Since "Precision Medicine" was initially launched by President Obama, it presents a huge challenge and chance for the computational biology and biomedicine. In the recent years, computational methods appeared vastly in the biomedicine and bioinformatics research, including medical image analysis, healthcare informatics, and cancer genomics. Lots of prediction and mining works were required on the medical data, such as tumor images, electronic medical records, microarray, and GWAS (Genome-Wide Association Study) data. Therefore, a growing number of data mining algorithms were employed in the prediction tasks of computational biology and biomedicine.

Advanced data mining techniques have also been developed quickly in recent years. Several impacted new methods were reported in the top journals and conferences. For example, affinity propagation was published in Science as a novel clustering algorithm. Recently, deep learning seems to be suitable for big data and is becoming to be the next hot topic. Parallel mechanism is also developed by the scholar and industry researchers, such as Mahout. A growing number of computer scientists are devoted to the advanced large scale data mining techniques. However, application in biomedicine has not fully been addressed and fell behind the technique growth.

This special issue will target the recent large scale data mining techniques together with biomedicine application. Application on medical and biology scalable data is encouraged. We especially encourage clinical or specific diseases genomics research with computational methods. We also welcome novel classification and clustering algorithms, such as strategies for large imbalanced learning, strategies for multiple views learning, strategies for various semisupervised learning, and strategies for multiple kernels learning. Only machine learning theory without biomedicine application cannot be accepted. We encourage authors to supply their codes and open their real biology or medical data, which would make our issue more innovative. Please do not test your algorithm just only on some well-known benchmark datasets.

The special issue welcomes a set of recent advances in the related topics, to provide a platform for researchers to exchange their innovative ideas and real biomedical data.

Potential topics include, but are not limited to:

• Novel computational strategies for clinical or specific diseases research
• Large scale classification algorithms with application on biomedicine or bioinformatics
• Large scale clustering algorithms with application on biomedicine or bioinformatics
• Imbalanced learning algorithms for biomedical or bioinformatics data
• Multiple views learning for medical image classification
• Semisupervised learning strategies for biomedical or bioinformatics data
• Ensemble learning strategies for biomedical or bioinformatics data
• Parallel learning techniques for ultra large biomedical or bioinformatics data
• Multiple kernels learning with application on biomedicine or bioinformatics
• Multiple labels classification algorithms with application on biomedicine or bioinformatics