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Comparison of dimension reduction-based logistic regression models for case-control genome-wide association study:principal components analysis *** least squares

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The Journal of Biomedical Research 2015年第4期29卷 298-307页

作者： Honggang Yi Hongmei Wo Yang Zhao Ruyang Zhang Junchen Dai Guangfu Jin Hongxia Ma Tangchun Wu Zhibin Hu Dongxin Lin Hongbing Shen Feng Chen Department of Epidemiology and Biostatistics School of Public Health Department of Public Service Management School of KangDa Section of Clinical Epidemiology Jiangsu Key Laboratory of Cancer BiomarkersPrevention and TreatmentCancer Center Institute of Occupational Medicine and Ministry of Education Key Laboratory for Environment and HealthSchool of Public HealthTongji Medical CollegeHuazhong University of Science and Technology State Key Laboratory of Reproductive Medicine Nanjing Medical University State Key Laboratory of Molecular Oncology and Department of Etiology and Carcinogenesis Cancer Institute and HospitalChinese Academy of Medical Sciences and Peking Union Medical College

With recent advances in biotechnology, genome-wide association study （GWAS） has been widely used to identify genetic variants that underlie human complex diseases and traits. In case-control GWAS, typical statistical strategy is traditional logistical regression （LR） based on single-locus analysis. However, such a single-locus analysis leads to the well-known multiplicity problem, with a risk of inflating type I error and reducing power. Dimension reduction-based techniques, such as principal component-based logistic regression （PC-LR）, partial least squares-based logistic regression （PLS-LR）, have recently gained much attention in the analysis of high dimensional genomic data. However, the perfor- mance of these methods is still not clear, especially in GWAS. We conducted simulations and real data application to compare the type I error and power of PC-LR, PLS-LR and LR applicable to GWAS within a defined single nucleotide polymorphism （SNP） set region. We found that PC-LR and PLS can reasonably control type I error under null hypothesis. On contrast, LR, which is corrected by Bonferroni method, was more conserved in all simulation settings. In particular, we found that PC-LR and PLS-LR had comparable power and they both outperformed LR, especially when the causal SNP was in high linkage disequilibrium with genotyped ones and with a small effective size in simulation. Based on SNP set analysis, we applied all three methods to analyze non-small cell lung cancer GWAS data.

关键词： principal components analysis partial least squares-based logistic regression genome-wide association study,type I error power

Estimation of the Number of Collapsed Houses Damaged by Typhoon Based on principal components analysis and Support Vector Machine

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Meteorological and Environmental Research 2010年第4期1卷 11-14页

作者：张新厂娄伟平南京信息工程大学研究生部浙江省新昌县气象局浙江新昌312500

The evaluation model was established to estimate the number of houses collapsed during typhoon disaster for Zhejiang *** factor leading to disaster,the environment fostering disaster and the exposure of buildings were processed by principal Component *** key factor was extracted to support input of vector machine model and to build an evaluation model;the historical fitting result kept in line with the *** the real evaluation of two typhoons landed in Zhejiang Province in 2008 and 2009,the coincidence of evaluating result and actual value proved the feasibility of this model.

关键词： Typhoon The number of collapsed houses principal components analysis Support Vector Machine Evaluation China

Fault Diagnosis Model Research Based on Support Vector Regression and principal components analysis

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Fault Diagnosis Model Research Based on Support Vector Regre...

Research on Fault Diagnosis Method of principal components analysis and D-S Evidence Theory

2010 Chinese Control and Decision Conference

作者： WenJie Tian,JiCheng Liu Beijing Automation Institute of Beijing Union University,Beijing,China,100101

＜正＞To overcome the deficiencies of low accuracy and high false alarm rate in fault diagnosis system,a new optimization method for f the fault diagnosis model is proposed based on support vector regression（SVR）and principal components *** the character that principal components analysis algorithm can keep the discernability of original dataset after reduction,the reduces of the original dataset are calculated and used to train individual SVR classifier for ensemble,which increase the diversity between individual classifiers,and consequently,increase the detection accuracy. To validate the effectiveness of the proposed method,simulation experiments are performed based on the electronic circuit *** results show that the proposed method is a promised ensemble method owning to its high diversity,high detection accuracy and faster speed in fault diagnosis.

关键词： Support Vector Regression principal components analysis Fault Diagnosis Reduction Ensemble

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Research on Fault Diagnosis Method of Principal Components A...

Oxypropylation of Brazilian Pine-Fruit Shell Evaluated by principal Component analysis

2009中国控制与决策会议

作者： Peng Tong~(),Lian-suo An~2,Ji Zhang~3,Yu-tao Liu~4 1.Department of Power Engineering,Baoding Technical College of Electric Power,Baoding 071003,China 2.Department of Power Engineering,North China Electric Power University,Baoding 071003,China 3.Department of Computer,North China Electric Power University,Baoding 071003,China 4.Department of Mathematics & Physics,North China Electric Power University,Baoding 071003,China

＜正＞According to the problem of amount and complexity of calculation exponent increased with the measurement dimension increment when multi-data source related by D-S Fusion arithmetic,a fault diagnosis method combined PCA and D-S evidence theory is ***,the characteristic of data dimension reduction of PCA and the completeness of fault diagnosis and the advantage of presenting and reasoning inaccurate information of evidence theory are utilized, and the problem of non-uniqueness of fault isolation of PCA and combination explosion of evidence theory is solved. And calculation amount and complexity are reduced quit,and the simulation study on boiler feed water control system shows that the fault of sensor can be isolated correctly and effectively by this method.

关键词： fault diagnosis D-S evidence theory principal components analysis power plant

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Journal of Renewable Materials 2018年第7期6卷 715-723页

作者： Stephany Cde Rezende João APinto Isabel PFernandes Fernanda VLeimann Maria-Filomena Barreiro Laboratory of Separation and Reaction Engineering-Laboratory of Catalysis and Materials(LSRE-LCM) Polytechnic Institute of BragançaBragançaPortugal Programa de Pós-Graduação em Tecnologia de Alimentos(PPGTA) Universidade Tecnológica Federal do Paraná-UTFPR Campus Campo MourãoVia Rosalina Maria Dos Santos1233.CEP 87301-899.Caixa Postal:271Campo Mourão-PR-Brasil Mountain Research Centre(CIMO) Polytechnic Institute of BragançaBragançaPortugal

Pine-fruit shell (PFS) is a lignocellulosic residue derived from the fruit of Araucaria angustifolia, a coniferoustree native of South America, part of a whole vegetation of the Atlantic Forest, found in the South and Southwestof Brazil. In this work PFS will be characterized and used in the production of PFS-based polyols throughoxypropylation. Three series were chosen (PFS/propylene oxide (PO) (w/v, g/mL) of 30/70, 20/80 and 10/90)with four catalyst levels (5%, 10%, 15% and 20%, (w/w, PFS based)). Oxypropylation occurred at moderateconditions of temperature, pressure and time giving rise to liquid polyols with a homopolymer content (PPO)ranging from 4-65%, a hydroxyl number (IOH) between 257-605 mg KOH/g and viscosities (V) varying from0.76 Pa.s to 373.90 Pa.s (20°C) for the series 20/80 and 10/90, while for the series 30/70, the viscosity reachedvalues higher than 500 Pa.s, 20°C. The unreacted PFS (UR) varied between 3.6% and 77.4% (PFS-basis). Afterthe PFS-based polyols production and characterization, a principal component analysis (PCA) was performed inorder to evaluate the established interactions between the used formulation variables and the obtained polyolproperties. The PCA analysis allowed to clarify the interactions between PFS and PO contents and the finalbiopolyol properties (PPO, IOH, V and UR). This approach showed to be a simple method to rationally analyzethe influence of the input formulation variables on the final polyol properties.

关键词： principal components analysis Brazilian pine-fruit shell (PFS) oxypropylation polyols

principal Component analysis （PCA） on Multivariate Data of Lard analysis in Cooking Oil

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Journal of Mathematics and System Science 2015年第7期5卷 300-306页

作者： Nor Aishah Mohd Salleh Mohd Sukri Hassan Faculty of Science and Technology Universiti Sains Islam Malaysia (USIM) Bandar Baru Nilai 71800 Nilai Negeri SembilanMalaysia. Institute of Halal Research & Management (IHRAM) Universiti Sains Islam Malaysia (USIM) Bandar Baru Nilai 71800 NilaiNegeri Sembilan Malaysia.

Discrimination of fatty acids （FAs） of lard in used cooking oil is important in halal determination. The aim of this study was to find the information related to the changes FAs of lard when frying in cooking oil. Quantitative analysis of FAs composition extracted from a series of experiments which involving frying cooking oil spiked with lard at three different parameters; concentration of spiked lard, heating temperatures and period of frying. The samples were analyzed using Gas Chromatography （GC） and principal components analysis （PCA） technique. Multivariate data from chromatograms of FAs were standardized and computed using Unscrambler X10 into covariance matrix and eigenvectors correspond to principal components （PCs）. Results have shown that the first and second PCs contribute to the FAs mapping which can be visualized by scores and loading plots to discriminate FAs of lard in used cooking oil

关键词： Fatty acids lard gas chromatography principal components analysis

Cardiovascular age of aviation personnel: based on the principal component analysis of heart rate and blood pressure variability

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Journal of Medical Colleges of PLA(China) 2004年第1期19卷 64-70页

作者：牛有国王守岩张玉海王兴邦张立藩 Department of Aerospace Physiology Department of Medical Statistics Fourth Military Medical University

Objective: To introduce a method to calculate cardiovascular age, a new, accurate and much simpler index for assessing cardiovascular autonomic regulatory function, based on statistical analysis of heart rate and blood pressure variability (HRV and BPV) and baroreflex sensitivity (BRS) data. Methods: Firstly, HRV and BPV of 89 healthy aviation personnel were analyzed by the conventional autoregressive (AR) spectral analysis and their spontaneous BRS was obtained by the sequence method. Secondly, principal component analysis was conducted over original and derived indices of HRV, BPV and BRS data and the relevant principal components, PCi orig and PCi deri (i=1, 2, 3,...) were obtained. Finally, the equation for calculating cardiovascular age was obtained by multiple regression with the chronological age being assigned as the dependent variable and the principal components significantly related to age as the regressors. Results: The first four principal components of original indices accounted for over 90% of total variance of the indices, so did the first three principal components of derived indices. So, these seven principal components could reflect the information of cardiovascular autonomic regulation which was embodied in the 17 indices of HRV, BPV and BRS exactly with a minimal loss of information. Of the seven principal components, PC2 orig , PC4 orig and PC2 deri were negatively correlated with the chronological age ( P analysis of HRV, BPV and BRS could be regarded as a comprehensive indicator reflecting the age dependency of autonomic regulation of cardiovascular system in healthy aviation personnel.

关键词： flying personnel heart rate variability blood pressure variability baroreflex sensitivity age principal components analysis multiple regression analysis

Characteristics of PM_(2.5) in rural areas of southern Jiangsu Province, China

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Journal of Environmental Sciences 2005年第6期17卷 977-980页

作者： ZHOU Zhen-feng LIU Kang WANG Xiao-rong HUANG Shi-hong School of Environmental Science and Engineering Ocean University of China Qingdao 266003 China Department of Environmental Science Northwest University Xi＇an 710069 China State Key Laboratory of Pollution Control and Resources Reuse School of the Environment Nanjing University Nanjing 210093 China Department of Atmospheric Sciences Nanjing University Nanjing 210093 China

To understand pollution level and possible sources of atmospheric fine particulates in rural areas of southern Jiangsu Province of China, samples of PM2.5 were collected and analyzed in Xueyan Town and Taihu Lake Station over three seasons from July 2002 to January 2003. The mass concentrations of PM2.5 and 14 principal component elements were obtained. The results showed that pollution of PM2.5 was serious and the concentration levels of S, Zn, Pb and As were similar to city. There are different seasonal distribution laws of pollutant elements in PM2.5 between two sampling sites, probably due to contribution of local sources, medium or long distance transportation of fine particulates and complicated meteorological conditions. The enrichment levels of S, Zn, Pb, As, K were high, reflecting the influence of anthropogenic activities. Particularly enrichment level of S was much higher in summer, which was probably related to meteorological condition. The result of principal components analysis showed major sources of PM2.5 included crustal resuspension, coal burning, metal processing industry or waste incineration, vehicular emission, which suggests anthropogenic activities is of important influence on PM2.5 in rural areas of southern Jiangsu Province.

关键词： rural areas PM2.5 sources enrichment factor principal components analysis

Drought Forecasting in a Semi-arid Watershed Using Climate Signals:a Neuro-fuzzy Modeling Approach

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Journal of Mountain Science 2014年第6期11卷 1593-1605页

作者： Bahram CHOUBIN Shahram KHALIGHI-SIGAROODI Arash MALEKIAN Sajjad AHMAD Pedram ATTAROD Department of Watershed Management Sari University of Agriculture Sciences and Natural ResourcesSari 48181-68984Iran University of Tehran Faculty of Natural ResourcesKaraj 31585-3314 Iran Department of Civil and Environmental Engineering University of NevadaLas Vegas NV 89154-4015 USA

Large-scale annual climate indices were used to forecast annual drought conditions in the Maharlu-Bakhtegan watershed,located in Iran,using a neuro-fuzzy *** Standardized Precipitation Index(SPI) was used as a proxy for drought *** the 45 climate indices considered,eight identified as most relevant were the Atlantic Multidecadal Oscillation(AMO),Atlantic Meridional Mode(AMM),the Bivariate ENSO Time series(BEST),the East Central Tropical Pacific Surface Temperature(NINO 3.4),the Central Tropical Pacific Surface Temperature(NINO 4),the North Tropical Atlantic Index(NTA),the Southern Oscillation Index(SOI),and the Tropical Northern Atlantic Index(TNA).These indices accounted for 81% of the variance in the principal components analysis(PCA) *** Atlantic surface temperature(SST:Atlantic) had an inverse relationship with SPI,and the AMM index had the highest *** forecasts of neuro-fuzzy model demonstrate better prediction at a two-year lag compared to a stepwise regression model.

关键词： Annual Rainfall Large-scale Climate Signals Neuro-Fuzzy Cross-Correlation principal components analysis Drought