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2篇 您的检索式：作者名="CUI Xiaoluo"

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		题名	作者	年代	出处	被引量
	1	Supervised Penalty Matrix Decomposition for Tumor Differentially Expressed Genes Selection显示文摘A reliable and precise recognition of the differentially expressed genes of tumor is crucial to treat the cancer effectively. The small number of differentially expressed genes in a huge gene expression dataset determines the important role of sparse methods, such as Penalty matrix decomposition(PMD), among the feature selection methods. The sparse methods always have the drawback: they do not take advantage of known class labels of gene expression data. A novel supervised-sparse method named as Supervised PMD(SPMD) is proposed by adding the class information into PMD via the total scatter matrix. The brief idea of our method used to select the differentially expressed genes is given as follows.The total scatter matrix is obtained according to the gene expression data with class label. The obtained total scatter matrix is decomposed by PMD to acquire the sparse vectors. The non-zero items in sparse vectors are selected as the differentially expressed genes. The Gene ontology(GO) enrichment of functional annotation of the selected genes is detected by Topp Fun. Experiments on synthetic data and two real tumor gene expression datasets show that the proposed SPMD is quite promising to select the differentially expressed genes.	LIU Jian CHENG Yuhu WANG Xuesong CUI Xiaoluo	2018	Chinese Journal of Electronics2018，27，4：	1
	2	Towards high-density storage of text and images into DNA by the“Xiao-Pang”codec system显示文摘Dear Editor,The information about human civilization has been passed down through texts and images.Owing to its remarkable property,DNA has been suggested as an excellent medium for the long-term storage of texts and images.Codec systems to convert the data into A/T/C/G sequence and vice versa have become quite critical.The current well-known codec systems(Blawat et al.,2016;Church et al.,2012;Erlich and Zielinski,2017;Goldman et al.,2013;Grass et al.,2015)	Mingwei Lu Yang Wang Wei Qiang Junting Cui Yu Wang Xiaoluo Huang Junbiao Dai	2023	Science China(Life Sciences)2023，66，6：	0

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