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Evidence of linkage to chromosome 1 for early age of onset of rheumatoid arthritis and HLA marker DRB1 genotype in NARAC data

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BMC proceedings 2007年第1期1 Suppl 1卷 S78页

作者： Wei Xu Hui Lan Pingzhao Hu Shelley B Bull Celia M T Greenwood Department of Biostatistics Princess Margaret Hospital Toronto Ontario 610 University Avenue Room 15-507 Toronto Ontario Canada M5G 2M9. wxu@utstat.toronto.edu

Focusing on chromosome 1, a recursive partitioning linkage algorithm (RP) was applied to perform linkage analysis on the rheumatoid arthritis NARAC data, incorporating covariates such as HLA-DRB1 genotype, age at onset, severity, anti-cyclic citrullinated peptide (anti-CCP), and life time smoking. All 617 affected sib pairs from the ascertained families were used, and an RP linkage model was used to identify linkage possibly influenced by covariates. This algorithm includes a likelihood ratio (LR)-based splitting rule, a pruning algorithm to identify optimal tree size, and a bootstrap method for final tree *** strength of the linkage signals was evaluated by empirical p-values, obtained by simulating marker data under null hypothesis of no linkage. Two suggestive linkage regions on chromosome 1 were detected by the RP linkage model, with identified associated covariates HLA-DRB1 genotype and age at onset. These results suggest possible gene x gene and gene x environment interactions at chromosome 1 loci and provide directions for further gene mapping.

关键词： Recursive Partitioning pruning algorithm North American Rheumatoid Arthritis Consortium Empirical Null Distribution Recursive Partitioning Model

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MODELING OF SUPERHEATED STEAM TEMPERATURE USING SPARSE LEAST SQUARES SUPPORT VECTOR NETWORKS

MODELING OF SUPERHEATED STEAM TEMPERATURE USING SPARSE LEAST...

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2006 International Conference on Machine Learning and Cybernetics(IEEE第五届机器学习与控制论坛)

作者： YONG WANG XIANG-JIE LIU JI-ZHEN LIU Deparrtment of Automation North Electric Power Beijing 102206 China Department of Computing Chan Deparrtment of Automation North Electric Power Beijing 102206 China

Super-heater steam temperature in power plant is the strong nonlinearity system. Though neural networks have the ability to approximate nonlinear functions with arbitrary accuracy, good generalization results are obtained only if the structure of the network is suitably chosen. Therefore, selecting the "best" structure of the neural network is more difficulty. Sparse Least squares support vector networks (SLSVN) are proposed to model the superheated steam of power plant in this paper. The structure of the SLSVN is obtained by equality-constrained minimization. Under the condition of modeling approximating to performance, the pruning algorithm gets the sparse modeling. The merits of the algorithm are conforming to the least structural risk in training process and hardly leading to over-fitting. The simulation of a superheating system, in a 600MW supercritical concurrent boiler, is taken. The result shows that the proposed SLSVN model can adapt to the strong nonlinear super-heater steam temperature process.

关键词： Support vector networks RBF pruning algorithm Sparseness least squares

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The m6A RNA methylation regulators related transcriptome for identification of pancreatic cancer subtypes and prognostic markers

The m6A RNA methylation regulators related transcriptome for...

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2021年第四届算法、计算和人工智能国际会议

作者： Zhengshu Lu Yanrui Ding Laboratory of Media Design and Software Technology Jiangnan University School of science Jiangnan University

N6-methyladenosine(m6 A) methylation is a major epigenetic modification of RNA that affects processes such as translation of related mRNAs and non-coding RNAs. A large number of recent studies have shown that m6 A modifications play a crucial role in cancer development, however, the prognostic value of the m6 A associated transcriptome in pancreatic cancer has rarely been investigated. The purpose of this study is to investigate the prognostic markers and prognostic subtypes of m6 A RNA methylation regulators related transcriptome in pancreatic ductal adenocarcinoma(PDAC). First,we identified the m6 A RNA methylation regulators related prognostic transcriptome by Pearson correlation analysis and univariate cox regression. Subsequently, to explore key prognostic markers from the prognostic transcriptome, we proposed a G-P model based on greedy algorithms and pruning algorithms to obtain a set of key genes CASC11, KRT14, PDZD4, and identified two high/low-risk subtypes of PDAC with significant prognostic differences based on key genes. The clustering Silhouette coefficients was 0.99 for the key genes. In addition, CASC11 and KRT14 were strongly upregulated in the high-risk subtype and PDZD4 was upregulated in the low-risk subtype, and their differential expression was significantly associated with survival. In conclusion, we revealed the typing role and prognostic value of the m6 A RNA methylation regulators-associated transcriptome in PDAC and provided new insights for identifying predictive biomarkers and therapeutic targets for PDAC.

关键词： m6A Biomarker Greedy algorithm pruning algorithm clustering

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A Method to Determine the Parameters of Phase Space Reconstruction Based on the Neural Network

A Method to Determine the Parameters of Phase Space Reconstr...

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2009 International Workshop on Chaos-Fractals Theories and Applications(IWCFTA 2009)

作者： Runjie LIU,Zhan HOU,Jinyuan SHEN School of Information Engineering Zhengzhou University Zhengzhou,China

In classic phase space reconstruction,the time lag is *** our research,the different time lags are found more effectively for teletraffic *** this paper,a method to determine the different time lags in phase space reconstruction is *** results show that the prediction is more accurate by using the different time lags in reconstruction phase space.

关键词： Phase Space Reconstruction time lags embedding dimensions TDNN pruning algorithm

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Study on Boiler Combustion Optimization Based on Sparse Least Squares Support Vector Machine

Study on Boiler Combustion Optimization Based on Sparse Leas...

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International Symposium on Computational Intelligence and Design

作者： Nankun Chen Jianhong Lv School of energy and environment Southeast University Nanjing Jiangsu Province China

ISBN: (纸本)9781467395885

Under the background of seeking high efficiency and low nitrogen oxides (NOx) emissions for the boiler of power plants, this paper used least squares support vector machine (LSSVM) to model the boiler efficiency and NOx emissions of a power plant according to the experimental data acquired from a combustion adjustment test. A pruning algorithm based on active learning was applied to the combustion model built earlier to obtain a sparse LSSVM model. Compared to Suykens standard pruning algorithm for LSSVM, AL-LSSVM (active learning LSSVM) can significantly reduce the complexity of combustion models without degrading much, which provides an effective method for incremental or adaptive learning of combustion models.

关键词： combustion optimization least squares support vector machine active learning pruning algorithm

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RISK ASSESSMENT IN ELECTRICAL POWER NETWORK PLANNING BASED ON SPARSE LEAST SQUARES SUPPORT VECTOR MACHINES

RISK ASSESSMENT IN ELECTRICAL POWER NETWORK PLANNING BASED O...

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2008 International Conference on Machine Learning and Cybernetics(2008机器学习与控制论国际会议)

作者： WEI SUN YUE MA Department of Economy Management North China Electric Power University Baoding 071003 Hebei China

To assess risk of electrical power network planning in an effective and fast way, the forecasting model of least square support vector machine (LS-SVM) based on pruning algorithm is established. Relative to the classical SVM, the least square SVM (LS-SVM) can transform a quadratic programming problem into a linear programming problem thus reducing the computational complexity. But sparseness is lost in the LS-SVM case. And the pruning algorithm make LSSVM recur sparseness. For illustration, a real-world planning project dataset is used to test the effectiveness of sparse least squares support vector machines(S-LS-SVM).

关键词： Electrical Power Network Planning Support Vector Machine Least Square Support Vector Machines Sparse pruning algorithm

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The m6A RNA methylation regulators related transcriptome for identification of pancreatic cancer subtypes and prognostic markers 21

The m6A RNA methylation regulators related transcriptome for...

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Proceedings of the 2021 4th International Conference on algorithms, Computing and Artificial Intelligence

作者： Zhengshu Lu Yanrui Ding Laboratory of Media Design and Software Technology Jiangnan University China School of science Jiangnan University China

ISBN: (纸本)9781450385053

N6-methyladenosine (m6A) methylation is a major epigenetic modification of RNA that affects processes such as translation of related mRNAs and non-coding RNAs. A large number of recent studies have shown that m6A modifications play a crucial role in cancer development, however, the prognostic value of the m6A associated transcriptome in pancreatic cancer has rarely been investigated. The purpose of this study is to investigate the prognostic markers and prognostic subtypes of m6A RNA methylation regulators related transcriptome in pancreatic ductal adenocarcinoma (PDAC). First, we identified the m6A RNA methylation regulators related prognostic transcriptome by Pearson correlation analysis and univariate cox regression. Subsequently, to explore key prognostic markers from the prognostic transcriptome, we proposed a G-P model based on greedy algorithms and pruning algorithms to obtain a set of key genes CASC11, KRT14, PDZD4, and identified two high/low-risk subtypes of PDAC with significant prognostic differences based on key genes. The clustering Silhouette coefficients was 0.99 for the key genes. In addition, CASC11 and KRT14 were strongly upregulated in the high-risk subtype and PDZD4 was upregulated in the low-risk subtype, and their differential expression was significantly associated with survival. In conclusion, we revealed the typing role and prognostic value of the m6A RNA methylation regulators-associated transcriptome in PDAC and provided new insights for identifying predictive biomarkers and therapeutic targets for PDAC.

关键词： clustering pruning algorithm m6A Biomarker Greedy algorithm

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Estimation of myriad haplotype frequencies

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Genetic Epidemiology 1985年第3期2卷 263-272页

作者： Maclean, Charles J. Morton, Newton E. Population Genetics Laboratory University of Hawaii Honolulu United States

The present paper demonstrates algorithms for applying gene counting estimation of haplotype frequencies in very large genetic systems. A factor union representation of phenotypes is used which conveniently yields the sets of potential haplotypes and diplotypes for each phenotype. Methods for storing and rapidly retrieving the relevant haplotypes are given. An example with several hundred frequencies is given which required a few seconds computing time for each estimation iteration on a small computer. A computer program employing the methods described has been written in Fortran-77 and is available to any investigator on request. Copyright © 1985 Wiley-Liss, Inc., A Wiley Company

关键词： factor union notation gene counting haplotype estimation pruning algorithm

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IMPROVED SCHEME TO ACCELERATE SPARSE LEAST SQUARES SUPPORT VECTOR REGRESSION

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Journal of Systems Engineering and Electronics 2010年第2期000卷

作者： Yongping Zhao Jianguo Sun ZNDY of Ministerial Key Lab Nanjing University of Science and TechnologyNanjing 210094P.R.China College of Energy and Power Engineering Nanjing University of Aeronautics and AstronauticsNanjing 210016P.R.China

The pruning algorithms for sparse least squares support vector regression machine are common methods,and easily comprehensible,but the computational burden in the training phase is heavy due to the retraining in performing the pruning process,which is not favorable for their *** this end,an improved scheme is proposed to accelerate sparse least squares support vector regression machine.A major advantage of this new scheme is based on the iterative methodology,which uses the previous training results instead of retraining,and its feasibility is strictly verified ***,experiments on benchmark data sets corroborate a significant saving of the training time with the same number of support vectors and predictive accuracy compared with the original pruning algorithms,and this speedup scheme is also extended to classification problem.

关键词： least squares support vector regression machine pruning algorithm iterative methodology classification

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