AIMC Topic: Multiomics

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A practical guide to FAIR data management in the age of multi-OMICS and AI.

Frontiers in immunology
Multi-cellular biological systems, including the immune system, are highly complex, dynamic, and adaptable. Systems biologists aim to understand such complexity at a quantitative level. However, these ambitious efforts are often limited by access to ...

MOCapsNet: Multiomics Data Integration for Cancer Subtype Analysis Based on Dynamic Self-Attention Learning and Capsule Networks.

Journal of chemical information and modeling
: 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
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
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...

Comprehensive bioinformatics analysis identifies metabolic and immune-related diagnostic biomarkers shared between diabetes and COPD using multi-omics and machine learning.

Frontiers in endocrinology
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
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...

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
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
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...