Data fusion: examples in fusing metabolomics and transcriptomics data

Published on November 30, 2017   41 min
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Hello, my name is Johan A. Westerhuis and I'm from the University of Amsterdam. I'm going to give you an overview of Data Fusion methods, and I will show some examples of fusing Metabolomics and Transcriptomics Data. On this next slide,
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you see the content of my talk. I will first introduce difference between data integration and data fusion. Now, we'll discuss some of the goals of data fusion, and after that I will show some of the methods used and some complex issues in data fusion. In this next slide,
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you see an example of data integration by multiple sources of omics data, and also ontologies and databases are combined. On this map, now you see an overview of the glycolysis, of some data, and where we have measured metabolomics data, RNA-seq, miRNA, proteomics and even more sources of data. Now, because this is the glycolysis, we have to know information about each gene, where it's active in this map. Therefore, we need the ontologies, and we might even use some of databases, for example, consisting of mRNA and miRNA associations. So, to get the whole overview of all of these different data sources, it's what I call data integration. In this next slide,
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Data fusion: examples in fusing metabolomics and transcriptomics data

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