Machine Learning in Biomedical Sciences

Dear Colleagues,

Machine learning (ML) has emerged as a transformative tool in the biomedical sciences, offering unprecedented opportunities for analyzing complex biological data and enhancing patient care. The integration of ML with biomedical research has led to significant advancements in diagnostics, personalized medicine, drug discovery, and understanding of complex diseases. As we navigate through vast datasets, ranging from genomic sequences to clinical records, machine learning provides powerful techniques to uncover patterns, make predictions, and inform decision making.

The convergence of biomedical data with advanced ML techniques such as deep learning, natural language processing, and computer vision is revolutionizing how we approach medical research and healthcare delivery. This integration allows for more accurate diagnostic tools, predictive models for disease progression, and the development of innovative therapeutic strategies. Furthermore, ML algorithms are becoming essential in handling the ever-growing data from medical imaging, electronic health records, and high-throughput omic technologies.

This Special Issue on "Machine Learning in Biomedical Sciences" aims to collect the latest research and developments in this rapidly evolving field. We invite contributions that explore the application of machine learning methods to various aspects of biomedical sciences, including diagnostic systems, predictive analytics, personalized treatment plans, and biomedical data analysis. This Special Issue seeks to provide a platform for researchers and practitioners to discuss the challenges, innovations, and future directions of ML in the biomedical domain.

Recommended topics include the following:

• Machine learning in medical imaging and diagnostics;
• Predictive models for disease progression and patient outcomes;
• Deep learning applications in genomics and proteomics;
• Natural language processing (NLP) in clinical text analysis;
• Integration of ML with electronic health records (EHRs) for personalized medicine;
• Drug discovery and development using machine learning;
• ML methods for analyzing high-throughput biological data;
• Reinforcement learning in treatment planning and decision support;
• Explainable AI in healthcare and biomedical research;
• Ethical considerations and biases in ML applications in biomedicine;
• Case studies on successful ML implementations in healthcare systems;
• Real-time data analysis and decision making in medical settings;
• Future trends and challenges in ML applications in biomedical sciences.

We encourage submissions from researchers, clinicians, and data scientists working at the intersection of machine learning and biomedical sciences. This Special Issue aims to provide insights into how ML is shaping the future of biomedicine and improving patient outcomes.

Dr. Matteo Bodini
Dr. Giovanni Cugliari
Dr. Andrea Loddo
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 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.

• machine learning
• biomedical sciences
• medical imaging
• predictive analytics
• genomics
• personalized medicine
• natural language processing (NLP)
• electronic health records (EHRs)
• drug discovery
• explainable AI