AIMC Topic: Multiomics

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Multi-omics Mendelian randomization and machine learning identify candidate therapeutic targets for Alzheimer's and Parkinson's diseases.

Mammalian genome : official journal of the International Mammalian Genome Society
Neurodegenerative diseases (NDDs), including Alzheimer's disease (AD) and Parkinson's disease (PD), are major public health challenges lacking effective therapies. To identify potential drug targets, we integrated large-scale genome-wide association ...

Machine learning and multi-omics integration identifies immunological predictors and mechanistic insights in autoimmune encephalitis.

Inflammation research : official journal of the European Histamine Research Society ... [et al.]
OBJECTIVE: To develop an interpretable prognostic prediction model for autoimmune encephalitis (AE) using immunological indicators and to investigate the potential role of nucleophosmin (NPM1) in disease pathogenesis through multi-omics approaches.

From glycolytic signatures to patients: A translational roadmap for reproducible, equitable deployment of multi-omics and AI in colorectal cancer.

Medical oncology (Northwood, London, England)
Recent advances in Medical Oncology highlight the integration of bulk and single-cell transcriptomics to reveal glycolytic heterogeneity in colorectal cancer. Translating these discoveries into reliable clinical tools requires rigorous methods, trans...

LIMPACAT: Multi-omics attention transformer for immune prediction in liver cancer using whole-slide imaging.

PloS one
Characterizing the tumor immune microenvironment from histopathological images offers opportunities for ex vivo immune profiling and prognostic assessment. However, the TCGA-LIHC dataset lacks direct immune cell composition data. Therefore, this stud...

Integrative multi-omics and network-based machine learning for early diagnosis of Parkinson's disease.

PloS one
BACKGROUND: Accurate diagnosis of Parkinson's Disease (PD) remains challenging due to its biological complexity. Integrating machine learning with multi-omics and network topological analyses may enhance diagnostic precision.

Multi-omics and machine learning refine HCC molecular subtypes and prognosis based on liquid-liquid phase separation related genes.

Scientific reports
Accumulating evidence has demonstrated that biological processes associated with liquid-liquid phase separation (LLPS) play a critical role in cancer development. However, the effect of LLPS on hepatocellular carcinoma (HCC) remains largely unknown. ...

Integrated multi-omic and symptom clustering reveals lower-gastrointestinal disorders of gut-brain interaction heterogeneity.

Gut microbes
Rome IV disorders of gut-brain interaction (DGBI) subtypes are known to be unstable and demonstrate high rates of non-treatment response, likely indicating patient heterogeneity. Cluster analysis, a type of unsupervised machine learning, can identify...

Multi-omics study of molecular and genetic bases of orthostatic hypotension.

Clinical epigenetics
Orthostatic hypotension is a sharp decrease in blood pressure when an individual transitions from a supine to an upright position. OH affects at least 30% of older adults. It is attributed to the dysfunction of the autonomic innervation and decreased...

Multi-omics driven computational framework for cancer molecular subtype classification.

Scientific reports
Cancer molecular subtype classification is an essential component of precision oncology which provides insights into cancer prognosis and guides targeted therapy. Despite the growing applications of AI for cancer molecular subtype classification, cha...

A novel prognostic model for lung squamous cell carcinoma based on multi-omics analysis and machine learning.

PloS one
Lung squamous-cell carcinoma (LUSC) is a highly aggressive malignancy with a poor prognosis. Tertiary lymphoid structures (TLS) play a crucial role in the immune response and significantly influence the efficacy of immunotherapy. However, the prognos...