Preface to Part VI Biologically Inspired Paradigms

An unprecedented wealth of biological data has been generated by recently developed technologies that measure biomolecules, notably nucleic acids and proteins. The demands for pattern discovery in these data have stimulated advances in computational intelligence (CI) and statistical learning theory (SLT) and particularly one of their subclasses: biologically inspired paradigms. Bioinformatics and biomedical informatics are generally accepted to be the application of tools of computation and analysis to the capture and interpretation of biological and medical data. These are interdisciplinary fields, encompassing computer science, mathematics, physics, statistical learning theory, computational intelligence, medicine and biology. Some of the prospects in bioinformatics and biomedical fields include future contributions to: (a.) a functional understanding of the human genome, which may well lead to (b.) enhanced drug discoveries as well as individualizing patient therapy, and (c.) integration and classification and predictive clinical decision support systems (CDSS) as well as other applications, which have their origin in CI and SLT. The very complexity of interpreting biological/biomedical data has stimulated the borrowing of ideas inspired by Nature’s own creations such as neural networks, evolutionary computation, swarm search, ant colony search, to name a few – biologically inspired paradigms. These paradigms, of course, are not limited only to biological/biomedical applications. Complexity is where you find it.