当前位置： SCI文献检索 > JOURNAL OF BIOMEDICAL INFORMATICS期刊下所有文献 > Interestingness measures and strategies for mining multi-ontology multi-level association rules from gene ontology annotations for the discovery of new GO relationships.

Interestingness measures and strategies for mining multi-ontology multi-level association rules from gene ontology annotations for the discovery of new GO relationships.

Abstract：

:The Gene Ontology (GO), a set of three sub-ontologies, is one of the most popular bio-ontologies used for describing gene product characteristics. GO annotation data containing terms from multiple sub-ontologies and at different levels in the ontologies is an important source of implicit relationships between terms from the three sub-ontologies. Data mining techniques such as association rule mining that are tailored to mine from multiple ontologies at multiple levels of abstraction are required for effective knowledge discovery from GO annotation data. We present a data mining approach, Multi-ontology data mining at All Levels (MOAL) that uses the structure and relationships of the GO to mine multi-ontology multi-level association rules. We introduce two interestingness measures: Multi-ontology Support (MOSupport) and Multi-ontology Confidence (MOConfidence) customized to evaluate multi-ontology multi-level association rules. We also describe a variety of post-processing strategies for pruning uninteresting rules. We use publicly available GO annotation data to demonstrate our methods with respect to two applications (1) the discovery of co-annotation suggestions and (2) the discovery of new cross-ontology relationships.

journal_name

J Biomed Inform

journal_title

Journal of biomedical informatics

authors

Manda P,McCarthy F,Bridges SM

doi

10.1016/j.jbi.2013.06.012

subject

Has Abstract

pub_date

2013-10-01 00:00:00

pages

849-56

issue

5

eissn

1532-0464

issn

1532-0480

pii

S1532-0464(13)00087-7

journal_volume

46

pub_type

杂志文章

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