Guest Editorial Foreword to the Special Issue on Intelligent Computation for Bioinformatics

T HE LAST few decades have witnessed significant advancements in intelligent computation techniques. Driven by the need to solve complex real-world problems, powerful and sophisticated intelligent data analysis technologies have been exploited or emerged, such as neural networks, support vector machines, evolutionary algorithms, clustering methods, fuzzy logic, particle swarm optimization, data mining, etc. In recent years, the volume of biological data has been increasing exponentially, thus, allowing significant learning and experimentation to be carried out using a multidisciplinary approach, which gives rise to many challenging problems. Bioinformatics has become an ideal research area where computer scientists can apply and further develop new intelligent computation methods, in both experimental and theoretical cases. The overall aim of this special issue is to bring together the latest applications and reviews of innovative intelligent computation tools for processing, management, analysis, interpretation , and integration of biological information. There were more than 20 papers submitted to this special issue, which covered both the practical and theoretical aspects of bioinformatics. After a rigorous peer-review process, six papers have been selected that emphasize what kind of intelligent computation tools are applied, and how these tools are exploited to solve the addressed biological data analysis problem. Medical databases have accumulated large quantities of information about patients and their clinical conditions. Relationships and patterns hidden in this data can provide new medical knowledge as has been proved in a number of medical data mining applications. However, the data are rarely provided in a format suitable for immediate application of conventional attribute-valued learning (AVL). In the paper " Sequential Data Mining: A Comparative Case Study in Development of Atherosclerosis Risk Factors " by Kléma et al., the problem of mining temporal and sequential medical data, which usually asks for complex preprocessing, is investigated. The sequences of the events are determined where each event is described by a numeric or sym-blic value and a time stamp. The event types are also shown to be distinguished. The dataset can either be a single sequence, or it can be composed of a number of sequences. Strong sequential patterns are identified, i.e., such as event chains (subsequences) that appear frequently in the dataset, and their interaction is optionally studied with the target event. Microarrays are at the center of a revolution in biotechnology, allowing researchers to simultaneously monitor the expression of tens of thousands of genes. Independent of the platform and the analysis methods …