A multi-tier data mining workflow to analyze the age related shift from diglycosylated- to tetra-glycosylated-FSH secretion by the anterior pituitary
Date
2010Author
Visvanathan, Mahesh
Rao, Shanker
Anand, Vijay
Lushington, Gerald H.
Netzer, Michael
Baumgartner, Christian
Bousfield, George R.
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Mahesh Visvanathan, Shanker Rao, Vijay Anand, Gerald H Lushington, Michael Netzer, Christian Baumgartner, George R Bousfield; A Multi-Tier Data Mining Workflow to Analyze the Age Related Shift from Diglycosylated- to Tetra-glycosylated-FSH Secretion by the Anterior Pituitary; BCB '10 Proceedings of the First ACM International Conference on Bioinformatics and Computational Biology Pages 508-514; ISBN: 978-1-4503-0438-2 doi>10.1145/1854776.1854874
Abstract
FSH is a glycoprotein hormone secreted as two major
glycosylation variants by the anterior pituitary, which regulates
reproduction in adults. As FSH consists of two functionally
significant glycoforms, differentially expressed genes related to
FSH biosynthesis in the anterior pituitary can help us to
understand implications of changes in their relative abundance
at the genomic level. Mapping these kinds of biomarker genes
and their corresponding pathways is a key technology for
studying the elaboration of FSH variants that affect the
reproductive system. In this paper we use a multiple tier data
mining work flow to identify FSH biosynthesis-related genes in
the anterior pituitary. Our methodology combines different filterbased feature selection mechanisms like Linear Regression (LR),
Z-Score statistics and the Biomarker Identifier (BMI).
Consequently, we identified differentially expressed genes in
response to the synthetic estrogen, diethylstilbestrol (DES),
treatment in male rats. As a next step, we performed pathway
analysis to identify the most relevant metabolic pathways
associated with a set of identified genes in a pathway. Finally,
we applied Mutual Information (MI) to calculate the measure of
association between differentially expressed genes and several
biosynthetic and signaling pathways of interest.
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