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Metabolomics

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Machine learning-based classification and prediction of typical Chinese green tea taste profiles.

Food research international (Ottawa, Ont.)
The taste of Chinese green tea is highly diverse. In this study, a combination of unsupervised and supervised learning methods was utilized to develop a model for classifying and predicting typical Chinese green tea taste. Three clustering methods we...

Metabolomic machine learning predictor for arsenic-associated hypertension risk in male workers.

Journal of pharmaceutical and biomedical analysis
Arsenic (As)-induced hypertension is a significant public health concern, highlighting the need for early risk prediction. This study aimed to develop a predictive model for occupational As exposure and hypertension using metabolomics and machine lea...

Machine Learning-Based Identification of Novel Exosome-Derived Metabolic Biomarkers for the Diagnosis of Systemic Lupus Erythematosus and Differentiation of Renal Involvement.

Current medical science
OBJECTIVE: This study aims to investigate the exosome-derived metabolomics profiles in systemic lupus erythematosus (SLE), identify differential metabolites, and analyze their potential as diagnostic markers for SLE and lupus nephritis (LN).

Toward an integrated omics approach for plant biosynthetic pathway discovery in the age of AI.

Trends in biochemical sciences
Elucidating plant biosynthetic pathways is key to advancing a sustainable bioeconomy by enabling access to complex natural products through synthetic biology. Despite progress from genomic, transcriptomic, and metabolomic approaches, much multiomics ...

Identification of metabolite-disease associations based on knowledge graph.

Metabolomics : Official journal of the Metabolomic Society
BACKGROUND: Despite the insights that metabolite analysis can provide into the onset, development, and progression of diseases-thus offering new concepts and methodologies for prevention, diagnosis, and treatment-traditional wet lab experiments are o...

Deep learning-based clustering for endotyping and post-arthroplasty response classification using knee osteoarthritis multiomic data.

Annals of the rheumatic diseases
OBJECTIVES: Primary knee osteoarthritis (KOA) is a heterogeneous disease with clinical and molecular contributors. Biofluids contain microRNAs and metabolites that can be measured by omic technologies. Multimodal deep learning is adept at uncovering ...

A metabolic fingerprint of ovarian cancer: a novel diagnostic strategy employing plasma EV-based metabolomics and machine learning algorithms.

Journal of ovarian research
Ovarian cancer (OC) is the third most common malignant tumor of women and is accompanied by an alteration of systemic metabolism. A liquid biopsy that captures and detects tumor-related biomarkers in body fluids has great potential for OC diagnosis. ...

Rapid metabolic profiling and authentication of Cordyceps using ambient ionization mass spectrometry and machine learning.

Analytical and bioanalytical chemistry
Cordyceps sinensis, a symbiotic organism formed between a fungus and an insect, is celebrated for its substantial medicinal benefits and economic significance in traditional Chinese medicine. However, the market for Cordyceps sinensis is rife with co...

Predicting Sprint Potential: A Machine Learning Model Based on Blood Metabolite Profiles in Young Male Athletes.

European journal of sport science
This study aims to utilize male blood metabolite signatures for (i) distinguishing between healthy individuals and athletes, thereby optimizing the athlete screening process; and (ii) predicting athletic performance in 100, 200, and 400 m sprints, en...