background classification of cancer subtypes by means of microarray signatures is becoming increasingly difficult to ignore as a potential to transform pathological diagnosis nonetheless, measurement of indicator genes in routine practice appears to be arduous. in a preceding published study, we utilized real-time pcr measurement of indicator genes in acute lymphoid leukaemia (all) and acute my...

b a c k g r o u n d  & aim: it is very helpful to classify and predict the clinical  category  of a sample based  on  its  gene  expression  profile.  this  study  was  conducted  to predict  tissues  of colorectal adenoma,  adenocarcinoma,  and  paired  normal  in  colon  based  on  microarray  data  using  nearest shrunken centroid method. methods   &  materials:    in  this  study,   the  co...

Quantitative risk assessment and the elucidation of mechanisms of toxicity requires computational infrastructure and innovative analysis approaches that systematically consider available data at all levels of biological organization. dbZach (http://dbzach.fst.msu.edu) is a modular relational database with associated data insertion, retrieval, and mining tools that manages traditional toxicology...

In this article, we study two microarray data integration techniques and describe how they can be applied and validated on a set of independent, but biologically related, microarray data sets in order to derive consistent and relevant clustering results. First, we present a cluster integration approach, which combines the information containing in multiple data sets at the level of expression o...

An initiative to increase biopharmaceutical research productivity by capturing, sharing and computationally integrating proprietary scientific discoveries with public knowledge is described. This initiative involves both organisational process change and multiple interoperating software systems. The software components rely on mutually supporting integration techniques. These include a richly s...

The size and scope of microarray experiments continue to increase. However, datasets generated on different platforms or at different centres contain biases. Improved techniques are needed to remove platform- and batch-specific biases. One experimental control is the replicate hybridization of a subset of samples at each site or on each platform to learn the relationship between the two platfor...

DNA microarray is a highly-parallel methodology to screen genome samples for the expression of particular DNA sequences [1]. To gather information about gene expression is a much more difficult task than DNA sequence, because gene expression is a dynamic process dependent on many factors. The knowledge of the ORFs (Open Reading Framework) for a certain organism opens the possibility to globally...

Microarray technologies allow monitoring of gene activity (or expression) of thousands of genes in parallel at the same time. Although very powerful, their complexity makes the observed data very difficult to integrate across experiments. In the first part of this paper I will introduce the microarray technology and several of our bioinformatics approaches for integrating multiple gene expressi...

1Cancer Genetics Branch, National Human Genome Research Institute, 49 Convent Drive, Bethesda, Maryland 20892 USA. 2Electrical Engineering Department, Texas A&M University, College Station, Texas 77843, USA. There are many statistical issues associated with the analysis and integration of microarray data. Here we touch on a few basic issues that relate to the current stage of the technology. Ou...

The completion of the human genome project has revolutionized the field of Biology. Vast amounts of information have been yielded which in turn requires high-throughput and large scale technologies to manipulate the data. DNA microarray technology is emerging as a powerful way to identify genetic disorders, drug discovery and toxicology, mutations, disease diagnosis etc. The potential applicati...