Malignancies and diseases of various genetic origins can be diagnosed and classified with microarray data. There are many obstacles to overcome due to the large size of the gene and the small number of samples in the microarray. A combination strategy for gene expression in a variety of diseases is described in this paper, consisting of two steps: identifying the most effective genes via soft ensembling and classifying them with a novel deep neural network. The feature selection approach combines three strategies to select wrapper genes and rank them according to the k-nearest neighbour algorithm, resulting in a very generalisable model with low error levels. Using soft ensembling, the most effective subsets of genes were identified from three microarray datasets of diffuse large cell lymphoma, leukaemia, and prostate cancer. A stacked deep neural network was used to classify all three datasets, achieving an average accuracy of 97.51%, 99.6%, and 96.34%, respectively. In addition, two previously unreported datasets from small, round blue cell tumors (SRBCTs)and multiple sclerosis-related brain tissue lesions were examined to show the generalisability of the model method.
An alumina support system for cobalt (II) acetylacetonate (Co(acac) 2 ) catalyst was studied for the cobalt-mediated radical polymerization (CMRP) of vinyl acetate (VAc). We report a simple but efficient technique to produce this supported catalysts through the adsorption of Co(acac) 2 on the surface of alumina particles. Moreover, kinetic and thermodynamic study of Co(acac) 2 adsorption on the alumina support were conducted and the influence of effective parameters were investigated. It was found that using alumina-supported Co(acac) 2 for radical polymerization of VAc yields polymers with controlled molecular weight, narrow molecular weight distribution, and high purity. For the alumina-supported CMRP, changing the polymerization mechanism and domination of termination pathway compared to degenerate transfer pathway resulted in a 2.5 times increase in polymerization rate (k ap ) and a drop in induction time while maintaining a good control of the VAc polymerization.
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