Download CliDaPa: A new approach for enriching genes expressions using

Mechanism of obtaining unsupervised knowledge to enrich CliDaPa approach
S. González1*, J. Veiga1, V. Robles1, J.M. Peña1 and F. Famili2
(1) Department of Computer Architecture and Technology, Universidad Politécnica de Madrid, Spain
(2) NRC Institute for Information Technology, Ottawa, Canada
e-mail:[email protected], [email protected], [email protected], [email protected], [email protected]
Keywords: CliDaPa, unsupervised, clinical, DNA microarrays, data analysis
Motivation and Aim:
Recalling conclusions obtained from [9] when we compare traditional data uses and CliDaPa
algorithm applied to two or more information sources (e. g. clinical and gene expression data), the
CliDaPa approach improved results on disease classification. However, if we analyze the data used,
we appreciate that the only data that is not easily understandable for expert biologists is gene
expression data. If we obtain new knowledge from gene information, probably we could use it as
new information source and we could improve the CliDaPa executions, improving results too.
Methods and Algorithms:
Sample-based clustering can be obtained using an unsupervised method, Quality Threshold [10],
with gene expression data. For that, it’s necessary to define several features: the distance measure,
the threshold and the minimum number of elements in a cluster. Using Euclidean, Manhattan,
Pearson correlation and Biweight correlation [11] as distance measures, and using 10 different
values of threshold within the range [mean – 2*deviation, mean + 2*deviation], 40 new clusters
can be obtained. This new data is injected as new clinical data from the in data. Thus, when we
execute the CliDaPa algorithm, where the data can be divided using any of these new data, only if
the classification can be improved.
Results:
To validate this proposal, several data sets with clinical and gene expression data (from Van’t Veer,
Van der Vivjer and Brain Cancer datasets) have been used. Several experiments have been carried
out, using an external MxN fold cross validation. Results obtained from this proposal show us a
10% of improvement in the classification, if we compare with regular CliDaPa executions.
Conclusions:
This new approach demonstrates that new unsupervised knowledge can improve and enrich a
supervised classification. Thus, applying to CliDaPa approach, this algorithm gets the fulfillment of
the proposed objectives.
Availability:
CliDaPa and its improvements are available for the research community. For further information,
please, contact the authors.
Acknowledgements:
The authors thankfully acknowledge the computer resources, technical expertise and assistance
provided by the Centro de Supercomputación y Visualización de Madrid (CeSViMa) and the
Spanish Supercomputing Network.
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