genes: An AI-based approach.

Chemical biology & drug design
The causal role of somatic mutation and its interrelationship with gene expression profile during tumor development has already been observed, which plays a major role to decide the cancer grades and overall survival. Accurate and robust prediction o...

TMLRpred: A machine learning classification model to distinguish reversible EGFR double mutant inhibitors.

Chemical biology & drug design
The EGFR is a clinically important therapeutic drug target in lung cancer. The first-generation tyrosine kinase inhibitors used in clinics are effective against L858R-mutated EGFR. However, relapse of the disease due to the presence of resistant muta...

Next-Generation Radiogenomics Sequencing for Prediction of EGFR and KRAS Mutation Status in NSCLC Patients Using Multimodal Imaging and Machine Learning Algorithms.

Molecular imaging and biology
PURPOSE: Considerable progress has been made in the assessment and management of non-small cell lung cancer (NSCLC) patients based on mutation status in the epidermal growth factor receptor (EGFR) and Kirsten rat sarcoma viral oncogene (KRAS). At the...

DeepCLIP: predicting the effect of mutations on protein-RNA binding with deep learning.

Nucleic acids research
Nucleotide variants can cause functional changes by altering protein-RNA binding in various ways that are not easy to predict. This can affect processes such as splicing, nuclear shuttling, and stability of the transcript. Therefore, correct modeling...

A critical review of five machine learning-based algorithms for predicting protein stability changes upon mutation.

Briefings in bioinformatics
A number of machine learning (ML)-based algorithms have been proposed for predicting mutation-induced stability changes in proteins. In this critical review, we used hypothetical reverse mutations to evaluate the performance of five representative al...

Predicting the pathogenicity of protein coding mutations using Natural Language Processing.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
DNA-Sequencing of tumor cells has revealed thousands of genetic mutations. However, cancer is caused by only some of them. Identifying mutations that contribute to tumor growth from neutral ones is extremely challenging and is currently carried out m...

Evaluating the Predictability of Cancer Types from 536 Somatic Mutations: A New Dataset.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
In this paper, we introduce a new dataset for cancer research containing somatic mutation states of 536 genes of the Cancer Gene Census (CGC). We used somatic mutation information from the Cancer Genome Atlas (TCGA) projects to create this dataset. A...

Unsupervised machine learning and prognostic factors of survival in chronic lymphocytic leukemia.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: Unsupervised machine learning approaches hold promise for large-scale clinical data. However, the heterogeneity of clinical data raises new methodological challenges in feature selection, choosing a distance metric that captures biological...