AIMC Topic: Databases, Genetic
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Statistical and machine learning based platform-independent key genes identification for hepatocellular carcinoma.
PloS one
Feb 5, 2025
Hepatocellular carcinoma (HCC) is the most prevalent and deadly form of liver cancer, and its mortality rate is gradually increasing worldwide. Existing studies used genetic datasets, taken from various platforms, but focused only on common different...
Decoding the cytokine code for heart failure based on bioinformatics, machine learning and Bayesian networks.
Biochimica et biophysica acta. Molecular basis of disease
Feb 4, 2025
BACKGROUND: Despite maximal pharmacological treatment guided by clinical guidelines, the prognosis of heart failure (HF) remains poor, posing a significant public health burden. This necessitates uncovering novel pathological and cardioprotective pat...
Elucidating the role of FBXW4 in osteoporosis: integrating bioinformatics and machine learning for advanced insight.
BMC pharmacology & toxicology
Jan 29, 2025
BACKGROUND: Osteoporosis (OP), often termed the "silent epidemic," poses a substantial public health burden. Emerging insights into the molecular functions of FBXW4 have spurred interest in its potential roles across various diseases.
Identification of biomarkers associated with coronary artery disease and non-alcoholic fatty liver disease by bioinformatics analysis and machine learning.
Scientific reports
Jan 28, 2025
The constantly emerging evidence indicates a close association between coronary artery disease (CAD) and non-alcoholic fatty liver disease (NAFLD). However, the exact mechanisms underlying their mutual relationship remain undefined. This study aims t...
Establishment of a nomogram model based on immune-related genes using machine learning for aortic dissection diagnosis and immunomodulation assessment.
International journal of medical sciences
Jan 21, 2025
The clinical manifestation of aortic dissection (AD) is complex and varied, making early diagnosis crucial for patient survival. This study aimed to identify immune-related markers to establish a nomogram model for AD diagnosis. Three datasets from G...
Simplifying clinical use of TCGA molecular subtypes through machine learning models.
Cancer cell
Jan 16, 2025
In this issue of Cancer Cell, Ellrott et al. present machine learning models to classify samples into The Cancer Genome Atlas molecular subtypes using compact sets of genomic features. These validated, ready-to-use models are publicly available, alth...
A robust transfer learning approach for high-dimensional linear regression to support integration of multi-source gene expression data.
PLoS computational biology
Jan 10, 2025
Transfer learning aims to integrate useful information from multi-source datasets to improve the learning performance of target data. This can be effectively applied in genomics when we learn the gene associations in a target tissue, and data from ot...
Interpretable identification of cancer genes across biological networks via transformer-powered graph representation learning.
Nature biomedical engineering
Jan 9, 2025
Graph representation learning has been leveraged to identify cancer genes from biological networks. However, its applicability is limited by insufficient interpretability and generalizability under integrative network analysis. Here we report the dev...
Integrating bioinformatics and machine learning to identify AhR-related gene signatures for prognosis and tumor microenvironment modulation in melanoma.
Frontiers in immunology
Jan 6, 2025
BACKGROUND: The Aryl Hydrocarbon Receptor (AhR) pathway significantly influences immune cell regulation, impacting the effectiveness of immunotherapy and patient outcomes in melanoma. However, the specific downstream targets and mechanisms by which A...
Databases, Genetic
Basic Helix-Loop-Helix Transcription Factors
Gene Expression Regulation, Neoplastic
Biomarkers, Tumor
Lymphocytes, Tumor-Infiltrating
Animals
Humans
Melanoma
Transcriptome
Receptors, Aryl Hydrocarbon
Gene Expression Profiling
Skin Neoplasms
Machine Learning
Mice
Tumor Microenvironment
Prognosis
Computational Biology
Classification of non-TCGA cancer samples to TCGA molecular subtypes using compact feature sets.
Cancer cell
Jan 2, 2025
Molecular subtypes, such as defined by The Cancer Genome Atlas (TCGA), delineate a cancer's underlying biology, bringing hope to inform a patient's prognosis and treatment plan. However, most approaches used in the discovery of subtypes are not suita...
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