While high-throughput technologies are expected to play a critical role in clinical translational research for complex disease diagnosis, the ability to accurately and consistently discriminate disease phenotypes by determining the gene and protein expression patterns as signatures of different clinical conditions remains a challenge in translational bioinformatics. In this study, we propose a novel feature selection algorithm: Multi-Resolution-Test (MRT-test) that can produce significantly accurate and consistent phenotype discrimination across a series of omics data. Our algorithm can capture those features contributing to subtle data behaviors instead of selecting the features contributing to global data behaviors, which seems to be essential in achieving clinical level diagnosis for different expression data. Furthermore, as an effective biomarker discovery algorithm, it can achieve linear separation for high-dimensional omics data with few biomarkers. We apply our MRT-test to complex disease phenotype diagnosis by combining it with state-of-the-art classifiers and attain exceptional diagnostic results, which suggests that our method's advantage in molecular diagnostics. Experimental evaluation showed that MRT-test based diagnosis is able to generate consistent and robust clinical-level phenotype separation for various diseases. In addition, based on the seed biomarkers detected by the MRT-test, we design a novel network marker synthesis (NMS) algorithm to decipher the underlying molecular mechanisms of tumorigenesis from a systems viewpoint. Unlike existing top-down gene network building approaches, our network marker synthesis method has a less dependence on the global network and enables it to capture the gene regulators for different subnetwork markers, which will provide biologically meaningful insights for understanding the genetic basis of complex diseases.