AIMC Topic: Metabolomics

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Deriving three one dimensional NMR spectra from a single experiment through machine learning.

Nature communications
Nuclear Magnetic Resonance (NMR) spectroscopy is a powerful tool for analyzing complex mixtures due to its ability to manage matrix complexity, provide detailed molecular insights, and preserve sample integrity. In metabolomics, NMR enables the ident...

Improving newborn screening accuracy through genome sequencing, targeted metabolomics, and machine learning.

BMC medical genomics
BACKGROUND: Newborn screening (NBS) enables early detection of metabolic disorders, but current tandem mass spectrometry (MS/MS) methods often lead to false positives and require confirmatory testing, causing diagnostic delays. We evaluated whether i...

Integrated metabolomic profiling identifies citrate as novel diagnostic biomarker for Anti-MDA5-Positive dermatomyositis.

Arthritis research & therapy
OBJECTIVE: Anti-melanoma differentiation-associated gene 5-positive dermatomyositis (anti-MDA5 + DM) is a unique subtype of idiopathic inflammatory myopathy (IIM) with a poorer prognosis. The immune-metabolic landscape underlying anti-MDA5 + DM patho...

Bioactive compound identification without fractionation: an Ocimum spp. case study.

Metabolomics : Official journal of the Metabolomic Society
INTRODUCTION: Identifying the phytochemistry underpinning a plant's observed therapeutic benefits is essential for understanding mechanisms of action and developing novel therapeutics. More recent efforts fusing global metabolomics and multivariate p...

Multi-marker discovery for mild cognitive impairment in metabolomics using machine learning with a global surrogate model via partial least squares.

Metabolomics : Official journal of the Metabolomic Society
INTRODUCTION: Dementia can be prevented through early intervention; hence, there is an urgent need for biomarkers to help diagnose mild cognitive impairment (MCI).

Precision integrated identification of predictive first-trimester metabolomics signatures for early detection of gestational diabetes mellitus.

Cardiovascular diabetology
BACKGROUND AND AIM: Gestational diabetes mellitus (GDM), a common pregnancy-related metabolic disorder, often goes undiagnosed until the second trimester, limiting early intervention opportunities. Given the higher prevalence of GDM in India, there i...

Recent Advances in Integrating Machine Learning with Omics Approaches in Food Science and Nutrition Research.

Journal of agricultural and food chemistry
Omics technologies are revolutionizing food and nutrition research by enabling high-throughput analysis of food components and microorganisms and revealing the intricate relationships between food and human health. Machine learning (ML) methods are p...

Beyond single biomarkers: multi-omics strategies to predict immunotherapy outcomes in blood cancers.

Clinical and experimental medicine
Immunotherapy has revolutionized hematologic cancer treatment, yet responses remain unpredictable due to primary resistance, relapse, and life-threatening toxicities. Conventional biomarkers fail to capture the complexity of tumor-immune interactions...

Comprehensive analysis of metabolic and molecular alterations in the blood of patients with Sjögren's syndrome based on untargeted metabolomics analysis.

BMC medical genomics
BACKGROUND: Sjögren's syndrome (SS) is a chronic autoimmune disorder marked by lymphocytic infiltration of exocrine glands, leading to xerostomia, keratoconjunctivitis sicca, and systemic involvement including fatigue, arthralgia, and visceral organ ...

Using unsupervised machine learning methods to cluster cardio-metabolic profile of the middle-aged and elderly Chinese with general and central obesity.

BMC cardiovascular disorders
BACKGROUND: Obesity is a disease with high heterogeneity. Both overall obesity and central obesity are associated with increased risks of having cardio-metabolic co-morbidities. This study is aimed to examine the cardio-metabolic characteristics and ...