Biomedical subjects

Winston Patrick Kuo

Publications and source records attributed to Winston Patrick Kuo.

10 recordsLinked to original sources

PubMed2006

A sequence-oriented comparison of gene expression measurements across different hybridization-based technologies.

Over the last decade, gene expression microarrays have had a profound impact on biomedical research. The diversity of platforms and analytical methods available to researchers have made the comparison of data from multiple platforms challenging. In this study, we describe a framework for comparisons across platforms and laboratories. We have attempted to include nearly all the available commercial and 'in-house' platforms. Using probe sequences matched at the exon level improved consistency of measurements across the different microarray platforms compared to annotation-based matches. Generally, consistency was good for highly expressed genes, and variable for genes with lower expression values as confirmed by quantitative real-time (QRT)-PCR. Concordance of measurements was higher between laboratories on the same platform than across platforms. We demonstrate that, after stringent preprocessing, commercial arrays were more consistent than in-house arrays, and by most measures, one-dye platforms were more consistent than two-dye platforms.

Chromosome Mapping↗

PubMed2004

A primer on gene expression and microarrays for machine learning researchers.

Data originating from biomedical experiments has provided machine learning researchers with an important source of motivation for developing and evaluating new algorithms. A new wave of algorithmic development has been initiated with the publication of gene expression data derived from microarrays. Microarray data analysis is particularly challenging given the large number of measurements (typically in the order of thousands) that are reported for relatively few samples (typically in the order of dozens). Many data sets are now available on the web. It is important that machine learning researchers understand how data are obtained and which assumptions are necessary in the analysis. Microarray data have the potential to cause significant impact in machine learning research, not just as a rich and realistic source of cases for testing new algorithms, as has been the UCI machine learning repository in the past decades, but also as a main motivation for their development. In this article, we briefly review the biology underlying microarrays, the process of obtaining gene expression measurements, and the rationale behind the common types of analyses involved in a microarray experiment. We outline the main challenges and reiterate critical considerations regarding the construction of supervised learning models that use this type of data. The goal of this article is to familiarize machine learning researchers with data originated from gene expression microarrays.

Algorithms↗

PubMed2004

Deciphering gene expression profiles generated from DNA microarrays and their applications in oral medicine.

Genome-wide monitoring of gene expression profiles using DNA microarrays provides a unique approach to exploring the biological processes underlying oral diseases and disorders by providing a comprehensive survey of a cell's or tissue's transcriptional mapping. This revolutionary technology allows for the simultaneous assessment of the transcription levels of tens of thousands of genes, and of their relative expression between normal and diseased cells. As microarray data analysis evolves, there is a widespread hope that microarrays will significantly impact our ability to explore the genetic changes associated with disease etiology and development, ultimately leading to the discovery of new biomarkers for disease diagnosis and prognosis prediction as well as new therapeutic tools. The goal of this manuscript is to review 2 of the most commonly used microarray technologies, provide an overview of data analyses involved in a typical microarray experiment, and comment upon the application of microarrays to oral medicine.

Biomarkers↗

PubMed2003

Microarrays and clinical dentistry.

BACKGROUND: The Human Genome Project, or HGP, has inspired a great deal of exciting biology recently by enabling the development of new technologies that will be essential for understanding the different types of abnormalities in diseases related to the oral cavity. LITERATURE REVIEWED: The authors review current literature pertaining to the advanced microarray technologies arising from the HGP and how they can contribute to dentistry. This technology has become a standard tool for monitoring activities of genes at both academic and pharmaceutical research institutions. RESULTS: With the availability of the DNA sequences for the entire human genome, attention now is focused on understanding various diseases at the genome level. Deciphering the molecular behavior of genetically encoded proteins is crucial to obtaining a more comprehensive picture of disease processes. Important progress has been made using microarrays, which have been shown to be effective in identifying gene expression patterns and variations that correlate with cellular development, physiology and function. Arrays can be used to classify tissue samples accurately based on molecular profiles and to select candidate genes related to a number of cancers, including oral cancer. This type of oral genetic approach will aid in the understanding of disease progression, thus improving diagnosis and treatment for patients. CLINICAL IMPLICATIONS: Microarrays hold much promise for the analysis of diseases in the oral cavity. As the technology evolves, dentists may see these tools as screening tests for better managing patients' dental care.

Anti-Bacterial Agents↗

PubMed2003

Classification and identification of genes associated with oral cancer based on gene expression profiles. A preliminary study.

Oral squamous cell carcinoma (OSCC) is an aggressive malignancy. The five-year survival rate remains largely unchanged for the past 40 years. Early diagnosis has been shown to correlate with increased survival, based on cytologic changes. In order to improve our current treatment strategies for OSCC, it is necessary to understand the genetic and molecular networks underlying this disease. In this preliminary study, we illustrate the application of DNA microarrays to study OSCC. Using computational and statistical algorithms, we were able to differentiate (or classify) "cancer" and "normal" samples based on the behavior of the gene expression profiles. We found 651 genes to be associated with cancer. This article describes a preliminary study of current developments from the Human Genome Project (HGP) and its application to OSCC.

Carcinoma, Squamous Cell↗

PubMed2003

Functional relationships between gene pairs in oral squamous cell carcinoma.

We developed a novel method for the discovery of functional relationships between pairs of genes based on gene expression profiles generated from microarrays. This approach examines all possible pairs of genes and identifies those in which the relationship between the two genes changes in different diseases or conditions. In contrast to previous methods that have focused on differentially expressed genes, this method attempts to find changes in the correlation between genes. These changes may be indicative of the functional relationships related to a disease mechanism. We demonstrate the utility of this approach by applying it to an oral squamous cell carcinoma (OSCC) microarray data set. Our results suggest new directions for future experimental investigations.

Carcinoma, Squamous Cell↗

PubMed2002

Gene expression profiling by DNA microarrays and its application to dental research.

UNLABELLED: DNA microarray technology has been used for genome-wide gene expression studies that incorporate molecular genetics and computer science skills on massive levels. The technology permits the simultaneous analysis of tens of thousands of genes for the purposes of gene discovery, disease diagnosis. improved drug development, and therapeutics tailored to specific disease processes. OBJECTIVE: In this review, the two most common microarray technologies and their potential application to dental research will be discussed. The authors review current articles pertaining to the technologies and analysis of mRNA expression using DNA micro-arrays and its application to dental research. Since many genes contribute to normal functioning, research efforts are moving from the search for a disease specific gene to the understanding of the biochemical and molecular functioning of a variety of genes and how complicated networks of interaction can lead to a disease state, such as oral cancer. With the incorporation of DNA micro-array based research, we can look forward to more accurate diagnosis and surgical treatment/drug-delivery therapy based on an individual patient's genetic profile.

Carcinoma, Squamous Cell↗

PubMed2002

DNA microarrays in otolaryngology-head and neck surgery.

OBJECTIVES: Our goal was to review the technologies underlying DNA microarrays and to explore their use in otolaryngology-head and neck surgery. STUDY DESIGN: The current literature relating to microarray technology and methodology is reviewed, specifically the use of DNA microarrays to characterize gene expression. Bioinformatics involves computational and statistical methods to extract, organize, and analyze the huge amounts of data produced by microarray experiments. The means by which these techniques are being applied to otolaryngology-head and neck surgery are outlined. RESULTS: Microarray technologies are having a substantial impact on biomedical research, including many areas relevant to otolaryngology-head and neck surgery. CONCLUSIONS: DNA microarrays allow for the simultaneous investigation of thousands of individual genes in a single experiment. In the coming years, the application of these technologies to clinical medicine should allow for unprecedented methods of diagnosis and treatment. SIGNIFICANCE: These highly parallel experimental techniques promise to revolutionize gene discovery, disease characterization, and drug development.

Gene Expression Profiling↗

PubMed2002

Analysis of matched mRNA measurements from two different microarray technologies.

MOTIVATION: [corrected] The existence of several technologies for measuring gene expression makes the question of cross-technology agreement of measurements an important issue. Cross-platform utilization of data from different technologies has the potential to reduce the need to duplicate experiments but requires corresponding measurements to be comparable. METHODS: A comparison of mRNA measurements of 2895 sequence-matched genes in 56 cell lines from the standard panel of 60 cancer cell lines from the National Cancer Institute (NCI 60) was carried out by calculating correlation between matched measurements and calculating concordance between cluster from two high-throughput DNA microarray technologies, Stanford type cDNA microarrays and Affymetrix oligonucleotide microarrays. RESULTS: In general, corresponding measurements from the two platforms showed poor correlation. Clusters of genes and cell lines were discordant between the two technologies, suggesting that relative intra-technology relationships were not preserved. GC-content, sequence length, average signal intensity, and an estimator of cross-hybridization were found to be associated with the degree of correlation. This suggests gene-specific, or more correctly probe-specific, factors influencing measurements differently in the two platforms, implying a poor prognosis for a broad utilization of gene expression measurements across platforms.

Cluster Analysis↗

PubMed2002

Gene expression levels in different stages of progression in oral squamous cell carcinoma.

Oral squamous cell carcinoma (OSCC) is one of the most common cancer types worldwide. The prognosis for patients with this disease is generally poor and little is known about its progression. Gene expression studies may provide important insights to the molecular mechanisms of this disease. We analyzed gene expression data from a small panel of patients diagnosed with OSCC. Even with only 13 patient samples we were able to find genes with significant differences in expression levels between normal, dysplasia, and cancer samples. The largest differences in expression were generally found between normal and cancer samples, but significant differences were also found for several genes between dysplasia and the other two sample types. We also represent the significance levels of differentially expressed genes on the chromosome domain. The genes and genetic features we examine are potentially important factors on the molecular level in the progression of OSCC.

Carcinoma, Squamous Cell↗