ADI-Minebio: A Graph Mining Algorithm for Biomedical Data
Rodrigo de Sousa Gomide, Cristina Dutra de Aguiar Ciferri, Ricardo Rodrigues Ciferri, Marina Teresa Pires Vieira · Journal of Information and Data Management · 2011
Pablo 0 0 2010-02-26T16:45:00Z 2010-02-26T16:45:00Z 1 128 734 USP 6 1 861 14.0 Normal 0 false false false EN-US JA X-NONE /* Style Definitions */ table.MsoNormalTable {mso-style-name:Table Normal; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-parent:; mso-padding-alt:0cm 5.4pt 0cm 5.4pt; mso-para-margin:0cm; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:10.0pt; font-family:Calibri; mso-ansi-language:EN-US;} Graph mining is concerned with mining frequent subgraph patterns over a collection of graphs, aiming to find novel and useful knowledge. It has being used to analyze data from different domains, sometimes using algorithms tailored for a specific area of knowledge. In this paper, we propose a graph-mining algorithm and its application in the biomedical domain. We introduce the ADI-bio structure, which organizes data from a database with information of a disease’s patient, and also the ADI-Minebio algorithm, which performs a search on the proposed ADI-bio structure to find frequent subgraphs. Our approach is based on the ADI (adjacency index) structure and the ADI-Mine algorithm, but specifies a different structure and hence a new way of analyzing data through this structure. We also present a performance study to show the feasibility of our approach.