AIMC Topic: Multiomics

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Brassica microgreens shape gut microbiota and functional metabolite profiles in a species-related manner: A multi-omics approach following in vitro gastrointestinal digestion and large intestine fermentation.

Microbiological research
Brassicaceae microgreens constitute a novel and promising source of bioactive compounds, such as polyphenols and glucosinolates. In this work, an integrative computational approach was performed to decipher the interaction between bioaccessible micro...

Recent advances in omics and the integration of multi-omics in osteoarthritis research.

Arthritis research & therapy
Osteoarthritis (OA) is a complex disorder driven by the combination of environmental and genetic factors. Given its high global prevalence and heterogeneity, developing effective and personalized treatment methods is crucial. This requires identifyin...

Clinical Validation of a Noninvasive Multi-Omics Method for Multicancer Early Detection in Retrospective and Prospective Cohorts.

The Journal of molecular diagnostics : JMD
Recent studies highlight the promise of blood-based multicancer early detection (MCED) tests for identifying asymptomatic patients with cancer. However, most focus on a single cancer hallmark, thus limiting effectiveness because of cancer's heterogen...

Machine Learning-Based Radiomics in Malignancy Prediction of Pancreatic Cystic Lesions: Evidence from Cyst Fluid Multi-Omics.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)
The malignant potential of pancreatic cystic lesions (PCLs) varies dramatically, leading to difficulties when making clinical decisions. This study aimed to develop noninvasive clinical-radiomic models using preoperative CT images to predict the mali...

Leveraging TME features and multi-omics data with an advanced deep learning framework for improved Cancer survival prediction.

Scientific reports
Glioma, a malignant intracranial tumor with high invasiveness and heterogeneity, significantly impacts patient survival. This study integrates multi-omics data to improve prognostic prediction and identify therapeutic targets. Using single-cell data ...

Identification of pivotal genes and regulatory networks associated with SAH based on multi-omics analysis and machine learning.

Scientific reports
Subarachnoid hemorrhage (SAH) is a disease with high mortality and morbidity, and its pathophysiology is complex but poorly understood. To investigate the potential therapeutic targets post-SAH, the SAH-related feature genes were screened by the comb...

Integrating machine learning models with multi-omics analysis to decipher the prognostic significance of mitotic catastrophe heterogeneity in bladder cancer.

Biology direct
BACKGROUND: Mitotic catastrophe is well-known as a major pathway of endogenous tumor death, but the prognostic significance of its heterogeneity regarding bladder cancer (BLCA) remains unclear.

Deconvolution of cell types and states in spatial multiomics utilizing TACIT.

Nature communications
Identifying cell types and states remains a time-consuming, error-prone challenge for spatial biology. While deep learning increasingly plays a role, it is difficult to generalize due to variability at the level of cells, neighborhoods, and niches in...

A Multi-Omics-Based Prognostic Model for Elderly Breast Cancer by Machine Learning: Insights From Hypoxia and Immunity of Tumor Microenvironment.

Clinical breast cancer
INTRODUCTION: Older adult breast cancer (OABC) patients (≥ 65 years) frequently experience poorer prognoses compared to younger adults, attributed to complex tumor biology and age-related factors. The present study employs a multiomics approach combi...