A massive amount of data in various forms needs to be handled for any healthcare application and data type, data size and other features are significant in data handling. With the growth of big data in biomedical and healthcare communities, accurate analysis of medical data has the benefits of early disease detection, improved patient care, and effective community services. Because of its significance, there is a need to develop efficient and better-performing algorithms, techniques, and tools to analyze multi-modal medical big data from the gene level to the clinical level. However, the traditional algorithms used are not capable of analyzing such complex data. Machine learning algorithms with a good fit for these kinds of data and analytics are required. Also, different regions exhibit unique characteristics of certain regional diseases, and this may weaken the prediction of disease outbreaks.

In this Special Issue, we would like to highlight the characteristics and features of big data, the importance of big data analytics in healthcare sectors and the various machine learning algorithms used in big data analytics, and the efficiency of such algorithms. We aim to provide data analytics from the genet level, molecular level, to the clinical level in major healthcare areas such as electronic health record maintenance, disease diagnosis, and prediction of emergency conditions of patients.

Potential topics include but are not limited to the following:

• Identification of essential genes and biomarkers for disease diagnosis and prognosis
• Gene regulatory element identification
• Multi-modal big medical data integration methodology
• Advanced algorithms in biomedical big data analytics
• Tool and Server development for big data analytics
• Advances in feature representation learning in complex medical big data analysis
• Data-driven disease diagnosis and prediction
• Modeling and analysis of gene expression data
• Bioinformatics analysis for medical data and other data
• Database construction for medical big data