Discovering Implicit Associations among Critical Biological Entities.
K. Seki, and J. Mostafa. International Journal of Data Mining and Bioinformatics, 3 (2):
105-123(2009)Inference network model, diseases and genes as input..
Abstract
We propose an approach to predicting implicit gene-disease associations
based on the inference network, whereby genes and diseases are represented
as nodes and are connected via two types of intermediate nodes: gene functions
and phenotypes. To estimate the probabilities involved in the model, two learning
schemes are compared; one baseline using co-annotations of keywords and the
other taking advantage of free text. Additionally, we explore the use of domain
ontologies to complement data sparseness and examine the impact of full text documents.
The validity of the proposed framework is demonstrated on the benchmark
data set created from real-world data.
%0 Journal Article
%1 gene.disease.seki.2009
%A Seki, Kazuhiro
%A Mostafa, Javed
%D 2009
%J International Journal of Data Mining and Bioinformatics
%K CAT CAT-REL-COOR CAT-REL-STAT-RANK CAT-REL-SWANSON disease gene mining relationship text
%N 2
%P 105-123
%T Discovering Implicit Associations among Critical Biological Entities.
%U http://www.ai.cs.kobe-u.ac.jp/~kseki/myarticles/seki2009ijdmb.pdf
%V 3
%X We propose an approach to predicting implicit gene-disease associations
based on the inference network, whereby genes and diseases are represented
as nodes and are connected via two types of intermediate nodes: gene functions
and phenotypes. To estimate the probabilities involved in the model, two learning
schemes are compared; one baseline using co-annotations of keywords and the
other taking advantage of free text. Additionally, we explore the use of domain
ontologies to complement data sparseness and examine the impact of full text documents.
The validity of the proposed framework is demonstrated on the benchmark
data set created from real-world data.
@article{gene.disease.seki.2009,
abstract = {We propose an approach to predicting implicit gene-disease associations
based on the inference network, whereby genes and diseases are represented
as nodes and are connected via two types of intermediate nodes: gene functions
and phenotypes. To estimate the probabilities involved in the model, two learning
schemes are compared; one baseline using co-annotations of keywords and the
other taking advantage of free text. Additionally, we explore the use of domain
ontologies to complement data sparseness and examine the impact of full text documents.
The validity of the proposed framework is demonstrated on the benchmark
data set created from real-world data.
},
added-at = {2009-06-27T03:04:57.000+0200},
author = {Seki, Kazuhiro and Mostafa, Javed},
biburl = {https://www.bibsonomy.org/bibtex/20af16a8abb863e6e8617c1f086602ab8/huiyangsfsu},
interhash = {7d75abe08c647bb34927125de4700927},
intrahash = {0af16a8abb863e6e8617c1f086602ab8},
journal = {International Journal of Data Mining and Bioinformatics},
keywords = {CAT CAT-REL-COOR CAT-REL-STAT-RANK CAT-REL-SWANSON disease gene mining relationship text},
note = {Inference network model, diseases and genes as input. },
number = 2,
pages = {105-123},
timestamp = {2010-11-12T05:00:59.000+0100},
title = {Discovering Implicit Associations among Critical Biological Entities.},
url = {http://www.ai.cs.kobe-u.ac.jp/~kseki/myarticles/seki2009ijdmb.pdf },
volume = 3,
year = 2009
}