AIMC Topic: Multiomics
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MOCapsNet: Multiomics Data Integration for Cancer Subtype Analysis Based on Dynamic Self-Attention Learning and Capsule Networks.
Journal of chemical information and modeling
Jan 16, 2025
: With the rapid development of the accumulation of large-scale multiomics data sets, integrating various omics data to provide a thorough study from multiple perspectives can significantly provide stronger support for precise treatment of diseases. ...
Machine learning and multi-omics in precision medicine for ME/CFS.
Journal of translational medicine
Jan 14, 2025
Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) is a complex and multifaceted disorder that defies simplistic characterisation. Traditional approaches to diagnosing and treating ME/CFS have often fallen short due to the condition's hetero...
Integrated multi-omics analysis identifies a machine learning-derived signature for predicting prognosis and therapeutic vulnerability in clear cell renal cell carcinoma.
Life sciences
Jan 12, 2025
AIMS: Clear cell renal cell carcinoma (ccRCC) shows considerable variation within and between tumors, presents varying treatment responses among patients, possibly due to molecular distinctions. This study utilized a multi-center and multi-omics anal...
Multi‑omics identification of a novel signature for serous ovarian carcinoma in the context of 3P medicine and based on twelve programmed cell death patterns: a multi-cohort machine learning study.
Molecular medicine (Cambridge, Mass.)
Jan 8, 2025
BACKGROUND: Predictive, preventive, and personalized medicine (PPPM/3PM) is a strategy aimed at improving the prognosis of cancer, and programmed cell death (PCD) is increasingly recognized as a potential target in cancer therapy and prognosis. Howev...
Comprehensive bioinformatics analysis identifies metabolic and immune-related diagnostic biomarkers shared between diabetes and COPD using multi-omics and machine learning.
Frontiers in endocrinology
Jan 8, 2025
BACKGROUND: Diabetes and chronic obstructive pulmonary disease (COPD) are prominent global health challenges, each imposing significant burdens on affected individuals, healthcare systems, and society. However, the specific molecular mechanisms suppo...
MO-GCN: A multi-omics graph convolutional network for discriminative analysis of schizophrenia.
Brain research bulletin
Jan 7, 2025
The methodology of machine learning with multi-omics data has been widely adopted in the discriminative analyses of schizophrenia, but most of these studies ignored the cooperative interactions and topological attributes of multi-omics networks. In t...
Uncovering the epigenetic regulatory clues of PRRT1 in Alzheimer's disease: a strategy integrating multi-omics analysis with explainable machine learning.
Alzheimer's research & therapy
Jan 7, 2025
BACKGROUND: Alzheimer's disease (AD) is a complex neurodegenerative disorder with a largely unexplored epigenetic landscape.
Integrating immune multi-omics and machine learning to improve prognosis, immune landscape, and sensitivity to first- and second-line treatments for head and neck squamous cell carcinoma.
Scientific reports
Dec 28, 2024
In recent years, immune checkpoint inhibitors (ICIs) has emerged as a fundamental component of the standard treatment regimen for patients with head and neck squamous cell carcinoma (HNSCC). However, accurately predicting the treatment effectiveness ...
Multiomics integration and machine learning reveal prognostic programmed cell death signatures in gastric cancer.
Scientific reports
Dec 28, 2024
Gastric cancer (GC) is characterized by notable heterogeneity and the impact of molecular subtypes on treatment and prognosis. The role of programmed cell death (PCD) in cellular processes is critical, yet its specific function in GC is underexplored...
Prediction of Composite Clinical Outcomes for Childhood Neuroblastoma Using Multi-Omics Data and Machine Learning.
International journal of molecular sciences
Dec 27, 2024
Neuroblastoma is a common malignant tumor in childhood that seriously endangers the health and lives of children, making it essential to find effective prognostic markers to accurately predict their clinical outcomes. The development of high-throughp...
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