LogCHEM: interactive discriminative mining of chemical structure

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2008

Autores

Vítor Santos Costa (Autor) FCUP; Nuno A. Fonseca (Autor) Outra; Rui Camacho (Autor) FEUP

Ata de Conferência Internacional

Título: 2008 IEEE INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOMEDICINE, PROCEEDINGS

Páginas: 421-426

IEEE International Conference on Bioinformatics and Biomedicine

Philadelphia, PA, NOV 03-05, 2008

Indexação

ISI Web of Knowledge - 3 Citações

Scopus - 3 Citações

INSPEC

Classificação Científica

FOS: Ciências exactas e naturais > Matemática

CORDIS: Ciências Tecnológicas > Engenharia > Engenharia do conhecimento ; Ciências Tecnológicas > Tecnologia > Tecnologia farmacêutica

Outras Informações

ID Authenticus: P-004-5AB

DOI: 10.1109/bibm.2008.45

Abstract (EN): One of the most well known successes of Inductive Logic Programming (ILP) is on Structure-Activity Relationship (SAR) problems. In such problems, ILP has proved several times to be capable of constructing expert comprehensible models that hell) to explain the activity of chemical compounds based on their structure and properties. However, despite its successes on SAR problems, ILP has severe scalability problems that prevent its application oil larger datasets. In this paper we present LogCHEM, an ILP based tool for discriminative interactive mining of chemical fragments. LogCHEM tackles ILP's scalability issues in the context of SAR applications. We show that LogCHEM benefits from the flexibility of ILP both by its ability to quickly extend the original mining model, and by its ability, to interface with external tools. Furthermore, We demonstrate that LogCHEM can be used to mine effectively large chemoinformatics datasets, namely, several datasets from EPA's DSSTox database and on a dataset based on the DTP AIDS anti-viral screen.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 6