Dear Colleagues,

Although statistics has historically been used to solve problems in data science, mathematical methods and machine learning (ML) can be extremely helpful, especially for building concise decision models, making fast approximations, and predicting evolving phenomena based on known samples. In particular, mathematical modeling and machine learning methods are increasingly used to help interpret biomedical data produced by high-throughput genomics and proteomics projects. Indeed, as the study of biological systems becomes more quantitative, the role played by mathematical analysis increases. This ranges from the macroscopic (e.g., how to model the spread of a disease across a community) to the microscopic (e.g., how to determine the three-dimensional structure of proteins from the knowledge of their amino acid sequence).

The revolution in biological and information technologies has produced huge amounts of data and is accelerating the process of knowledge discovery from biological systems. Furthermore, clinical data complement biological data, allowing for detailed descriptions of both healthy and diseased states, as well as disease progression and response to therapies. With medical imaging playing an increasingly prominent role in disease diagnosis, interest in medical image processing has also increased significantly over the past several decades, with deep learning methods attracting more and more attention.

However, although advances in machine learning algorithms have been deemed critical for improving performance in analyzing huge datasets, their opacity, if not supported by preventive mathematical modeling of the problem, could prevent human experts—and especially doctors—from trusting their abilities and results.

This Special Issue provides a platform for researchers from academia and industry to present their new and unpublished work, and to promote future studies in an emerging field such as applying mathematically founded ML models to highly sensitive data. Topics include but are not limited to:

• Mathematical and numerical methods in understanding biological systems and biomolecular dynamics, e.g., from disease diffusion to intracellular pattern formation.
• Mathematical and computational models in therapy and diagnosis.
• Machine learning algorithms and models for bio-information and bio-data understanding.
• Deep learning techniques and evolutionary computing in biomedical image and signal processing.
• Statistical and artificial intelligence-based models for complex biological data.
• Big data analytics on biomedical pattern recognition.
• Machine learning and artificial intelligence methods in data science with applications in other areas, etc.

Dr. Monica Bianchini
Dr. Maria Lucia Sampoli
Dr. Simone Bonechi
Dr. Pietro Bongini
Guest Editors

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• Mathematical modeling
• Numerical methods
• Machine learning
• Statistical techniques
• Statistical learning
• Data science
• Bioinformatics
• Medical image processing
• Biosignal processing