Knowledge discovery and management in life sciences: Impacts and challenges

摘要

Knowledge discovery, the process by which one analyzes large data sets in search of patterns that result in useful knowledge, has emerged as a fundamental solution in understanding the real value of today''s data that we collect. Many examples exist, among them are knowledge discovery in life sciences, physical systems (e.g. sensor-based systems) and financial domain. The exponential growth of databanks has also created opportunities to further expand research topics such as Operational Research from a data mining point of view. An example is the development of scientific approaches to intelligently "mine" the huge databanks that complex systems rely on for their management. Of the more complex of all domains is the life sciences where one tries to integrate and analyze large amounts of high-throughput genomics and proteomics data obtained from either single time point or time-series applications. Similar to many other domains, in life sciences various methods have been developed, and many data mining tools (commercial, non-commercial) have been introduced. These applications have contributed to problems such as: (i) identification of certain genes or proteins and their functions, (ii) gene response analysis in biological studies, such in-vitro, in-vivo or x-vivo, research and (iii) understanding the molecular mechanism of certain species and their associated biological pathways. This wealth of newly discovered and existing knowledge has prompted a question: what is the best way to properly manage all discovered knowledge, when it is validated. This question has also been one of the motivations behind several data mining research projects in many institutes. Here, in addition to searching for patterns from large data sets, one tries to identify proper ways to represent, structure, and distribute all forms of knowledge, most preferably taking an Artificial Intelligence approach.