Abstract
It is increasingly clear that machine learning algorithms need to be integrated in an iterative scientific discovery loop, in which data is queried repeatedly by means of inductive queries and where the computer provides guidance to the experiments that are being performed. In this chapter, we summarise several key challenges in achieving this integration of machine learning and data mining algorithms in methods for the discovery of Quantitative Structure Activity Relationships (QSARs). We introduce the concept of a robot scientist, in which all steps of the discovery process are automated; we discuss the representation of molecular data such that knowledge discovery tools can analyse it, and we discuss the adaptation of machine learning and data mining algorithms to guide QSAR experiments. © 2010 Springer Science+Business Media, LLC.
Original language | English |
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Title of host publication | Inductive Databases and Constraint-Based Data Mining|Inductive Databases and Constraint-Based Data Min. |
Publisher | Springer Nature |
Pages | 425-451 |
Number of pages | 26 |
DOIs | |
Publication status | Published - 2010 |
Keywords
- Robot Scientist