Conclusion

Abstract

A summary is presented of the concluding observations from previous chapters in which it is noted that complexity underpins chemistry, physicochemical properties, pharmaceutical manufacturing, preclinical observations, and population biology (of clinical relevance) and has implications for machine learning and artificial intelligence. For each of these, topic tools or mathematical interpretations have been presented. The concluding observation for the volume, as it appeared in the first edition, appears to be somewhat prescient. Rather than the typical data-to-wisdom overlay that has been placed on ontologies emerging from the so-called big data revolution, it is clear that there are feedback elements from wisdom to knowledge and information such that rather than drawing heuristic inferences from existing data, we can take a holistic view of the entire framework to allow the most meaningful interpretation. This seems to be a key characteristic of all data analytic approaches that are underway in science and society currently.

An all-too-pervasive modern myth holds that science consists in the discovery and collection of authoritative facts or truths. This myth stands in contrast to the experience of all genuine scientists, that ours is really just the passionate quest for what nature has to tell us. – Victor R. Baker, University of Arizona, The Times Literary Supplement, July 17, 2009