Abstract
The complexities of human cancer have frustrated attempts to understand its genetic underpinnings and explain the unpredictable behavior of individual tumors. Advances in robotics and computer science, as well as the sequence data from the human genome project are now allowing us to begin to glean useful information from the simultaneous analysis of thousands of data elements from hundreds of tumors. Through the sophisticated analysis of patterns in these complex data sets, we are seeing clues that we may be able to predict the behavior of individual tumors as well as to define novel biomarkers and therapeutic targets. In this review, we will discuss the application of these high-throughput technologies for the study of human lung cancer.
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