Application of artiﬁcial intelligence techniques to the life sciences requires addressing some fundamental issues: eﬀective information representation, its eﬃcient access, analysis and use during decision-making. It is becoming clear that the high dimensionality poses a serious challenge to existing knowledgediscovery and reasoning tools. Most existing tools were developed for relational types of data, which typically have millions of instances but low dimensionality. Life science domains involve tens of thousands of dimensions, which cause most existing tools to either fail or provide outcomes with limited usefulness. Since these domains are characterized by the diversity of representation formalisms used, the complexity and amount of information present, uncertain or missing values, and the evolution of knowledge, it is necessary for an intelligent information system to be ﬂexible and scalable.