Neuro- and onco-biology are two fields of application for mathematical biology aimed at disentangling the complex mechanisms behind brain functioning and cancer progression and treatment, respectively. Magnetic Resonance Imaging, Computerized Tomography, Positron Emission Tomography, Magneto/Electro-encephalography, Mass Spectroscopy, Citometry, and Next Generation Sequencing are some of the diagnostic tools currently available to neuroscientists and oncologists. However, the large amount of data produced by these devices requires the development of suitable data science approaches to interpret, classify and model the recorded information. Concrete applications of the advanced mathematical tools developed in these fields include, for example, automatic computation of biomarkers for early-stage diagnosis of cancer and neurodegenerative diseases, in silico identification of novel targets for cancer therapies, non-invasive localization of epileptogenic zones to guide possible surgery plans.
This Research Topic aims at providing an interdisciplinary overview of the most recent developments and current challenges in the field of mathematical oncology and neuroscience. To this end we will welcome manuscripts presenting any kind of mathematical tools tackling practical issues rising in the fields of neuro- and onco-biology. A particular interest is devoted to (i) inverse problem techniques to interpret data for which only indirect measurements are available; (ii) imaging techniques to automatically extract features from recorded images and use them for stratification purposes; (iii) dynamical models of cell signaling processes; (iv) machine learning and feature selection techniques to identify biomarkers and build predictive models. Manuscripts presenting novel mathematical development as well as papers describing specific software tools are welcome. Papers with a richer biology content aimed at highlighting new methodological challenges will also be considered.
The topics mainly include but are not limited to the following aspects:
• Chemical reaction network models for cells signaling in cancer
• Pathway-based methods for drug repurposing
• Compartmental analysis in tracer kinetics
• Mathematical tools for functional brain connectivity estimation
• Forward and inverse modeling of neural sources
• AI techniques in radiomics
• Treatment optimization in personalized healthcare.
• Bioinformatics tools for analysing multiomics data
A variety of types of manuscripts will be considered, including Original Research, Methods, Review, Mini Review, and Perspective.
Neuro- and onco-biology are two fields of application for mathematical biology aimed at disentangling the complex mechanisms behind brain functioning and cancer progression and treatment, respectively. Magnetic Resonance Imaging, Computerized Tomography, Positron Emission Tomography, Magneto/Electro-encephalography, Mass Spectroscopy, Citometry, and Next Generation Sequencing are some of the diagnostic tools currently available to neuroscientists and oncologists. However, the large amount of data produced by these devices requires the development of suitable data science approaches to interpret, classify and model the recorded information. Concrete applications of the advanced mathematical tools developed in these fields include, for example, automatic computation of biomarkers for early-stage diagnosis of cancer and neurodegenerative diseases, in silico identification of novel targets for cancer therapies, non-invasive localization of epileptogenic zones to guide possible surgery plans.
This Research Topic aims at providing an interdisciplinary overview of the most recent developments and current challenges in the field of mathematical oncology and neuroscience. To this end we will welcome manuscripts presenting any kind of mathematical tools tackling practical issues rising in the fields of neuro- and onco-biology. A particular interest is devoted to (i) inverse problem techniques to interpret data for which only indirect measurements are available; (ii) imaging techniques to automatically extract features from recorded images and use them for stratification purposes; (iii) dynamical models of cell signaling processes; (iv) machine learning and feature selection techniques to identify biomarkers and build predictive models. Manuscripts presenting novel mathematical development as well as papers describing specific software tools are welcome. Papers with a richer biology content aimed at highlighting new methodological challenges will also be considered.
The topics mainly include but are not limited to the following aspects:
• Chemical reaction network models for cells signaling in cancer
• Pathway-based methods for drug repurposing
• Compartmental analysis in tracer kinetics
• Mathematical tools for functional brain connectivity estimation
• Forward and inverse modeling of neural sources
• AI techniques in radiomics
• Treatment optimization in personalized healthcare.
• Bioinformatics tools for analysing multiomics data
A variety of types of manuscripts will be considered, including Original Research, Methods, Review, Mini Review, and Perspective.