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For classification problems based on microarray data, the data typically contains a large number of irrelevant and redundant features. In this paper, a new gene selection method is proposed to choose the best subset of features for microarray data with the irrelevant and redundant features removed. We formulate the selection problem as a L1-regularized(More)
Inferring the topology of gene regulatory networks (GRNs) from microarray gene expression data has many potential applications, such as identifying candidate drug targets and providing valuable insights into the biological processes. It remains a challenge due to the fact that the data is noisy and high dimensional, and there exists a large number of(More)
Mycorrhizal fungi colonize orchid seeds and induce germination. This so-called symbiotic germination is a critical developmental process in the lifecycle of all orchid species. However, the molecular changes that occur during orchid seed symbiotic germination remain largely unknown. To better understand the molecular mechanism of orchid seed germination, we(More)
Titanium and its alloys have become the most attractive implant materials due to their high corrosion resistance, excellent biocompatibility and relatively low elastic modulus. However, the current Ti materials used for implant applications exhibit much higher Young's modulus (50 ~ 120 GPa) than human bone (~30 GPa). This large mismatch in the elastic(More)
Dimension reduction is a crucial technique in machine learning and data mining, which is widely used in areas of medicine, bioinformatics and genetics. In this paper, we propose a two-stage local dimension reduction approach for classification on microarray data. In first stage, a new L1-regularized feature selection method is defined to remove irrelevant(More)
In this paper, we propose a novel dimension reduction algorithm that implements an information fusion of Centroid-based feature selection and partial least squares (PLS) based feature extraction. This paper focuses on mining the potential information hidden in multiclass microarray data and interpreting the results provided by the potential information.(More)
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