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Metabolomics

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An integrated deep learning framework for the interpretation of untargeted metabolomics data.

Briefings in bioinformatics
Untargeted metabolomics is gaining widespread applications. The key aspects of the data analysis include modeling complex activities of the metabolic network, selecting metabolites associated with clinical outcome and finding critical metabolic pathw...

Deep Learning Based Metabolite Annotation.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Metabolite annotation is a major bottleneck in untargeted metabolomics studies by liquid chromatography coupled with mass spectrometry (LC-MS). This is in part due to the limited publicly available spectral libraries, which consist of tandem mass spe...

1st International Conference on Biomedical Sciences, Dow University of Health Sciences, Karachi Pakistan.

JPMA. The Journal of the Pakistan Medical Association
Institute of Biomedical Sciences (IBMS) at Dow University of Health Sciences (DUHS), organised a two day's conference on Biomedical Sciences. IBMS being the part of one of the largest public sector health universities of Pakistan, is now transforming...

SmartGate is a spatial metabolomics tool for resolving tissue structures.

Briefings in bioinformatics
Imaging mass spectrometry (IMS) is one of the powerful tools in spatial metabolomics for obtaining metabolite data and probing the internal microenvironment of organisms. It has dramatically advanced the understanding of the structure of biological t...

3D-MSNet: a point cloud-based deep learning model for untargeted feature detection and quantification in profile LC-HRMS data.

Bioinformatics (Oxford, England)
MOTIVATION: Liquid chromatography coupled with high-resolution mass spectrometry is widely used in composition profiling in untargeted metabolomics research. While retaining complete sample information, mass spectrometry (MS) data naturally have the ...

Using Artificial Intelligence to Better Predict and Develop Biomarkers.

Clinics in laboratory medicine
Advancements in technology have improved biomarker discovery in the field of heart failure (HF). What was once a slow and laborious process has gained efficiency through use of high-throughput omics platforms to phenotype HF at the level of genes, tr...

METAbolomics data Balancing with Over-sampling Algorithms (META-BOA): an online resource for addressing class imbalance.

Bioinformatics (Oxford, England)
MOTIVATION: Class imbalance, or unequal sample sizes between classes, is an increasing concern in machine learning for metabolomic and lipidomic data mining, which can result in overfitting for the over-represented class. Numerous methods have been d...

Deep learning-based pseudo-mass spectrometry imaging analysis for precision medicine.

Briefings in bioinformatics
Liquid chromatography-mass spectrometry (LC-MS)-based untargeted metabolomics provides systematic profiling of metabolic. Yet, its applications in precision medicine (disease diagnosis) have been limited by several challenges, including metabolite id...

massNet: integrated processing and classification of spatially resolved mass spectrometry data using deep learning for rapid tumor delineation.

Bioinformatics (Oxford, England)
MOTIVATION: Mass spectrometry imaging (MSI) provides rich biochemical information in a label-free manner and therefore holds promise to substantially impact current practice in disease diagnosis. However, the complex nature of MSI data poses computat...

CRISP: a deep learning architecture for GC × GC-TOFMS contour ROI identification, simulation and analysis in imaging metabolomics.

Briefings in bioinformatics
Two-dimensional gas chromatography-time-of-flight mass spectrometry (GC × GC-TOFMS) provides a large amount of molecular information from biological samples. However, the lack of a comprehensive compound library or customizable bioinformatics tool is...