To prove the flexibility of the reviewed methods, the authors also considered a problem rising from astrophysics, namely clustering of ultraluminous infrared galaxies based on their spectral energy distribution.

In both scenarios, magnetoencephalography and electroencephalography were used for monitoring brain activity. The reviewed methods are illustrated on two neuroscientific studies: (i) the study of correlations between sleep stages and brain activity (ii) the characterization of brain response to median nerve stimulation. reviewed two families of numerical approaches for dimensionality reduction of complex systems, based on principal component analysis and graph clustering theory, respectively. The Research Topic features two Reviews and three Original Research articles that are briefly summarized below. To this end, we focused on two specific biomedical fields, namely neuroscience and oncology, motivated by the fact that cancer and various neurodegenerative dementias, e.g., due to Alzheimer's and Parkinson's diseases, are among the leading causes of death worldwide 1. The purpose of this Research Topic is to review and present recent mathematical and statistical developments in the analysis of biomedical data. Similarly, computational models and simulations may be used to build a patient's digital twin opening the possibilities for highly personalized therapeutic plans. For example, machine learning and data mining approaches can be used for patients classification and modeling of disease progression. ĭata science approaches based on robust mathematical and statistical methods have proven themselves to be of crucial importance in both these scenarios. In contrast, at a clinical level the need for real-time, minimally-invasive, and cost-effective measurements often reduces the quality of the recorded data rendering them to be noisy and incomplete. In many fields of biomedical research the development and use of breakthrough technologies often produce a huge amount of heterogeneous, multi-scale data ranging from molecular genomic data to images describing the function and structure of tissues.

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