Manuscript Topics

The last decade was amazing in the identification of genes and biomarkers associated to genetic diseases and disorders; however, with the increasing development of new diseases and their associated data, it is required to develop new methods to discover disease associated genes, biomarkers and new medicines to cure them. Machine Learning (ML) and Deep Learning (DL) have emerged as promising approaches for constructing precise and robust models from medical data, which is collected in huge volumes by modern healthcare systems. However, developing an effective analytical approach is still a critical challenge due to the diversity of big health data.

On the other hand, data security and privacy are known as one of the biggest concerns in the existing methods since they often rely on storing data in a centralized repository, where ML and DL analysis are done with full access to the sensitive underlying content. Recent advance in Artificial Intelligence (AI) results in developing a federated learning as a new approach to overcome the privacy issue and meld AI with HIPAA-mandated data privacy.

This special issue will focus on the application of federated learning and machine learning techniques for discussing new research, development, and deployment efforts in the analysis of genetic diseases and disorders. In this special issue, we invite authors to submit original, high-quality research articles, clearly focused on aspects of the theoretical foundations, empirical studies, and novel applications of federated learning and machine learning for next-generation intelligent systems.

Potential topics include, but are not limited to:  
• Federated Recommended Systems and Information Retrieval  
• Incentive Models and Mechanisms for Federated Learning  
• Application of Federated Learning in healthcare and data privacy  
• Intelligence Edge Computing and federated learning in healthcare  
• Machine learning in computational biomarker discovery in genetic diseases and disorders  
• Clinical machine learning and translational medicine in genetic diseases and disorders  
• Mathematical modelling in neurocomputing  
• Biological big data in neurocomputing  
• Deep learning techniques to uncover novel patterns and biomarkers in genetic diseases and disorders  
• Supercomputing to identify genetic biomarkers  
• Mathematical methods for diagnostic classifiers  
• Biological network analysis

Instructions for authors
https://www.aimspress.com/mbe/news/solo-detail/instructionsforauthors
Please submit your manuscript to online submission system
https://aimspress.jams.pub/