Translational Bioinformatics for Disease Diagnosis and Treatment Planning
Research Abstract
Microarray technology for measuring gene expression values has created significant opportunities for advances in
disease diagnosis and individualized treatment planning. However,
the random noise introduced by the sample preparation,
hybridization, and scanning stages of microarray processing
creates significant inaccuracies in the gene expression levels,
and hence presents a major barrier in realizing the anticipated
advances. Literature presents several methodologies for noise
reduction, which can be broadly categorized as: 1) model based
approaches for estimation and removal of hybridization noise, 2)
approaches using commonly available image denoising tools, and
3) approaches involving the need for control sample(s). In this
research, we present a novel methodology for identifying and removing
hybridization and scanning noise from microarray images,
using a dual tree complex wavelet transform based multiresolution
analysis coupled with bivariate shrinkage thresholding. The key
features of our methodology include consideration of inherent
features and type of noise specific to microarray images, and
the ability to work with a single microarray without needing a
control. Our methodology is first benchmarked on a fabricated
data set that mimics a real microarray probe data set. Thereafter,
our methodology is tested on data sets obtained from a number
of Affymetrix GeneChipr human genome HG-U133 Plus 2.0
arrays, processed on HCT-116 cell line at the Microarray Core
Facility of Moffitt Cancer Center and Research Institute. The
results indicate an appreciable improvement in the quality of the
microarray data.
Publications
Conference Presentations
- “A Novel Approach for Denoising Gene Expression Values Obtained from Microarrays", Presentation made in the Quality, Statistics, and Reliability (QSR) section at an Invited Session at INFORMS annual meeting at Pittsburgh , PA , November 2006.
- "A Multiresolution Approach to Microarray Denoising for Efficient Disease Diagnosis and Drug Discovery”, Poster Presentation made during “Engineering Research Day” at University of South Florida , Tampa , Spring 2007.
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