AIMC Topic: Mutation
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Ontology-driven identification of inconsistencies in clinical data: A case study in lung cancer phenotyping.
Journal of biomedical informatics
Mar 21, 2025
OBJECTIVE: To illustrate the use of an ontology in evaluating data quality in the medical field, focusing on phenotyping lung cancers.
A fusion model to predict the survival of colorectal cancer based on histopathological image and gene mutation.
Scientific reports
Mar 20, 2025
Colorectal cancer (CRC) is a prevalent gastrointestinal tumor worldwide with high morbidity and mortality. Predicting the survival of CRC patients not only enhances understanding of their life expectancies but also aids clinicians in making informed ...
Histopathology based AI model predicts anti-angiogenic therapy response in renal cancer clinical trial.
Nature communications
Mar 17, 2025
Anti-angiogenic (AA) therapy is a cornerstone of metastatic clear cell renal cell carcinoma (ccRCC) treatment, but not everyone responds, and predictive biomarkers are lacking. CD31, a marker of vasculature, is insufficient, and the Angioscore, an RN...
A multimodal framework for assessing the link between pathomics, transcriptomics, and pancreatic cancer mutations.
Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Mar 15, 2025
In Pancreatic Ductal Adenocarcinoma (PDAC), predicting genetic mutations directly from histopathological images using Deep Learning can provide valuable insights. The combination of several omics can provide further knowledge on mechanisms underlying...
Deep learning prioritizes cancer mutations that alter protein nucleocytoplasmic shuttling to drive tumorigenesis.
Nature communications
Mar 14, 2025
Genetic variants can affect protein function by driving aberrant subcellular localization. However, comprehensive analysis of how mutations promote tumor progression by influencing nuclear localization is currently lacking. Here, we systematically ch...
Deep learning radiomics for the prediction of epidermal growth factor receptor mutation status based on MRI in brain metastasis from lung adenocarcinoma patients.
BMC cancer
Mar 12, 2025
BACKGROUND: Early and accurate identification of epidermal growth factor receptor (EGFR) mutation status in non-small cell lung cancer (NSCLC) patients with brain metastases is critical for guiding targeted therapy. This study aimed to develop a deep...
Biochemical Characterization of Disease-Associated Variants of Human Ornithine Transcarbamylase.
ACS chemical biology
Mar 10, 2025
Human ornithine transcarbamylase deficiency (OTCD) is the most common ureagenesis disorder in the world. OTCD is an X-linked genetic deficiency in which patients experience hyperammonemia to varying degrees depending on the severity of the genetic mu...
Identification of Novel Fourth-Generation Allosteric Inhibitors Targeting Inactive State of EGFR T790M/L858R/C797S and T790M/L858R Mutations: A Combined Machine Learning and Molecular Dynamics Approach.
The journal of physical chemistry. B
Mar 7, 2025
Targeted therapy with an allosteric inhibitor (AIs) is an important area of research in patients with epidermal growth factor receptor (EGFR) mutations. Current treatment of nonsmall cell lung cancer patients with EGFR mutations using orthosteric inh...
Development of a MVI associated HCC prognostic model through single cell transcriptomic analysis and 101 machine learning algorithms.
Scientific reports
Mar 7, 2025
Hepatocellular carcinoma (HCC) is an exceedingly aggressive form of cancer that often carries a poor prognosis, especially when it is complicated by the presence of microvascular invasion (MVI). Identifying patients at high risk of MVI is crucial for...
Liver
Meta-Analysis as Topic
Mutation
Datasets as Topic
Gene Expression Regulation, Neoplastic
Treatment Outcome
Neoplasm Invasiveness
Kaplan-Meier Estimate
Microvessels
Immune Checkpoint Proteins
Clinical Decision-Making
Biomarkers, Tumor
Risk Assessment
Liver Neoplasms
Carcinoma, Hepatocellular
Single-Cell Gene Expression Analysis
Immunotherapy
Humans
Machine Learning
Precision Medicine
Prognosis
Deep mutational learning for the selection of therapeutic antibodies resistant to the evolution of Omicron variants of SARS-CoV-2.
Nature biomedical engineering
Mar 5, 2025
Most antibodies for treating COVID-19 rely on binding the receptor-binding domain (RBD) of SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2). However, Omicron and its sub-lineages, as well as other heavily mutated variants, have rendered m...
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