Translation of knowledge from basic science to medicine is essential to improving both clinical research and practice. In this translation, high-throughput genomic approaches can greatly accelerate our understanding of molecular mechanisms of diseases. A successful high-throughput genomic study of disease requires, first, comprehensive and efficient platforms to collect genomic data from clinical samples, and second, computational analysis methods that utilize databases of prior biological knowledge together with experimental data to derive clinically meaningful results. In this thesis, we discuss the development of a new microarray platform as well as computational methods for knowledge-based analysis along with their applications in clinical research. First, we and other colleagues have developed a new high-density oligonucleo-tide array of the human transcriptome for high-throughput and cost-efficient analysis of patient samples in clinical studies. This array allows comprehensive examination of gene expression and genome-wide identification of alternative splicing, and also pro-vides assays for coding SNP detection and non-coding transcripts. Compared with high-throughput mRNA sequencing technology, we show that this array is highly re-producible in estimating gene and exon expression, and sensitive in detecting expres-sion changes. In addition, the exon-exon junction feature of this array is shown to im-prove detection efficiency for mRNA alternative splicing when combined with an ap-propriate computational method. We implemented the use of this array in a multi-center clinical program and have obtained comparable levels of high quality and re-producible data. With low costs and high throughputs for sample processing, we antic-ipate that this array platform will have a wide range of applications in high-throughput clinical studies. Second, we investigated knowledge-based methods that utilize prior know-ledge from biology and medicine to improve analysis and interpretation of high-throughput genomic data. We have developed knowledge-based methods to enrich our prior knowledge, illustrate dynamic response to external stimulus, and identify distur-bances in cellular pathways by chemical exposure, as well as discover hidden biological signatures for the prediction of patient outcomes. Finally, we applied a knowledge-based approach in a large scale genomic study of trauma patients. Cooperating with clinical information, prior knowledge improved the interpretation of common and dif-ferential genomic response to injury, and provided efficient risk assessment for patient outcomes. The clinical and genomic data as well as analysis results in this trauma study were systematically organized and provided to research communities as new knowledge of traumatic injury. The microarray platform and knowledge-based methods presented in this thesis provide appropriate research tools for high-throughput translational medicine in a large clinical setting. This thesis is expected to advance understanding and treatment for dis-eases, and finally, improve public health.