With the advances of computer technology, computation methods have been comprehensively developed and have opened up new possibilities for information acquisition, storage, processing, and interpretation. The past few decades have witnessed the rapid increase of the use of computation methods in biomedical practices and research settings. The application of computation methods to biomedical information analysis not only allows medical professionals and researcher to bypass the issues caused by manual information processing but also provides them unprecedented control of the biomedical data.

Fast-growing biomedical and healthcare data have encompassed multiple scales ranging from molecules and individuals to populations and various types including images, speech, and texts. Those data have been becoming an enabling resource for accelerating basic science discoveries and facilitating evidence-based clinical solutions. Although the methods for extracting patterns from data have been around for decades, it is still extremely difficult to transform massive data into valuable knowledge by these traditional means of analysis. This motivates the development of state-of-the-art solutions to discover available representations or structures of biomedical data using efficient computation methods. Therefore, the novel integration of computation methods and biomedical information analysis has become a great meaningful topic for researchers now.

We invite researchers from both academia and healthcare industry to contribute original research articles as well as review articles that will address the challenges in computation methods for biomedical information analysis.

Potential topics include but are not limited to the following:

• Big data science including storage, analysis, modeling, and visualization for biomedical data
• Personalized medicine and translational bioinformatics
• Medical image processing and analysis including reconstruction, restoration, compression, registration, fusion, segmentation, modeling, and visualization
• Natural language processing, literature mining, semantic analysis, and date privacy
• Modeling and simulation of biological processes, pathways, and networks
• Evolutionary computing, swarm intelligence/optimization, and ensemble methods
• Neural computing, kernel methods, and feature selection/extraction/reduction of biomedical information
• Deep learning techniques for biomedical information processing
• Brain informatics including cognitive neuropsychology, cerebral function, and artificial neural networks
• Tumor informatics including tumor detection/classification/staging and computer-aided diagnosis
• Hospital information system including design, modeling, and evaluation