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Using that data, Dai's programmers used MATLAB to perform DNA micro array analysis, which identified genes that were strong predictors of distant metastases. Dai says that the image processing and data analysis tools are good for matrix calculations, and "we did not have to spend time writing the low-level image processing and the basic data analysis routines like vector and matrix calculations," notes Dai.
The programmers then developed an unsupervised, hierarchical clustering algorithm that enabled them to group the patients' tumors based on the dominant expression features. They then developed a classifier based on the genes that carry the prognosis information. They discovered that 70 genes correlated tightly with the patients' outcome, indicating that a prognosis could be determined based on the gene expression profile of the primary tumor.
"One of the key challenges in micro array experiments is image analysis," explains Dai. His team needed an effective means of extracting signal intensities from TIFF images of micro array slides to determine how much of a gene was present in a particular cell. Because the TIFF images are too large and complex to be processed by hand, programmers would need to pre-process the images and design a batch process to extract the relevant data.
The advanced software tools they used helped the researchers eliminate some of those steps and allows them to spend more of their time on analyzing the data and working on finding the gene that can be a predictor of cancer.
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