AIMC Topic: Lipidomics

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Resolving multi-image spatial lipidomic responses to inhaled toxicants by machine learning.

Nature communications
Regional responses to inhaled toxicants are essential to understand the pathogenesis of lung disease under exposure to air pollution. We evaluate the effect of combined allergen sensitization and ozone exposure on eliciting spatial differences in lip...

Untargeted Lipidomic Biomarkers for Liver Cancer Diagnosis: A Tree-Based Machine Learning Model Enhanced by Explainable Artificial Intelligence.

Medicina (Kaunas, Lithuania)
: Liver cancer ranks among the leading causes of cancer-related mortality, necessitating the development of novel diagnostic methods. Deregulated lipid metabolism, a hallmark of hepatocarcinogenesis, offers compelling prospects for biomarker identifi...

Toward Machine Learning Electrospray Ionization Sensitivity Prediction for Semiquantitative Lipidomics in Stem Cells.

Journal of chemical information and modeling
Specificity, sensitivity, and high metabolite coverage make mass spectrometry (MS) one of the most valuable tools in metabolomics and lipidomics. However, translation of metabolomics MS methods to multiyear studies conducted across multiple batches i...

Machine learning-driven Raman spectroscopy: A novel approach to lipid profiling in diabetic kidney disease.

Nanomedicine : nanotechnology, biology, and medicine
Diabetes mellitus is a chronic metabolic disease that increasingly affects people every year. It is known that with its progression and poor management, metabolic changes can lead to organ dysfunctions, including kidneys. The study aimed to combine R...

Screening of serum biomarkers in patients with PCOS through lipid omics and ensemble machine learning.

PloS one
Polycystic ovary syndrome (PCOS) is a primary endocrine disorder affecting premenopausal women involving metabolic dysregulation. We aimed to screen serum biomarkers in PCOS patients using untargeted lipidomics and ensemble machine learning. Serum fr...

Enhancing lipid identification in LC-HRMS data through machine learning-based retention time prediction.

Journal of chromatography. A
The comprehensive identification of peaks in untargeted lipidomics using LC-MS/MS remains a significant challenge. Confidence in lipid annotation can be greatly improved by integrating a highly accurate machine learning-based retention time predictio...

LipoCLEAN: A Machine Learning Filter to Improve Untargeted Lipid Identification Confidence.

Analytical chemistry
In untargeted lipidomics experiments, putative lipid identifications generated by automated analysis software require substantial manual filtering to arrive at usable high-confidence data. However, identification software tools do not make full use o...

Combination of plasma-based lipidomics and machine learning provides a useful diagnostic tool for ovarian cancer.

Journal of pharmaceutical and biomedical analysis
Ovarian cancer (OC), the second leading cause of death among gynecological cancers, is often diagnosed at an advanced stage due to its asymptomatic nature at early stages. This study aimed to explore the diagnostic potential of plasma-based lipidomic...

Machine learning and biological validation identify sphingolipids as potential mediators of paclitaxel-induced neuropathy in cancer patients.

eLife
BACKGROUND: Chemotherapy-induced peripheral neuropathy (CIPN) is a serious therapy-limiting side effect of commonly used anticancer drugs. Previous studies suggest that lipids may play a role in CIPN. Therefore, the present study aimed to identify th...

Lipidomics combined with random forest machine learning algorithms to reveal freshness markers for duck eggs during storage in different rearing systems.

Poultry science
The differences in lipids in duck eggs between the 2 rearing systems during storage have not been fully studied. Herein, we propose untargeted lipidomics combined with a random forest (RF) algorithm to identify potential marker lipids based on ultra-...