Dalhousie University

Mathmatics and Statistics

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Publications:

-Molly G. Hayes, Morgan G. I. Langille, Hong Gu. (2023) Cross-Study Analyses of Microbial Abundance Using Generalized Common Factor Methods. BMC Bioinformatics. https://doi.org/10.1186/s12859‑023‑05509‑4.

-Yun Cai, Hong Gu and Toby Kenney. (2023) Rank Selection for Non-negative Matrix Factorization. Statistics in Medicine. https://doi.org/10.1002/sim.9934.

-Libai Xu, Dehan Kong, Lidan Wang, Hong Gu, Tobias Kenney, Ximing Xu. (2023) Proportional stochastic generalized Lotka–Volterra model with an application to learning microbial community structures. Applied Mathematics and Computation. 448, 2023. https://doi.org/10.1016/j.amc.2023.127932.

-Joerg Behnke, Yun Cai, Hong Gu, Julie LaRoche. (2023) Short-term response to iron resupply in an iron-limited open ocean diatom reveals rapid decay of iron-responsive transcripts. PLoS ONE 18(1): e0280827. https://doi.org/10.1371/journal.pone.0280827.

-Chaoyue Liu, Toby Kenney, Robert G. Beiko , and Hong Gu. (2022) The Community Coevolution Model with Application to the Study of Evolutionary Relationships between Genes based on Phylogenetic Profiles. Systematic Biology, syac052, https://doi.org/10.1093/sysbio/syac052.

-Paul Bjorndahl, Joseph P Bielawski, Lihui Liu, Wei Zhou & Hong Gu, (2022) Novel application of survival models for predicting microbial community transitions with variable selection for eDNA. Applied and Environmental Microbiology. 88(6).

-Q. Zeng, Z. Yang, …, T. Kenney, H. Gu, S. Feng, S. Li, Y. He, X. Xu and W. Dai (2021). Association between metabolic status and gut microbiome in obese populations.  Microbial Genomics 7(8).

-L. Liu, H. Gu, J. Van Limbergen, T. Kenney. (2021).  SuRF: a New Method for Sparse Variable Selection, with Application in Microbiome Data Analysis. Statistics in Medicine. 40(4) 897-919. https://doi.org/10.1002/sim.8809

-Kenney T, Gu H, Huang T. (2021) Poisson PCA: Poisson measurement error corrected PCA, with application to microbiome data. Biometrics. 2021, 77(4): 1369-1384.  doi: 10.1111/biom.13384.

-Mia T. Parenteau, Hong Gu, Bernie J. Zebarth, Athyna N. Cambouris, Jean Lafond, Alison Nelson, Judith Nyiraneza, Charlotte Davidson, Martin Lagüe, José Héctor Galvez, Martina V. Strömvik  and Helen H. Tai  (2020).  Data Mining Nitrogen‐Responsive Gene Expression for Source–Sink Relations and Indicators of N Status in Potato. Agronomy 2020, 10, 1617; doi:10.3390/agronomy10101617.

-T. Kenney, J. Gao, H. Gu. (2020) Application of OU processes to modelling temporal dynamics of the human microbiome, and calculating optimal sampling schemes. BMC Bioinformatics, 21:450  https://doi.org/10.1186/s12859-020-03747-4. https://rdcu.be/b8qva

-Zeng, Q., Li, D., He, Y. … Gu H.,Li Y., Zhou K., Li S., Dai W.,  Discrepant gut microbiota markers for the classification of obesity-related metabolic abnormalities. Scientific Reports 9, 13424 (2019). https://www.nature.com/articles/s41598-019-49462-w

-Wei Chen, Toby Kenney, Joseph Bielawski and Hong Gu (2019). Testing adequacy for DNA substitution Models. BMC Bioinformatics 2019 20:349. https://rdcu.be/bHn14

-Katherine A. Dunn, Toby Kenney, Hong Gu and Joseph P. Bielawski (2019). Improved inference of site-specific positive selection under a generalized parametric codon model when there are multinucleotide mutations and multiple nonsynonymous rates. BMC Evolutionary Biology, 201919:22 https://doi.org/10.1186/s12862-018-1326-7

-Chaoyue Liu*, Benjamin Wright*, Emma Allen-Vercoe, Hong Gu and Robert Beiko (2018). Phylogenetic clustering of genes reveals shared evolutionary trajectories and putative gene functions. Genome Biology and Evolution, evy178, https://doi.org/10.1093/gbe/evy178

-Moamen Bydoun, Andra Sterea, Henry Liptay, Andrea Uzans, Weei-Yuan Huang, Gloria J. Rodrigues, Ian Weaver, Hong Gu, David M Waisman (2018).  S100A10, a Novel Biomarker in Pancreatic Ductal Ade-nocarcinoma. Molecular Oncology. doi: 10.1002/1878-0261.12356.

-Yun Cai, Hong Gu and Toby Kenney (2017) Learning Microbial Community Structures with Supervised and Unsupervised Non-negative Matrix Factorization. Microbiome, 5:110. https://doi.org/10.1186/s40168-017-0323-1.

-Nidhin Nandhakumar, Ehsan Sherkat, Evangelos E.Milios, Hong Gu and Michael Butler (2017). Clinically Significant Information Extraction from Radiology Report. The 17^th ACM Symposium on Document Engineering (DocEng 2017) (refereed conference in CS).

-Mahdi Shafiei, Katherine Dunn, Eva Boon, Shelley MacDonald, David Walsh, Hong Gu and Joseph P Bielawski (2015).  BioMiCo: a supervised Bayesian model for inference of microbial community structure. Microbiome, 3(8).

-Mahdi Shafiei, Katherine A. Dunn, Hugh Chipman, Hong Gu, Joseph P. Bielawski (2014). BiomeNet: A Bayesian model for inference of metabolic divergence among microbial communities. PLoS Comput Biol 10(11).

-Melanie Abeysundera, Toby Kenney, Chris Field, Hong Gu (2014). Combining Distance Matrices on Identical Taxon Sets for Multi-gene Analysis with Singular Value Decomposition.  PLOS ONE, available online at http://dx.plos.org/10.1371/journal.pone.0094279. 

-Toby Kenney and Hong Gu (2012). Hessian Calculation for Phylogenetic Likelihood based on the Pruning Algorithm and its Applications. Statistical Applications in Genetics and Molecular Biology. 11(4): Article 14. PDF.

-Hong Gu, Katherine A. Dunn and Joseph P. Bielawski (2012) Likelihood Based Clustering (LiBaC) for Codon Models in "Codon Evolution: mechanisms and models", edited by GM Cannarozzi and A Schneider. Oxford University Press.

-Melanie Abeysundera, Chris Field and Hong Gu (2012). Phylogenetic analysis based on spectral methods. Molecular Biology and Evolution. 2012: 29(2):579-597. PDF.

-Gu, H., Kenney, T. and Zhu, M. (2010). Partial Generalized Additive Models: An Information-theoretic Approach for Dealing with Concurvity and Selecting Variables. Journal of Computational and Graphical Statistics. September 1, 2010, 19(3): 531-551. PDF.

-Katherine A. Dunn, Joseph P. Bielawski, Todd J. Ward, Caroline Urquhart and Hong Gu  (2009). Reconciling ecological and genomic divergence among lineages of Listeria under an "extended mosaic genome concept". Mol Biol Evol. MBE Advance Access published online

-Fallah N., Gu H., Mohammad K., Seyyedsalehi S.A., Nourijelyani K and Eshraghian M. (2009). Nonlinear Poisson regression using neural networks: a simulation study. Neural Computing & Applications.  PDF.

-Morine MJ, Gu H, Myers RA, Bielawski JP. (2009). Trade-Offs Between Efficiency and Robustness in Bacterial Metabolic Networks Are Associated with Niche Breadth. J Mol Evol.

-Chakraborty, H. and Gu, H. (2009). A Mixed Model Approach for Intent-to-Treat Analysis in Longitudinal Clinical Trials with Missing Values. RTI Press publication No. MR-0009-0903. PDF

-Xiaofei Shi, Hong Gu and Chris Field (2008). Pattern classification of phylogeny signals. Statistical Applications in Genetics and Molecular Biology: Vol. 7: Iss. 1, Article 30.Available at: http://www.bepress.com/sagmb/vol7/iss1/art30.

-Le Bao, Hong Gu, Katherine A. Dunn and Joe Bielawski (2008). Likelihood Based Clustering (LiBaC) for Codon Models, a method for grouping sites according to similarities in the underlying process of evolution. Mol Biol Evol. Vol. 25(9), pp. 1995-2007. PDF

- Emad Bahrami Samani, M. Mehdi Homayounpour and Hong Gu (2007). A Novel Hybrid GMM/SVM Architecture for Protein Secondary Structure Prediction. Applications of Fuzzy Sets Theory. Springer Berlin / Heidelberg, Volume 4578/2007.

-Le Bao, Hong Gu, Katherine Dunn and Joe Bielawski (2007). Methods for selecting fixed-effect models for heterogeneous codon evolution, with comments on their application to gene and genome data. BMC Evolutionary Biology 2007, 7(suppl 1):S5 . PDF.

-Wing K. Fung, Hong Gu, Liming Xiang and Kelvin K. W. Yau (2006). Assessing local influSence in principal component analysis with application to hematology study data. Statistics in Medicine, Vol. 26, Issue 13 , Pages 2730-2744.

-Xiaofei Shi, Hong Gu and Chris Field (2006). Testing a clade in Phylogenetic trees. Mol Biol Evol. Vol. 23, Number 10, pp. 1976-1983(8) .Abstract. PDF.

-Krista Collins, Hong Gu, and Chris Field (2006) "Examining Protein Structure and Similarities By Spectral Analysis Technique," Statistical Applications in Genetics and Molecular Biology: Vol. 5: No. 1, Article 23. Available at: http://www.bepress.com/sagmb/vol5/iss1/art23. R or Splus codes: Spectral envelope. Spectral covariance. For an example of input data format, see the output of the data formating program on aa01.txt.

-Lorenzo Vega-Montoto, Hong Gu and Peter D. Wentzell. (2005). Mathematical Improvements to Maximum Likelihood Parallel Factor Analysis: Theory and Simulations. Journal of Chemometrics. 19: 216-235. PDF.

-Xiaofei Shi, Hong Gu, Edward Susko and Chris Field (2005). The Comparison of the Confidence Regions in Phylogeny. Mol Biol Evol. 22: 2285-2296.

- Hugh A. Chipman and Hong Gu (2005). Interpretable Dimension Reduction. Journal of Applied Statistics, 32, 969-987. PDF.

- Hugh A. Chipman and Hong Gu (2001). "Discussion of 'Flexible regression modeling with adaptive logistic basis functions' by P. M. Hooper''. Canadian Journal of Statistics, 29,370--374.

- Hong Gu and Wing K. Fung (2001). Influence diagnostics in common principal components analysis. Journal of Multivariate Analysis, 79, 275-294.

- Hong Gu and Wing K. Fung (2001). Local influence for the restricted likelihood with applications. Sankhya, 63, 250-259.

- Hong Gu and Wing K. Fung (2000). Influence diagnostics in common canonical variates analysis. Annals of the Institute of Statistical Mathematics (AISM), 52, 753-766.

- Wing K. Fung, Ruben H. Zamar and Hong Gu (1999). Robust estimation of coefficient of variation. Technical Report.

- Hong Gu and Wing K. Fung (1998). Assessing local influence in canonical correlation analysis. Annals of the Institute of Statistical Mathematics (AISM), 50, PP.755-772.

- Wing K. Fung and Hong Gu (1998). The second order approximation to sample influence curve in canonical correlation analysis. Psychometrika, 63, PP. 263-269.

- Wing K. Fung and Hong Gu (1998). Discussion on the paper by Hodges (Some algebra and geometry for hierarchical models, applied to diagnostics). J. R. Statist. Soc. B, 60, PP.531-532.