Finding frequent substructures in chemical compounds
Year: 1998
Type: conference-paper
Abstract: The discovery of the relationships between chemical structure and biological function is central to biological science and medicine.In this paper we apply data mining to the problem of predicting chemical carcinogenicity.This toxicology application was launched at IJCAI'97 as a research challenge for arti cial intelligence.Our approach to the problem is descriptive rather than based on classi cation; the goal being to nd common substructures and properties in chemical compounds, and in this way to contribute to scienti c insight.This approach contrasts with previous machine learning research on this problem, which has mainly concentrated on predicting the toxicity of unknown chemicals.Our contribution to the eld of data mining is the ability to discover useful frequent patterns that are beyond the complexity of association rules or their known variants.This is vital to the problem, which requires the discovery of patterns that are out of the reach of simple transformations to frequent itemsets.We present a knowledge discovery method for structured data, where patterns re ect the one-to-many and many-to-many relationships of several tables.Background knowledge, represented in a uniform manner in some of the tables, has an essential role here, unlike in most data mining settings for the discovery of frequent patterns.
Source: Lirias
Authors: Luc De Raedt, Hannu Toivonen, Ross D. King
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W202299441