This paper is a manifesto aimed at computer scientists interested in developing and applying scientific discovery methods. It argues that: science is experiencing an unprecedented "explosion" in the amount of available data; traditional data analysis methods cannot deal with this increased quantity of data; there is an urgent need to automate the process of refining scientific data into scientific knowledge; inductive logic programming (ILP) is a data analysis framework well suited for this task; and exciting new scientific discoveries can be achieved using ILP scientific discovery methods. We describe an example of using ILP to analyse a large and complex bioinformatic database that has produced unexpected and interesting scientific results in functional genomics. We then point a possible way forward to integrating machine learning with scientific databases to form intelligent databases. © Springer-Verlag Berlin Heidelberg 2007.
CITATION STYLE
King, R. D., Karwath, A., Clare, A., & Dehaspe, L. (2007). Logic and the automatic acquisition of scientific knowledge: An application to functional genomics. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4660 LNAI, pp. 273–289). Springer Verlag. https://doi.org/10.1007/978-3-540-73920-3_13
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