bbc 2015

BeNeLux Bioinformatics Conference – Antwerp, December 7-8 2015
Abstract ID: P
Poster
10th Benelux Bioinformatics Conference bbc 2015
P52. SUPERVISED TEXT MINING FOR DISEASE AND GENE LINKS
Jaak Simm 1,2,3* , Adam Arany 1,2 , Sarah ElShal 1,2 & Yves Moreau 1,2 .
Department of Electrical Engineering (ESAT), STADIUS Center for Dynamical Systems, Signal Processing, and Data
Analytics, KU Leuven, Kasteelpark Arenberg 10, box 2446, 3001 Leuven, Belgium 1 ; iMinds Medical IT, Kasteelpark
Arenberg 10, box 2446, 3001 Leuven, Belgium 2 ; Institute of Gene Technology, Tallinn University of Technology,
Akadeemia tee 15A, Estonia 3 . * jaak.simm@esat.kuleuven.be
Scientific publications contain rich information about genetic disorders. Text mining these publications provides an
automatic way to quickly query and summarize the information. We propose a supervised learning approach that takes
advantage of the well known unsupervised approach TF-IDF (term frequency–inverse document frequency) and
integrates it with supervised approach using logistic loss error metric. The preliminary results on OMIM dataset look
promising.
INTRODUCTION
Scientific publications contain rich information about
genetic disorders. Text mining these publications provides
an automatic way to quickly query and summarize the
information.
The traditional approaches employ unsupervised text
mining approaches like TF-IDF (term frequency–inverse
document frequency) or Latent Dirichlet Allocation
(LDA) by Blei et al. (2003) for linking terms to genes and
diseases. A recent text mining software Beegle (ElShal et
al., 2015) developed for linking diseases and genes has
taken this approach using TF-IDF as its similarity metric.
PROPOSED METHOD
Our work proposes a supervised learning of the
importance of the textual terms, which can automatically
filter out many terms that are unnecessary for the task at
hand. We formulate it as a prediction of supervised values
y given the terms for all genes g and all diseases d where i
is the index of the term:
and w i is the weight for the term i and σ is sigmoid
function. The main idea is to learn the weight vector w that
minimizes the difference between known values y and
predictions. The minimization can transformed into a
logistic regression.
For the supervised values we use OMIM database
(Hamosh et al., 2003). More specifically y corresponds to
1 if there is a link between the given gene-disease pair and
0 if there is no link. Intuitively, in this setup the text
mining is transformed into a classification problem. We
use dataset of 330 OMIM terms and their linked genes and
randomly sample genes as negatives for each disease.
For the textual terms we use MEDLINE abstracts as the
source of biomedical text. We employ MetaMap (Aronson
et al. 2010) to link terms with abstracts. We use geneRIF
to link genes with abstracts, and PubMed to link diseases
with abstracts. We apply a TF-IDF transformation to score
a term with a given disease or gene based on the abstracts
linked to each entity. We only use the terms linked to
abstracts that belong to genes. Hence our vocabulary
consists of 66,883 terms.
RESULTS & DISCUSSION
The preliminary results show that supervised learning
allows to automatically pick up the keywords that are
informative, improving the recall of the genes that are
related to genetic disorders. We will present more detailed
results in the poster.
We are also investigate how to integrate the supervised
approach to have answers to online queries provided by
Beegle.
REFERENCES
Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent dirichlet
allocation. the Journal of machine Learning research, 3, 993-1022.
Hamosh, A., Scott, A. F., Amberger, J. S., Bocchini, C. A., & McKusick,
V. A. (2005). Online Mendelian Inheritance in Man (OMIM), a
knowledgebase of human genes and genetic disorders. Nucleic acids
research, 33(suppl 1), D514-D517.
ElShal, S., Tranchevent L.C., Sifrim A., Ardeshirdavani A., Davis J.,
Moreau Y. (2015). Beegle: from literature mining to disease-gene
discovery. Nucleic Acids Res, gkv905.
Aronson, A. R., & Lang, F. M. (2010). An overview of MetaMap:
historical perspective and recent advances. Journal of the American
Medical Informatics Association, 17(3), 229-236.
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