Big Data and Mathematical Modeling in Biomedicine

Dear Colleagues,

In recent years, Big Data have garnered significant interest from the scientific community. The concept of Big Data refers to extremely large and massive data sets that, because of their complexity and high degree of heterogeneity, cannot be analyzed and interpreted by means of conventional approaches (such as multivariate regression analyses and similar techniques). Being technically demanding and computationally challenging, they require particular efforts: New algorithms are required to effectively handle, manipulate, and coherently integrate data (the so-called “Big Data analytics”). These methodologies enable scholars to extract significant and relevant patterns in terms of trends, interactions, associations, and correlations. Big Data are classically characterized by three Vs: (i) velocity (in terms of the speed of data acquisition and data processing, Big Data as “fast data”); (ii) volume (in terms of amount of information); and (iii) variety (in terms of the number of sources and streams that can produce Big Data). There are different kinds of Big Data, depending on their generating sources, including: (i) “molecular Big Data”, produced by wet-lab techniques (and recent OMICS-based approaches like genomics, proteomics, postgenomics, metabolomics/metabonomics, epigenomics, etc.); (ii) “digital Big Data” generated by imaging technologies and sensors (in particular, wearable sensors); and (iii) “computational Big Data” produced by sources like the internet, mobiles, smart phones, and other devices. In the field of biomedicine, Big Data can be used for a variety of purposes and tasks, such as: (i) performing disease surveillance and signal detection, (ii) predicting disease risk, (iii) targeting treatment interventions, and, last but not least, (iv) understanding the etiopathogenetic mechanisms of diseases. Mathematical modelling is of paramount importance in Big Data visualization, mining, clustering, analysis, and interpretation.

Dr. Nicola Bragazzi
Prof. Jianhong Wu
Guest Editors

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