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Forecasting Chronic Diseases Using Data Fusion.

Evrim Acar¹, Gözde Gürdeniz², Francesco Savorani³

¹Department of Food Science, Faculty of Science, University of Copenhagen , 1958 Frederiksberg C, Denmark.

Journal of Proteome Research

|June 1, 2017

Summary

This summary is machine-generated.

Data fusion of metabolomics (LC-MS, NMR) and lifestyle data improved acute coronary syndrome (ACS) prediction. However, fusion was not always optimal, with NMR alone excelling for breast cancer forecasting.

Keywords:

acute coronary syndrome cancer data fusion liquid chromatography−mass spectrometry multiple kernel learning nuclear magnetic resonance spectroscopy

Related Experiment Videos

Area of Science:

Metabolomics
Biomarker Discovery
Data Fusion

Background:

Metabolomics, utilizing liquid chromatography-mass spectrometry (LC-MS) and nuclear magnetic resonance (NMR) spectroscopy, offers complementary data for biofluid analysis.
Data fusion integrates these diverse datasets to enhance information extraction.

Purpose of the Study:

To forecast acute coronary syndrome (ACS), breast cancer, and colon cancer by jointly analyzing LC-MS, NMR, and lifestyle metadata.
To evaluate the efficacy of supervised data fusion using multiple kernel learning for disease prediction.

Main Methods:

Supervised data fusion based on multiple kernel learning.
Analysis of plasma samples using LC-MS and NMR spectroscopy.
Integration of participant lifestyle metadata.

Main Results:

Fusion of LC-MS, NMR, and metadata improved ACS case and referent separation compared to individual datasets.
NMR data alone showed superior performance for breast cancer forecasting; fusion did not enhance results.
Neither individual datasets nor fusion effectively predicted colon cancer.

Conclusions:

Data fusion can enhance disease prediction for certain conditions like ACS by integrating metabolomics and metadata.
Fusion is not universally superior; the optimal approach depends on the specific disease, as demonstrated by breast cancer.
The study highlights the strengths and limitations of data fusion in biomarker discovery and disease forecasting.