AIMC Topic: Mutation

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Integrative analysis of epigenetic subtypes in acute myeloid Leukemia: A multi-center study combining machine learning for prognostic and therapeutic insights.

PloS one
BACKGROUND: Acute Myeloid Leukemia (AML) exhibits significant heterogeneity in clinical outcomes, yet current prognostic stratification systems based on genetic alterations alone cannot fully capture this complexity. This study aimed to develop an in...

From Pixels to Prognosis: Artificial Intelligence and Machine Learning Models in Brain Tumour Mutation Prediction.

JPMA. The Journal of the Pakistan Medical Association
Brain tumours are a leading cause of death and disability, impacting individuals across all ages, genders, and ethnicities. They are primarily diagnosed using MRI but a precise diagnosis is dependent on the molecular biology of the tumour studied on ...

Enhancing Molecular Network-Based Cancer Driver Gene Prediction Using Machine Learning Approaches: Current Challenges and Opportunities.

Journal of cellular and molecular medicine
Cancer is a complex disease driven by mutations in the genes that play critical roles in cellular processes. The identification of cancer driver genes is crucial for understanding tumorigenesis, developing targeted therapies and identifying rational ...

Explainable Machine Learning Predictions for the Benefit From Chemotherapy in Advanced Non-Small Cell Lung Cancer Without Available Targeted Mutations.

The clinical respiratory journal
BACKGROUND: Non-small cell lung cancer (NSCLC) is a global health challenge. Chemotherapy remains the standard therapy for advanced NSCLC without mutations, but drug resistance often reduces effectiveness. Developing more effective methods to predict...

Building a Risk Scoring Model for ARDS in Lung Adenocarcinoma Patients Using Machine Learning Algorithms.

Journal of cellular and molecular medicine
Lung adenocarcinoma (LUAD), the predominant form of non-small-cell lung cancer, is frequently complicated by acute respiratory distress syndrome (ARDS), which increases mortality risks. Investigating the prognostic implications of ARDS-related genes ...

Raman-based machine-learning platform reveals unique metabolic differences between IDHmut and IDHwt glioma.

Neuro-oncology
BACKGROUND: Formalin-fixed, paraffin-embedded (FFPE) tissue slides are routinely used in cancer diagnosis, clinical decision-making, and stored in biobanks, but their utilization in Raman spectroscopy-based studies has been limited due to the backgro...

Deep learning-based analysis of EGFR mutation prevalence in lung adenocarcinoma H&E whole slide images.

The journal of pathology. Clinical research
EGFR mutations are a major prognostic factor in lung adenocarcinoma. However, current detection methods require sufficient samples and are costly. Deep learning is promising for mutation prediction in histopathological image analysis but has limitati...

Diffusion-weighted MRI precisely predicts telomerase reverse transcriptase promoter mutation status in World Health Organization grade IV gliomas using a residual convolutional neural network.

The British journal of radiology
OBJECTIVES: Telomerase reverse transcriptase promoter (pTERT) mutation status plays a key role in making decisions and predicting prognoses for patients with World Health Organization (WHO) grade IV glioma. This study was conducted to assess the valu...

Gene-Specific Machine Learning Models to Classify Driver Mutations in Clonal Hematopoiesis.

Cancer discovery
There is no general consensus on the set of mutations capable of driving the age-related clonal expansions in hematopoietic stem cells known as clonal hematopoiesis, and current variant classifications typically rely on rules derived from expert know...

Identification of Clonal Hematopoiesis Driver Mutations through In Silico Saturation Mutagenesis.

Cancer discovery
Clonal hematopoiesis (CH) is a phenomenon of clonal expansion of hematopoietic stem cells driven by somatic mutations affecting certain genes. Recently, CH has been linked to the development of hematologic malignancies, cardiovascular diseases, and o...