生物資訊與人工智慧的發展現況與展望

The research in molecular biology is becoming ever more database driven, motivated nowadays, in part, by the introduction of large-scale functional genomics and proteomics experiments such as those comprehensively measuring gene expression. These provide abundant information on thousands of proteins which encoded by genome. Consequently, a challenge in bioinformatics is integrating databases to connect this unrelated information as well as performing large-scale studies to collectively analyze many different data sets. This approach represents an example shift away from traditional biology, and it often involves statistical analyses focusing on the occurrence of particular features (e.g., protein functions, foldings, interactions, pseudogenes, or localization) in genomics and proteomics.
In addition, the application of computing intelligence techniques can be used to discover trends and patterns in the primary data. In this article, author gave several examples of these techniques in a genomic and proteomic context: clustering methods to organize microarray expression data, support vector machines to predict protein function, Bayesian networks to predict sub-cellular localization, and decision trees to optimize target selection for high-throughput proteomics.