At the accomplishment of the human genome project, techniques that can analyze large amounts of data are urgently needed. Advances in computational techniques for analyzing high-throughput data in genomics, proteomics, and visualization have been extensively studied and have played vital roles in understanding biological mechanisms. Machine learning and related techniques such as support vector machines, Markov models, decision trees, and neural networks have been increasingly used to solve problems in genomics and systems biology.

The main focus of this special issue is on new applications and developments of machine learning techniques to address the contemporary problems in genomics and systems biology, especially those computationally hard problems and those which involve randomness and noisy data. This special issue will serve as an international platform for researchers who have an expertise in machine learning, genomics, systems biology, and their applications in medicine. It will also serve as a forum for researchers to discuss recent advancements in machine learning methods in the field. Potential topics include, but are not limited to:

• Data mining and pattern recognition methods for next-generation sequencing data analysis
• Data management and data visualization methods and tools
• Biomarker data integration and information retrieval
• Identification of structural variations
• Computational proteomics for clinical applications
• Prediction of protein structure and protein-protein interactions
• Large-scale data integration for genomics or proteomics data

Before submission authors should carefully read over the journal's Author Guidelines, which are located at http://www.hindawi.com/journals/cmmm/guidelines/. Prospective authors should submit an electronic copy of their complete manuscript through the journal Manuscript Tracking System at http://mts.hindawi.com/ according to the following timetable: