AIMC Topic: Metabolome

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Discovery of urinary biosignatures for tuberculosis and nontuberculous mycobacteria classification using metabolomics and machine learning.

Scientific reports
Nontuberculous mycobacteria (NTM) infection diagnosis remains a challenge due to its overlapping clinical symptoms with tuberculosis (TB), leading to inappropriate treatment. Herein, we employed noninvasive metabolic phenotyping coupled with comprehe...

Metabolic profiling during COVID-19 infection in humans: Identification of potential biomarkers for occurrence, severity and outcomes using machine learning.

PloS one
BACKGROUND: After its emergence in China, the coronavirus SARS-CoV-2 has swept the world, leading to global health crises with millions of deaths. COVID-19 clinical manifestations differ in severity, ranging from mild symptoms to severe disease. Alth...

Machine Learning-Based Integrated Multiomics Characterization of Colorectal Cancer Reveals Distinctive Metabolic Signatures.

Analytical chemistry
The metabolic signature identification of colorectal cancer is critical for its early diagnosis and therapeutic approaches that will significantly block cancer progression and improve patient survival. Here, we combined an untargeted metabolic analys...

Integrating Machine Learning in Metabolomics: A Path to Enhanced Diagnostics and Data Interpretation.

Small methods
Metabolomics, leveraging techniques like NMR and MS, is crucial for understanding biochemical processes in pathophysiological states. This field, however, faces challenges in metabolite sensitivity, data complexity, and omics data integration. Recent...

Metabolomics facilitates differential diagnosis in common inherited retinal degenerations by exploring their profiles of serum metabolites.

Nature communications
The diagnosis of inherited retinal degeneration (IRD) is challenging owing to its phenotypic and genotypic complexity. Clinical information is important before a genetic diagnosis is made. Metabolomics studies the entire picture of bioproducts, which...

Metabolomics profile and machine learning prediction of treatment responses in immune thrombocytopenia: A prospective cohort study.

British journal of haematology
Immune thrombocytopenia (ITP) is an autoimmune disease characterized by antibody-mediated platelet destruction and impaired platelet production. The mechanisms underlying ITP and biomarkers predicting the response of drug treatments are elusive. We p...

Multi-omics analysis identifies potential microbial and metabolite diagnostic biomarkers of bacterial vaginosis.

Journal of the European Academy of Dermatology and Venereology : JEADV
BACKGROUND: Bacterial vaginosis (BV) is a common clinical manifestation of a perturbed vaginal ecology associated with adverse sexual and reproductive health outcomes if left untreated. The existing diagnostic modalities are either cumbersome or requ...

DeepOmicsAE: Representing Signaling Modules in Alzheimer's Disease with Deep Learning Analysis of Proteomics, Metabolomics, and Clinical Data.

Journal of visualized experiments : JoVE
Large omics datasets are becoming increasingly available for research into human health. This paper presents DeepOmicsAE, a workflow optimized for the analysis of multi-omics datasets, including proteomics, metabolomics, and clinical data. This workf...

Machine learning approach reveals microbiome, metabolome, and lipidome profiles in type 1 diabetes.

Journal of advanced research
INTRODUCTION: Type 1 diabetes (T1D) is a complex disorder influenced by genetic and environmental factors. The gut microbiome, the serum metabolome, and the serum lipidome have been identified as key environmental factors contributing to the pathophy...

Untargeted metabolomic profiling in children identifies novel pathways in asthma and atopy.

The Journal of allergy and clinical immunology
BACKGROUND: Asthma and other atopic disorders can present with varying clinical phenotypes marked by differential metabolomic manifestations and enriched biological pathways.