Variation of Background Knowledge in an Industrial Application of ILP

Abstract

Abstract. In several recent papers ILP has been applied to Systems Biology problems, in which it has been used to fill gaps in the descriptions of biological networks. In the present paper we describe two new applications of this type in the area of plant biology. These applications are of particular interest to the agrochemical industry in which improvements in plant strains can have benefits for modelling crop development. The background knowledge in these applications is extensive and is derived from public databases in a Prolog format using a new system called Ondex (developers BBSRC Rothamsted). In this paper we explore the question of how much of this background knowledge it is beneficial to include, taking into account accuracy increases versus increases in learning time. The results indicate that relatively shallow background knowledge is needed to achieve maximum accuracy.

Does Multi-Clause Learning Help in Real-World Applications?

“…Second, the explanations for each example inevitably involve multiple reaction states, which will be explained later in section 3. The same applications were also used in [12] to study how varying the background knowledge affects the accuracy, but the modelling has been extended by better way of using transcriptomic data.…”