Identifying barley pan-genome sequence anchors using genetic mapping and machine learning

Journal article

Gao, Shang, Wu, Ryan, Stiller, Jiri, Zheng, Zhi, Zhou, Meixue, Wang, You-Gan and Liu, Chunji. (2020). Identifying barley pan-genome sequence anchors using genetic mapping and machine learning. Theoretical and Applied Genetics. 133(9), pp. 2535-2544. https://doi.org/10.1007/s00122-020-03615-y

Publication dates
Authors	Gao, Shang, Wu, Ryan, Stiller, Jiri, Zheng, Zhi, Zhou, Meixue, Wang, You-Gan and Liu, Chunji
Abstract	We identified 1.844 million barley pan-genome sequence anchors from 12,306 genotypes using genetic mapping and machine learning. There is increasing evidence that genes from a given crop genotype are far to cover all genes in that species; thus, building more comprehensive pan-genomes is of great importance in genetic research and breeding. Obtaining a thousand-genotype scale pan-genome using deep-sequencing data is currently impractical for species like barley which has a huge and highly repetitive genome. To this end, we attempted to identify barley pan-genome sequence anchors from a large quantity of genotype-by-sequencing (GBS) datasets by combining genetic mapping and machine learning algorithms. Based on the GBS sequences from 11,166 domesticated and 1140 wild barley genotypes, we identified 1.844 million pan-genome sequence anchors. Of them, 532,253 were identified as presence/absence variation (PAV) tags. Through aligning these PAV tags to the genome of hulless barley genotype Zangqing320, our analysis resulted in a validation of 83.6% of them from the domesticated genotypes and 88.6% from the wild barley genotypes. Association analyses against flowering time, plant height and kernel size showed that the relative importance of the PAV and non-PAV tags varied for different traits. The pan-genome sequence anchors based on GBS tags can facilitate the construction of a comprehensive pan-genome and greatly assist various genetic studies including identification of structural variation, genetic mapping and breeding in barley.
Keywords	Algorithms; Chromosome Mapping; Plant Genome; Genotype; Hordeum genetics; Linkage Disequilibrium; Machine Learning
Year	01 Jan 2020
Journal	Theoretical and Applied Genetics
Journal citation	133 (9), pp. 2535-2544
Publisher	Springer
ISSN	0040-5752
Digital Object Identifier (DOI)	https://doi.org/10.1007/s00122-020-03615-y
PubMed ID	32448920
Web address (URL)	https://link.springer.com/article/10.1007/s00122-020-03615-y
Open access	Published as non-open access
Research or scholarly	Research
Page range	2535-2544
Publisher's version	License All rights reserved File Access Level Controlled
Output status	Published
Print	24 May 2020
Publication process dates
Accepted	16 May 2020
Deposited	11 Jan 2023
Additional information	© Springer-Verlag GmbH Germany, part of Springer Nature 2020.
Place of publication	Germany

Permalink -

https://acuresearchbank.acu.edu.au/item/8y92q/identifying-barley-pan-genome-sequence-anchors-using-genetic-mapping-and-machine-learning

Restricted files

Publisher's version

(1 files)

196
total views
0
total downloads
0
views this month
0
downloads this month

These values are for the period from 19th October 2020, when this repository was created.

Export as

Related outputs

Issues relating to CAT and Gain in psycho-metric testing of STEM subjects using IRT

Naylor, Wade, Rubab, Sundas, Wang, You-Gan and Qiu, Xuelan. (2024). Issues relating to CAT and Gain in psycho-metric testing of STEM subjects using IRT. In In White, Peta J., Tytler, Russell, Ferguson, Joseph Paul and Clark, Cripps John (Ed.). Methodological approaches to STEM education research ; volume 5 pp. 362-382 Cambridge Scholars Publishing.

Rapeseed seed coat color classification based on the visibility graph algorithm and hyperspectral technique

Zou, Chaojun, Zhu, Xinghui, Wang, Fang, Wu, Jinran and Wang, You-Gan. (2024). Rapeseed seed coat color classification based on the visibility graph algorithm and hyperspectral technique. Agronomy. 14(5), p. Article 941. https://doi.org/10.3390/agronomy14050941

Inflation transmission diagnostics via a Bayesian Graph Vector Autoregressive Model with Markov Switching

Fu, Jiali, Cai, Fengjing, Wu, Jinran, Zhao, Shangrui and Wang, You-Gan. (2024). Inflation transmission diagnostics via a Bayesian Graph Vector Autoregressive Model with Markov Switching. Journal of Systems Science and Complexity. pp. 1-24. https://doi.org/10.1007/s11424-024-3022-6

Solving the one dimensional vertical suspended sediment mixing equation with arbitrary eddy diffusivity profiles using temporal normalized physics-informed neural networks

Zhang, Shaotong, Deng, Jiaxin, Li, Xi-An, Zhao, Zixi, Wu, Jinran, Li, Weide, Wang, You-Gan and Jeng, Dong-Sheng. (2024). Solving the one dimensional vertical suspended sediment mixing equation with arbitrary eddy diffusivity profiles using temporal normalized physics-informed neural networks. Physics of Fluids. 36(1), pp. 1-17. https://doi.org/10.1063/5.0179223

Augmented support vector regression with an autoregressive process via an iterative procedure

Wu, Jinran, Wang, You-Gan and Zhang, Hao. (2024). Augmented support vector regression with an autoregressive process via an iterative procedure. Applied Soft Computing Journal. 158, pp. 1-13. https://doi.org/10.1016/j.asoc.2024.111549

An adaptive trimming approach to Bayesian additive regression trees

Cao, Taoyun, Wu, Jinran and Wang, You-Gan. (2024). An adaptive trimming approach to Bayesian additive regression trees. Complex and Intelligent Systems. pp. 6805-6823. https://doi.org/10.1007/s40747-024-01516-x

Assessing and predicting water quality index with key water parameters by machine learning models in coastal cities, China

Xu, Jing, Mo, Yuming, Zhu, Senlin, Wu, Jinran, Jin, Guangqiu, Wang, You-Gan, Ji, Qingfeng and Li, Ling. (2024). Assessing and predicting water quality index with key water parameters by machine learning models in coastal cities, China. Heliyon. 10(13), pp. 1-19. https://doi.org/10.1016/j.heliyon.2024.e33695

Item Response Theory Models for Polytomous Multidimensional Forced-Choice Items to Measure Construct Differentiation

Qiu, Xuelan, de la Torre, Jimmy, Wang, You-Gan and Wu, Jinran. (2024). Item Response Theory Models for Polytomous Multidimensional Forced-Choice Items to Measure Construct Differentiation. Educational Measurement: Issues and Practice. pp. 1-12. https://doi.org/10.1111/emip.12621

Optimization of suspended particulate transport parameters from measured concentration profiles with a new analytical model

Zhang, Shaotong, Zhao, Zixi, Wu, Jinran, Perrochet, Pierre, Wang, You-Gan, Li, Guangxue and Li, Sanzhong. (2024). Optimization of suspended particulate transport parameters from measured concentration profiles with a new analytical model. Water Research. 254, pp. 1-11. https://doi.org/10.1016/j.watres.2024.121407

Physical informed neural networks with soft and hard boundary constraints for solving advection-diffusion equations using Fourier expansions

Li, Xi-An, Deng, Jiaxin, Wu, Jinran, Zhang, Shaotong, Li, Weide and Wang, You-Gan. (2024). Physical informed neural networks with soft and hard boundary constraints for solving advection-diffusion equations using Fourier expansions. Computers and Mathematics with Applications. 159, pp. 60-75. https://doi.org/10.1016/j.camwa.2024.01.021

Estimation of Sediment Transport Parameters From Measured Suspended Concentration Time Series Under Waves and Currents With a New Conceptual Model

Zhang, Shaotong, Zhao, Zixi, Li, Guangxue, Wu, Jinran, Wang, You-Gan, Nielsen, Peter, Jeng, Dong-Sheng, Qiao, Lulu, Wang, Chenghao and Li, Sanzhong. (2024). Estimation of Sediment Transport Parameters From Measured Suspended Concentration Time Series Under Waves and Currents With a New Conceptual Model. Water Resources Research. 60(4), pp. 1-20. https://doi.org/10.1029/2023WR034933

Solving a class of multi-scale elliptic PDEs by Fourier-based mixed physics informed neural networks

Li, Xi-An, Wu, Jinran, Tai, Xin, Xu, Jianhua and Wang, You-Gan. (2024). Solving a class of multi-scale elliptic PDEs by Fourier-based mixed physics informed neural networks. Journal of Computational Physics. 508(C), pp. 1-23. https://doi.org/10.1016/j.jcp.2024.113012

An Iterative Scale Purification Procedure on lz for the Detection of Aberrant Responses

Qiu, Xuelan, Huang, Sheng-Yun, Wang, Wen-Chung and Wang, You-Gan. (2024). An Iterative Scale Purification Procedure on lz for the Detection of Aberrant Responses. Multivariate Behavioral Research. 59(1), pp. 62-77. https://doi.org/10.1080/00273171.2023.2211564

Recent advances in longitudinal data analysis

Fu, Liya, Wang, You-Gan and Wu, Jinran. (2024). Recent advances in longitudinal data analysis. Handbook of Statistics. 50, pp. 173-221. https://doi.org/10.1016/bs.host.2023.10.007

Robust augmented estimation for hourly PM 2.5 using heteroscedastic spatiotemporal models

Song, Yanan, Wu, Jinran, Fu, Liya and Wang, You-Gan. (2023). Robust augmented estimation for hourly PM 2.5 using heteroscedastic spatiotemporal models. Stochastic Environmental Research and Risk Assessment. 38, pp. 1423-1451. https://doi.org/10.1007/s00477-023-02628-5

Predictions of runoff and sediment discharge at the lower Yellow River Delta using basin irrigation data

Zhao, Shangrui, Yang, Zhen, Zhang, Shaotong, Wu, Jinran, Zhao, Zixi, Jeng, Dong-Sheng and Wang, You-Gan. (2023). Predictions of runoff and sediment discharge at the lower Yellow River Delta using basin irrigation data. Ecological Informatics. 78, pp. 1-21. https://doi.org/10.1016/j.ecoinf.2023.102385

Progressive-fidelity computation of the genetic algorithm for energy-efficient virtual machine placement in cloud data centers

Ding, Zhe, Tian, Yu-Chu, Wang, You-Gan, Zhang, Weizhe and Yu, Zu-Guo. (2023). Progressive-fidelity computation of the genetic algorithm for energy-efficient virtual machine placement in cloud data centers. Applied Soft Computing. 146, pp. 1-13. https://doi.org/10.1016/j.asoc.2023.110681

Enhancing Feature Selection Optimization for COVID-19 Microarray Data

Krishanthi, Gayani, Jayetileke, Harshanie L., Wu, Jinran, Liu, Chan-Juan and Wang, You-Gan. (2023). Enhancing Feature Selection Optimization for COVID-19 Microarray Data. COVID. 3(9), pp. 1336-1355. https://doi.org/10.3390/covid3090093

Forecasting stock closing prices with an application to airline company data

Xu, Xu, Zhang, Yixiang, McGrory, Clare Anne, Wu, Jinran and Wang, You-Gan. (2023). Forecasting stock closing prices with an application to airline company data. Data Science and Management. 6(4), pp. 239-246. https://doi.org/10.1016/j.dsm.2023.09.005

Improved prediction of local significant wave height by considering the memory of past winds

Zhang, Shaotong, Yang, Zhen, Zhang, Yaqi, Zhao, Shangrui, Wu, Jinran, Wang, Chenghao, Wang, You-Gan, Jeng, Dong-Sheng, Nielsen, Peter, Li, Guangxue and Li, Sanzhong. (2023). Improved prediction of local significant wave height by considering the memory of past winds. Water Resources Research. 59(8), p. Article e2023WR034974. https://doi.org/10.1029/2023WR034974

Inferring circadian gene regulatory relationships from gene expression data with a hybrid framework

Hu, Shuwen, Jing, Yi, Li, Tao, Wang, You-Gan, Liu, Zhenyu, Gao, Jing and Tian, Yu-Chu. (2023). Inferring circadian gene regulatory relationships from gene expression data with a hybrid framework. BMC Bioinformatics. 24(1), p. Article 362. https://doi.org/10.1186/s12859-023-05458-y

Mixture extreme learning machine algorithm for robust regression

Zhao, Shangrui, Chen, Xuan-Ang, Wu, Jinran and Wang, You-Gan. (2023). Mixture extreme learning machine algorithm for robust regression. Knowledge-Based Systems. 280, p. Article 111033. https://doi.org/10.1016/j.knosys.2023.111033

Robust regression for electricity demand forecasting against cyberattacks

VandenHeuvel, Daniel, Wu, Jinran and Wang, You-Gan. (2023). Robust regression for electricity demand forecasting against cyberattacks. International Journal of Forecasting. 39(4), pp. 1573-1592. https://doi.org/10.1016/j.ijforecast.2022.10.004

An evaluation of the impact of COVID-19 lockdowns on electricity demand

Wu, Jinran, Levi, Noa, Araujo, Robyn and Wang, You-Gan. (2023). An evaluation of the impact of COVID-19 lockdowns on electricity demand. Electric Power Systems Research. 216, p. Article 109015. https://doi.org/10.1016/j.epsr.2022.109015

Robust adaptive rescaled lncosh neural network regression toward time-series forecasting

Yang, Yang, Zhou, Hu, Wu, Jinran, Ding, Zhe, Tian, Yu-Chu, Yue, Dong and Wang, You-Gan. (2023). Robust adaptive rescaled lncosh neural network regression toward time-series forecasting. IEEE Transactions on Systems, Man and Cybernetics: Systems. 53(9), pp. 5658-5669. https://doi.org/10.1109/TSMC.2023.3272880

Does one subgenome become dominant in the formation and evolution of a polyploid?

Liu, Chunji and Wang, You-Gan. (2023). Does one subgenome become dominant in the formation and evolution of a polyploid? Annals of Botany. 131(1), pp. 11-16. https://doi.org/10.1093/aob/mcac024

A novel deep learning framework with a COVID-19 adjustment for electricity demand forecasting

Cui, Zhesen, Wu, Jinran, Lian, Wei and Wang, You-Gan. (2023). A novel deep learning framework with a COVID-19 adjustment for electricity demand forecasting. Energy Reports. 9, pp. 1887-1895. https://doi.org/10.1016/j.egyr.2023.01.019

QQLMPA : A quasi-opposition learning and Q-learning based marine predators algorithm

Zhao, Shangrui, Wu, Yulu, Tan, Shuang, Wu, Jinran, Cui, Zhesen and Wang, You-Gan. (2023). QQLMPA : A quasi-opposition learning and Q-learning based marine predators algorithm. Expert Systems with Applications. 213(Part C), p. Article 119246. https://doi.org/10.1016/j.eswa.2022.119246

Accelerated computation of the genetic algorithm for energy-efficient virtual machine placement in data centers

Ding, Zhe, Tian, Yu-Chu, Wang, You-Gan, Zhang, Wei-Zhe and Yu, Zu-Guo. (2023). Accelerated computation of the genetic algorithm for energy-efficient virtual machine placement in data centers. Neural Computing and Applications. 35(7), pp. 5421-5436. https://doi.org/10.1007/s00521-022-07941-8

Predictions of machine learning with mixed-effects in analyzing longitudinal data under model misspecification

Hu, Shuwen, Wang, You-Gan, Drovandi, Christopher and Cao, Taoyun. (2023). Predictions of machine learning with mixed-effects in analyzing longitudinal data under model misspecification. Statistical Methods and Applications. 32(2), pp. 681-711. https://doi.org/10.1007/s10260-022-00658-x

A working likelihood approach to support vector regression with a data-driven insensitivity parameter

Wu, Jinran and Wang, You-Gan. (2023). A working likelihood approach to support vector regression with a data-driven insensitivity parameter. International Journal of Machine Learning and Cybernetics. 14(3), pp. 929-945. https://doi.org/10.1007/s13042-022-01672-x

A new algorithm for support vector regression with automatic selection of hyperparameters

Wang, You-Gan, Wu, Jinran, Hu, Zhi-Hua and McLachlan, Geoffrey J.. (2023). A new algorithm for support vector regression with automatic selection of hyperparameters. Pattern Recognition. 133, p. Article 108989. https://doi.org/10.1016/j.patcog.2022.108989

A hybrid deep learning framework for air quality prediction with spatial autocorrelation during the COVID‑19 pandemic

Zhao, Zixi, Wu, Jinran, Cai, Fengjing, Zhang, Shaotong and Wang, You-Gan. (2023). A hybrid deep learning framework for air quality prediction with spatial autocorrelation during the COVID‑19 pandemic. Scientific Reports. 13(1), pp. 1-17. https://doi.org/10.1038/s41598-023-28287-8

Iterative learning in support vector regression with heterogeneous variances

Wu, Jinran and Wang, You-Gan. (2023). Iterative learning in support vector regression with heterogeneous variances. IEEE Transactions on Emerging Topics in Computational Intelligence. 7(2), pp. 513-522. https://doi.org/10.1109/TETCI.2022.3182725

Parameter estimation for univariate skew-normal distribution based on the modified empirical characteristic function

Hou, Gege, Xu, Ancha, Cai, Fengjing and Wang, You-Gan. (2022). Parameter estimation for univariate skew-normal distribution based on the modified empirical characteristic function. Communications in Statistics - Theory and Methods. 51(22), pp. 7897-7910. https://doi.org/10.1080/03610926.2021.1883655

A novel decompose-cluster-feedback algorithm for load forecasting with hierarchical structure

Yang, Yang, Zhou, Hu, Wu, Jinran, Liu, Chan-Juan and Wang, You-Gan. (2022). A novel decompose-cluster-feedback algorithm for load forecasting with hierarchical structure. International Journal of Electrical Power and Energy Systems. 142(Part A), p. Article 108249. https://doi.org/10.1016/j.ijepes.2022.108249

Optimal battery capacity in electrical load scheduling

Duan, Qibin, Wu, Jinran and Wang, You-Gan. (2022). Optimal battery capacity in electrical load scheduling. Journal of Energy Storage. 50, p. Article 104190. https://doi.org/10.1016/j.est.2022.104190

An effective distance-based centrality approach for exploring the centrality of maritime shipping network

Kuang, Zengjie, Liu, Chanjuan, Wu, Jinran and Wang, You-Gan. (2022). An effective distance-based centrality approach for exploring the centrality of maritime shipping network. Heliyon. 8(11), p. Article e11474. https://doi.org/10.1016/j.heliyon.2022.e11474

A statistical learning framework for spatial-temporal feature selection and application to air quality index forecasting

Zhao, Zixi, Wu, Jinran, Cai, Fengjing, Zhang, Shaotong and Wang, You-Gan. (2022). A statistical learning framework for spatial-temporal feature selection and application to air quality index forecasting. Ecological Indicators. 144, p. Article 109416. https://doi.org/10.1016/j.ecolind.2022.109416

Spatio-temporal quantile regression analysis revealing more nuanced patterns of climate change : A study of long-term daily temperature in Australia

Duan, Qibin, McGrory, Clare A., Brown, Glenn, Mengersen, Kerrie and Wang, You-Gan. (2022). Spatio-temporal quantile regression analysis revealing more nuanced patterns of climate change : A study of long-term daily temperature in Australia. PLoS ONE. 17, p. Article e0271457. https://doi.org/10.1371/journal.pone.0271457

Performance of variance estimators in the analysis of longitudinal data with a large cluster size

Hu, Shuwen, Wang, You-Gan and Fu, Liya. (2022). Performance of variance estimators in the analysis of longitudinal data with a large cluster size. Journal of Statistical Computation and Simulation. 92(1), pp. 1-18. https://doi.org/10.1080/00949655.2021.1929983

Ridgelized Hotelling's T2 test on mean vectors of large dimension

Ha, Gao-Fan, Zhang, Qiuyan, Bai, Zhidong and Wang, You-Gan. (2022). Ridgelized Hotelling's T2 test on mean vectors of large dimension. Random Matrices: Theory and Application. 11(1), p. Article 2250011. https://doi.org/10.1142/S2010326322500113

Distribution, transfer process and influence factors of phosphorus at sediment-water interface in the Huaihe River

Xu, Jing, Mo, Yuming, Tang, Hongwu, Wang, Kun, Ji, Qingfeng, Zhang, Pei, Wang, You-Gan, Jin, Guangqiu and Li, Ling. (2022). Distribution, transfer process and influence factors of phosphorus at sediment-water interface in the Huaihe River. Journal of Hydrology. 612, p. Article 128079. https://doi.org/10.1016/j.jhydrol.2022.128079

A new mixture copula model for spatially correlated multiple variables with an environmental application

Abraj, Mohomed, Wang, You-Gan and Thompson, M. Helen. (2022). A new mixture copula model for spatially correlated multiple variables with an environmental application. Scientific Reports. 12(1), p. Article 13867. https://doi.org/10.1038/s41598-022-18007-z

Energy-efficient virtual machine placement in data centres via an accelerated Genetic Algorithm with improved fitness computation

Hormozi, Elham, Hu, Shuwen, Ding, Zhe, Tian, Yu-Chu, Wang, You-Gan, Yu, Zu-Guo and Zhang, Weizhe. (2022). Energy-efficient virtual machine placement in data centres via an accelerated Genetic Algorithm with improved fitness computation. Energy. 252, pp. 1-15. https://doi.org/10.1016/j.energy.2022.123884

A physics-informed statistical learning framework for forecasting local suspended sediment concentrations in marine environment

Zhang, Shaotong, Wu, Ryan, Wang, You-Gan, Jeng, Dong-Sheng and Li, Guangxue. (2022). A physics-informed statistical learning framework for forecasting local suspended sediment concentrations in marine environment. Water Research. 218, pp. 1-16. https://doi.org/10.1016/j.watres.2022.118518

Robustified extreme learning machine regression with applications in outlier-blended wind-speed forecasting

Yang, Yang, Zhou, Hu, Wu, Ryan, Ding, Zhe and Wang, You-Gan. (2022). Robustified extreme learning machine regression with applications in outlier-blended wind-speed forecasting. Applied Soft Computing. 122, pp. 1-14. https://doi.org/10.1016/j.asoc.2022.108814

An opposition learning and spiral modelling based arithmetic optimization algorithm for global continuous optimization problems

Yang, Yang, Gao, Yuchao, Tan, Shuang, Zhao, Shangrui, Wu, Jinran, Gao, Shangce, Zhang, Tengfei, Tian, Yu-Chu and Wang, You-Gan. (2022). An opposition learning and spiral modelling based arithmetic optimization algorithm for global continuous optimization problems. Engineering Applications of Artificial Intelligence. 113, p. Article 104981. https://doi.org/10.1016/j.engappai.2022.104981

A modified memetic algorithm with an application to gene selection in a sheep body weight study

Miao, Maoxuan, Wu, Jinran, Cai, Fengjing and Wang, You-Gan. (2022). A modified memetic algorithm with an application to gene selection in a sheep body weight study. Animals. 12(2), p. Article 201. https://doi.org/10.3390/ani12020201

Robust penalized extreme learning machine regression with applications in wind speed forecasting

Yang, Yang, Zhou, Hu, Gao, Yuchao, Wu, Jinran, Wang, You-Gan and Fu, Liya. (2022). Robust penalized extreme learning machine regression with applications in wind speed forecasting. Neural Computing and Applications. 34(1), pp. 391-407. https://doi.org/10.1007/s00521-021-06370-3

Packing computing servers into the vessel of an underwater data center considering cooling efficiency

Hu, Zhi-Hua, Zheng, Yu-Xin and Wang, You-Gan. (2022). Packing computing servers into the vessel of an underwater data center considering cooling efficiency. Applied Energy. 314, p. Article 118986. https://doi.org/10.1016/j.apenergy.2022.118986

Multi-horizon accommodation demand forecasting : A New Zealand case study

Zhu, Min, Wu, Jinran and Wang, You-Gan. (2021). Multi-horizon accommodation demand forecasting : A New Zealand case study. International Journal of Tourism Research. 23(3), pp. 442-453. https://doi.org/10.1002/jtr.2416

Differences between diploid donors are the main contributing factor for subgenome asymmetry measured in either gene ratio or relative diversity in allopolyploids

Ye, Xueling, Hu, Haiyan, Zhou, Hong, Jiang, Yunfeng, Gao, Shang, Yuan, Zhongwei, Stiller, Jiri, Li, Chengwei, Chen, Guoyue, Liu, Yaxi, Wei, Yuming, Zheng, You-Liang, Wang, You-Gan and Liu, Chunji. (2021). Differences between diploid donors are the main contributing factor for subgenome asymmetry measured in either gene ratio or relative diversity in allopolyploids. Genome. 64(9), pp. 847-856. https://doi.org/10.1139/gen-2020-0024

Small sample bias correction or bias reduction?

Zhang, Xuemao, Paul, Sudhir and Wang, You-Gan. (2021). Small sample bias correction or bias reduction? Communications in Statistics: Simulation and Computation. 50(4), pp. 1165-1177. https://doi.org/10.1080/03610918.2019.1577976

A robust and efficient variable selection method for linear regression

Yang, Zhuoran, Fu, Liya, Wang, You-Gan, Dong, Zhixiong and Jiang, Yunlu. (2021). A robust and efficient variable selection method for linear regression. Journal of Applied Statistics. 49(14), pp. 3677-3692. https://doi.org/10.1080/02664763.2021.1962259

Robust regression with asymmetric loss functions

Fu, Liya and Wang, You-Gan. (2021). Robust regression with asymmetric loss functions. Statistical Methods in Medical Research. 30(8), pp. 1800-1815. https://doi.org/10.1177/09622802211012012

A temporal LASSO regression model for the emergency forecasting of the suspended sediment concentrations in coastal oceans: Accuracy and interpretability

Zhang, Shaotong, Wu, Ryan, Jia, Yonggang, Wang, You-Gan, Zhang, Yaqi and Duan, Qibin. (2021). A temporal LASSO regression model for the emergency forecasting of the suspended sediment concentrations in coastal oceans: Accuracy and interpretability. Engineering Applications of Artificial Intelligence. 100, pp. 1-13. https://doi.org/10.1016/j.engappai.2021.104206

Robust approach for variable selection with high dimensional longitudinal data analysis

Fu, Liya, Li, Jiaqi and Wang, You-Gan. (2021). Robust approach for variable selection with high dimensional longitudinal data analysis. Statistics in Medicine. 40(30), pp. 6835-6854. https://doi.org/10.1002/sim.9213

Efficient and doubly-robust methods for variable selection and parameter estimation in longitudinal data analysis

Fu, Liya, Yang, Zhuoran, Cai, Fengjing and Wang, You-Gan. (2021). Efficient and doubly-robust methods for variable selection and parameter estimation in longitudinal data analysis. Computational Statistics. 36(2), pp. 781-804. https://doi.org/10.1007/s00180-020-01038-3

Predictive regression with p-lags and order-q autoregressive predictors

Jayetileke, Harshanie L., Wang, You-Gan and Zhu, Min. (2021). Predictive regression with p-lags and order-q autoregressive predictors. Journal of Empirical Finance. 62, pp. 282-293. https://doi.org/10.1016/j.jempfin.2021.04.006

An efficient Gehan-type estimation for the accelerated failure time model with clustered and censored data

Fu, Liya, Yang, Zhuoran, Zhou, Yan and Wang, You-Gan. (2021). An efficient Gehan-type estimation for the accelerated failure time model with clustered and censored data. Lifetime Data Analysis. 27(4), pp. 679-709. https://doi.org/10.1007/s10985-021-09526-4

Robust estimation procedure for autoregressive models with heterogeneity

Callens, A., Wang, Y.-G., Fu, L. and Liquet, B.. (2021). Robust estimation procedure for autoregressive models with heterogeneity. Environmental Modeling and Assessment. 26(3), pp. 313-323. https://doi.org/10.1007/s10666-020-09730-w

Influential factors on Chinese airlines’ profitability and forecasting methods

Xu, Xu, McGrory, Clare Anne, Wang, You-Gan and Wu, Jinran. (2021). Influential factors on Chinese airlines’ profitability and forecasting methods. Journal of Air Transport Management. 91, p. Article 101969. https://doi.org/10.1016/j.jairtraman.2020.101969

Support vector regression with asymmetric loss for optimal electric load forecasting

Wu, Ryan, Wang, You-Gan, Tian, Yu-Chu, Burrage, Kevin and Cao, Taoyun. (2021). Support vector regression with asymmetric loss for optimal electric load forecasting. Energy. 223, p. Article 119969. https://doi.org/10.1016/j.energy.2021.119969

Inclusion of features derived from a mixture of time window sizes improved classification accuracy of machine learning algorithms for sheep grazing behaviours

Hu, Shuwen, Ingham, Aaron, Schmoelzl, Sabine, McNally, Jody, Little, Bryce, Smith, Daniel, Bishop-Hurley, Greg, Wang, You-Gan and Li, Yutao. (2020). Inclusion of features derived from a mixture of time window sizes improved classification accuracy of machine learning algorithms for sheep grazing behaviours. Computers and Electronics in Agriculture. 179, p. Article 105857. https://doi.org/10.1016/j.compag.2020.105857

Response of sediments and phosphorus to catchment characteristics and human activities under different rainfall patterns with Bayesian Networks

Jin, Guangqiu, Xu, Jing, Mo, Yuming, Tang, Hongwu, Wei, Tong, Wang, You-Gan and Li, Ling. (2020). Response of sediments and phosphorus to catchment characteristics and human activities under different rainfall patterns with Bayesian Networks. Journal of Hydrology. 584, p. Article 124695. https://doi.org/10.1016/j.jhydrol.2020.124695

An improved firefly algorithm for global continuous optimization problems

Wu, Jinran, Wang, You-Gan, Burrage, Kevin, Tian, Yu-Chu, Lawson, Brodie and Ding, Zhe. (2020). An improved firefly algorithm for global continuous optimization problems. Expert Systems with Applications. 149, p. Article 113340. https://doi.org/10.1016/j.eswa.2020.113340

Bias reduction in the two-stage method for degradation data analysis

Xu, Ancha, Wang, You-Gan, Zheng, Shurong and Cai, Fengjing. (2020). Bias reduction in the two-stage method for degradation data analysis. Applied Mathematical Modelling. 77(Part 2), pp. 1413-1424. https://doi.org/10.1016/j.apm.2019.08.024

Exact algorithms for energy-efficient virtual machine placement in data centers

Wei, Chen, Hu, Zhi-Hua and Wang, You-Gan. (2020). Exact algorithms for energy-efficient virtual machine placement in data centers. Future Generation Computer Systems. 106, pp. 77-91. https://doi.org/10.1016/j.future.2019.12.043

A working likelihood approach for robust regression

Fu, Liya, Wang, You-Gan and Cai, Fengjing. (2020). A working likelihood approach for robust regression. Statistical Methods in Medical Research. 29(12), pp. 3641-3652. https://doi.org/10.1177/0962280220936310

Maritime convection and fluctuation between Vietnam and China : A data-driven study

Hu, Zhi-Hua, Liu, Chan-Juan, Chen, Wanting, Wang, You-Gan and Wei, Chen. (2020). Maritime convection and fluctuation between Vietnam and China : A data-driven study. Research in Transportation Business and Management. 34, pp. 1-15. https://doi.org/10.1016/j.rtbm.2019.100414

Natural mortality estimation using tree-based ensemble learning models

Liu, Chanjuan, Zhou, Shijie, Wang, You-Gan and Hu, Zhi-Hua. (2020). Natural mortality estimation using tree-based ensemble learning models. ICES Journal of Marine Science. 77(4), pp. 1414-1426. https://doi.org/10.1093/icesjms/fsaa058

Profile-guided three-phase virtual resource management for energy efficiency of data centers

Ding, Zhe, Tian, Yu-Chu, Tang, Maolin, Li, Yuefeng, Wang, You-Gan and Zhou, Chunjie. (2020). Profile-guided three-phase virtual resource management for energy efficiency of data centers. IEEE Transactions on Industrial Electronics. 67(3), pp. 2460-2468. https://doi.org/10.1109/TIE.2019.2902786

Response of water quality to land use and sewage outfalls in different seasons

Xu, Jing, Jin, Guangqiu, Tang, Hongwu, Mo, Yuming, Wang, You-Gan and Li, Ling. (2019). Response of water quality to land use and sewage outfalls in different seasons. Science of the Total Environment. 696, p. Article 134014. https://doi.org/10.1016/j.scitotenv.2019.134014

A new hybrid model to predict the electrical load in five states of Australia

Wu, Jinran, Cui, Zhesen, Chen, Yanyan, Kong, Demeng and Wang, You-Gan. (2019). A new hybrid model to predict the electrical load in five states of Australia. Energy. 166, pp. 598-609. https://doi.org/10.1016/j.energy.2018.10.076

Sweepstakes reproductive success is absent in a New Zealand snapper (Chrysophrus auratus) population protected from fishing despite “tiny” Ne/N ratios elsewhere

Jones, Andy, Lavery, Shane D., Le Port, Agnès, Wang, You-Gan, Blower, Dean and Ovenden, Jennifer. (2019). Sweepstakes reproductive success is absent in a New Zealand snapper (Chrysophrus auratus) population protected from fishing despite “tiny” Ne/N ratios elsewhere. Molecular Ecology. 28(12), pp. 2986-2995. https://doi.org/10.1111/mec.15130

A review of the Behrens–Fisher problem and some of its analogs : Does the same size fit all?

Paul, Sudhir, Wang, You-Gan and Ullah, Insha. (2019). A review of the Behrens–Fisher problem and some of its analogs : Does the same size fit all? Revstat Statistical Journal. 17(4), p. 563–597.

Incorporating social objectives in evaluating sustainable fisheries harvest strategy

Wu, Jiafeng, Wang, Na, Hu, Zhi-Hua, Hong, Zhenjie and Wang, You-Gan. (2019). Incorporating social objectives in evaluating sustainable fisheries harvest strategy. Environmental Modeling and Assessment. 24(4), pp. 381-386. https://doi.org/10.1007/s10666-019-9651-9

Significance tests for analyzing gene expression data with small sample sizes

Ullah, Insha, Paul, Sudhir, Hong, Zhenjie and Wang, You-Gan. (2019). Significance tests for analyzing gene expression data with small sample sizes. Bioinformatics. 35(20), pp. 3996-4003. https://doi.org/10.1093/bioinformatics/btz189

Robust Estimation Using Modified Huber’s Functions With New Tails

Jiang, Yunlu, Wang, You-Gan, Fu, Liya and Wang, Xueqin. (2019). Robust Estimation Using Modified Huber’s Functions With New Tails. Technometrics. 61(1), pp. 111-122. https://doi.org/10.1080/00401706.2018.1470037

Variable selection in rank regression for analyzing longitudinal data

Fu, Liya and Wang, You-Gan. (2018). Variable selection in rank regression for analyzing longitudinal data. Statistical Methods in Medical Research. 27(8), pp. 2447-2458. https://doi.org/10.1177/0962280216681347

Assessing temporal variations of Ammonia Nitrogen concentrations and loads in the Huaihe River Basin in relation to policies on pollution source control

Xu, Jing, Jin, Guangqiu, Tang, Hongwu, Zhang, Pei, Wang, Shen, Wang, You-Gan and Li, Ling. (2018). Assessing temporal variations of Ammonia Nitrogen concentrations and loads in the Huaihe River Basin in relation to policies on pollution source control. Science of the Total Environment. 642, pp. 1386-1395. https://doi.org/10.1016/j.scitotenv.2018.05.395

Genomic prediction of breeding values using a subset of SNPs identified by three machine learning methods

Li, Bo, Zhang, Nanxi, Wang, You-Gan, George, Andrew W., Reverter, Antonio and Li, Yutao. (2018). Genomic prediction of breeding values using a subset of SNPs identified by three machine learning methods. Frontiers in Genetics. 9, p. Article 237. https://doi.org/10.3389/fgene.2018.00237

Dividend growth and equity premium predictability

Zhu, Min, Chen, Rui, Du, Ke and Wang, You-Gan. (2018). Dividend growth and equity premium predictability. International Review of Economics and Finance. 56, pp. 125-137. https://doi.org/10.1016/j.iref.2017.10.020

Robust Regression with Data-Dependent Regularization Parameters and Autoregressive Temporal Correlations

Wang, Na, Wang, You-Gan, Hu, Shuwen, Hu, Zhi-Hua, Xu, Jing, Tang, Hongwu and Jin, Guangqiu. (2018). Robust Regression with Data-Dependent Regularization Parameters and Autoregressive Temporal Correlations. Environmental Modeling and Assessment. 23(6), pp. 779-786. https://doi.org/10.1007/s10666-018-9605-7

Analysis of spatial data with a nested correlation structure

Adegboye, Oyelola, Leung, Denis and Wang, You-Gan. (2018). Analysis of spatial data with a nested correlation structure. Journal of the Royal Statistical Society Series C: Applied Statistics. 67(2), pp. 329-354. https://doi.org/10.1111/rssc.12230

Working correlation structure selection in generalized estimating equations

Fu, Liya, Hao, Yangyang and Wang, You-Gan. (2018). Working correlation structure selection in generalized estimating equations. Computational Statistics. 33(2), pp. 983-996. https://doi.org/10.1007/s00180-018-0800-4

Selection of working correlation structure in generalized estimating equations

Wang, You-Gan and Fu, Liya. (2017). Selection of working correlation structure in generalized estimating equations. Statistics in Medicine. 36(14), pp. 2206-2219. https://doi.org/10.1002/sim.7262

Blockwise AICc for model selection in generalized linear models

Song, Guofeng, Dong, Xiaogang, Wu, Jiafeng and Wang, You-Gan. (2017). Blockwise AICc for model selection in generalized linear models. Environmental Modeling and Assessment. 22(6), pp. 523-533. https://doi.org/10.1007/s10666-017-9552-8

A comment on Koh’s “The optimal design of fallible organizations : Invariance of optimal decision threshold and uniqueness of hierarchy and polyarchy structures”

Zhu, Min, Liu, Chang and Wang, You-Gan. (2017). A comment on Koh’s “The optimal design of fallible organizations : Invariance of optimal decision threshold and uniqueness of hierarchy and polyarchy structures”. Social Choice and Welfare. 48(2), pp. 385-392. https://doi.org/10.1007/s00355-016-1009-5

The Buckley–James estimator and induced smoothing

Wang, You-Gan, Zhao, Yudong and Fu, Liya. (2016). The Buckley–James estimator and induced smoothing. Australian and New Zealand Journal of Statistics. 58(2), pp. 211-225. https://doi.org/10.1111/anzs.12155

Maximum likelihood estimation of natural mortality and quantification of temperature effects on catchability of brown tiger prawn (penaeus esculentus) in Moreton Bay (Australia) using logbook data

Kienzle, Marco, Sterling, David, Zhou, Shijie and Wang, You-Gan. (2016). Maximum likelihood estimation of natural mortality and quantification of temperature effects on catchability of brown tiger prawn (penaeus esculentus) in Moreton Bay (Australia) using logbook data. Ecological Modelling. 322, pp. 1-9. https://doi.org/10.1016/j.ecolmodel.2015.11.008

Otolith morphology of four mackerel species (Scomberomorus spp.) in Australia : Species differentiation and prediction for fisheries monitoring and assessment

Zischke, Mitchell T., Litherland, Lenore, Tilyard, Benjamin R., Stratford, Nicholas J., Jones, Ebony L. and Wang, You-Gan. (2016). Otolith morphology of four mackerel species (Scomberomorus spp.) in Australia : Species differentiation and prediction for fisheries monitoring and assessment. Fisheries Research. 176, pp. 39-47. https://doi.org/10.1016/j.fishres.2015.12.003

Efficient parameter estimation via Gaussian copulas for quantile regression with longitudinal data

Fu, Liya and Wang, You-Gan. (2016). Efficient parameter estimation via Gaussian copulas for quantile regression with longitudinal data. Journal of Multivariate Analysis. 143, pp. 492-502. https://doi.org/10.1016/j.jmva.2015.07.004

Mixture of time-dependent growth models with an application to blue swimmer crab length-frequency data

Lloyd-Jones, Luke R., Nguyen, Hien D., McLachlan, Geoffrey J., Sumpton, Wayne and Wang, You-Gan. (2016). Mixture of time-dependent growth models with an application to blue swimmer crab length-frequency data. Biometrics. 72(4), pp. 1255-1265. https://doi.org/10.1111/biom.12531

Movement and growth of the coral reef holothuroids Bohadschia argus and Thelenota ananas

Purcell, Steven W., Piddocke, Toby P., Dalton, Steven J. and Wang, You-Gan. (2016). Movement and growth of the coral reef holothuroids Bohadschia argus and Thelenota ananas. Marine Ecology Progress Series. 551, pp. 201-214. https://doi.org/10.3354/meps11720

Improved confidence intervals for the linkage disequilibrium method for estimating effective population size

Jones, A. T., Ovenden, J. R. and Wang, Y.-G.. (2016). Improved confidence intervals for the linkage disequilibrium method for estimating effective population size. Heredity. 117(4), pp. 217-223. https://doi.org/10.1038/hdy.2016.19