Abstract #301887

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JSM 2003 Abstract #301887
Activity Number: 283
Type: Topic Contributed
Date/Time: Tuesday, August 5, 2003 : 2:00 PM to 3:50 PM
Sponsor: Biopharmaceutical Section
Abstract - #301887
Title: Mining Gene Expression Data and DNA Sequence Database: A Case Study of in Silico Target Identification
Author(s): Nanxiang Ge*+ and Hong Liu
Companies: Aventis and
Address: 2 Tulip Dr., Newtown, PA, 18940-9265,
Keywords: data mining ; logistics regression ; motif ; ROC curve ; mixed-effect model ; genome scan
Abstract:

With the availability of genome sequence data and large amount of gene expression data, one important question in industry is how to transform this huge amount of data into something tangible. We present a case study on how statistical methods can be used to mine these available large databases. Two type of data are considered in this talk, gene expression data and transcription factor binding site pattern in the gene regulatory region. A mixed-effect mode was first used to analyze the gene expression data to identify genes that are either regulated or nonregulated. Score showing a particular motif appearing in the promoter region of these genes are also generated. A statistical model was then developed relating the regulation to the motif pattern in the promoter region. This statistical model was then applied to scan the whole genome with the goal to identify more genes that are regulated. In this case study, the genome scan helped identify 138 putative regulated genes. Down stream validation suggesting good predictive power of the model.

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