Statistical Methodologies

Spatial data modeling and its applications

64. B. Zhu#, G. Hu, Y. Xie, L. Xu, X. Fan, and Q. Li*, "Bayesian nonparametric clustering with feature selection for spatially resolved transcriptomics data," 2024, submitted [arXiv] [GitHub]

63. X. Jiang#, L. Dong, S. Wang, Z. Wen, M. Chen, L. Xu, G. Xiao, and Q. Li*, "Reconstructing spatial transcriptomics at the single-cell resolution with BayesDeep," 2024, submitted [bioRxiv] [GitHub]

62. C. Tang, L. Dong, X. Xiao, Y. Zhou, L. Guo, Q. Li, and L. Xu, "3D reconstruction of spatial transcriptomics with spatial pattern enhanced graph convolutional neural network," 2023, submitted [GitHub]

61. X. Jiang#, D. Luo, E. Fernández#, J. Yang#, H. Li#, Y. Zhan, B. Yao, S. Bedi#, G. Xiao, X. Zhan, Q. Li*, and Y. Xie, "Spatial Transcriptomics Arena (STAr): An integrated platform for spatial transcriptomics methodology research," 2023, GigaScience, in revision [bioRxiv] [web app]

60. X. Jiang#, S. Wang, L. Guo, B. Zhu#, Z. Wen, L. Jia, L. Xu, G. Xiao, and Q. Li*, "Integrating image and molecular profiles for spatial transcriptomics analysis," 2023, Genome Biology, revision submitted [bioRxiv] [GitHub]

59. H. Li#, X. Jiang#, L. Guo, Y. Xie, L. Xu, and Q. Li*, "An interpretable Bayesian clustering approach with feature selection for analyzing spatially resolved transcriptomics data," 2023, Biometrics, revision submitted [bioRxiv] [GitHub]

58. J. Yang#, X. Jiang#, K. W. Jin#, S. Shin, and Q.Li*, "Bayesian hidden mark interaction model for detecting spatially variable genes in imaging-based spatially resolved transcriptomics data," 2024, Frontiers in Genetics, Volume 15, 1356709 [link] [GitHub]

57. X. Jiang#, G. Xiao, and Q. Li*, "A Bayesian modified Ising model for identifying spatially variable genes from spatial transcriptomics data," Statistics in Medicine, 2022, Volume 41, Issue 23, pp.4511-4743 [link] [GitHub] 

56. Q. Li*, M. Zhang, Y. Xie, and G. Xiao, "Bayesian modeling of spatial molecular profiling data via Gaussian process," Bioinformatics, 2021, Volume 37, Issue 22, pp.4129–4136 [link] [GitHub]

55. Q. Li, X. Wang, F. Liang, and G. Xiao, "A Bayesian mark interaction model for analysis of tumor pathology images," The Annals of Applied Statistics, 2019, Volume 13, Number 3, pp.1708-1732 [link] [GitHub]

54. Q. Li, X. Wang, F. Liang, F. Yi, Y. Xie, A. Gazdar, and G. Xiao, "A Bayesian hidden Potts mixture model for analyzing lung cancer pathology images," Biostatistics, 2019, Volume 20, Issue 4, pp.565-581 [link] [GitHub]

Shape analysis and its applications

53. B. Nuwagira, Q. Li, and B. Coskunuz, "Topo-CNN: Breast cancer detection with topological deep learning," 2024, submitted

52. C. Zhang#, C. Moon, G. Xiao, M. Chen, and Q. Li*, "Bayesian landmark-based shape analysis of tumor pathology images," Journal of the American Statistical Association, 2023, accepted [link] [GitHub]

51. C. Moon, Q. Li, and G. Xiao, "Predicting survival outcomes using topological features of tumor pathology images," The Annals of Applied Statistics, 2023, Volume 17, Number 3, pp.2192-2211 [link] [GitHub]

50. E. Fernández#, S. Yang, S. H. Chiou, C. Moon, C. Zhang#, B. Yao, G. Xiao, and Q. Li*,  "SAFARI: Shape analysis for AI-segmented images," BMC Medical Imaging, 2022, Volume 22, Number 129 [link] [GitHub] [web app]

Temporal data modeling and its applications

49. T. Bedi#, Y. Xu, and Q. Li* "Bayesian segmentation modeling of epidemic growth," submitted [arXiv] [GitHub]

48. Q. Li*, "Book review: Bayesian analysis of infectious diseases: COVID-19 and beyond," The American Statistician, 2022, Volume 76, Number 2, pp.199 [link]

47. S. Jiang#, Q. Zhou, X. Zhan, and Q. Li*, "BayesSMILES: Bayesian segmentation modeling for longitudinal epidemiological studies," Journal of Data Science, 2021, Volume 19, Number 3, pp.365-389 [link] [GitHub] [web app]

46. Q. Li*, T. Bedi#, C. U. Lehmann, G. Xiao, and Y. Xie, "Evaluating short-term forecasting of COVID-19 cases among different epidemiological models under a Bayesian framework," GigaScience, 2021, Volume 10, Issue 2, giab009 [link] [GitHub] [web app]

Deep learning and its applications

45. Q. Li*, "Commentary: AI-powered Bayesian statistics in biomedicine," Statistics in Biosciences, 2023, Volume 15, pp.737-749 [link]

44. L. Zhang, R. Rong, Q. Li^, D. M. Yang, B. Yao, D. Luo, X. Zhang, X. Zhu, J. Luo, Y. Liu, X. Yang, X. Ji, Y. Xie, Y. Sha, Z. Li, and G. Xiao, "A deep learning-based model for screening and staging pneumoconiosis," Scientific Reports, 2021, Volume 11, Number 1, 2201 [link]

High-dimensional data modeling and its applications

43. T. Bedi#, B. Zhu#, M. L. Neugent,  K. C. Lutz#, N. J. De Nisco, and Q. Li*, "Bayesian modeling of co-occurrence microbial interaction networks," submitted [arXiv] [GitHub]

42. Q. Li, S. Jiang#, G. Xiao, A. Y. Koh, M. L. Neugent, N. J. De Nisco, Y. Xie, and X. Zhan, "Bayesian modeling of metagenomic sequencing data for discovering microbial biomarkers in colorectal cancer," The Annals of Applied Statistics, in revision [arXiv] [GitHub]

41. K. C. Lutz#, M. L. Neugent, T. Bedi#, N. J. De Nisco, and Q. Li*, "A generalized Bayesian stochastic block model for microbiome community detection," Statistics in Medicine, revision submitted [arXiv] [GitHub]

40. K. C. Lutz#, S. Jiang#, M. L. Neugent, N. J. De Nisco, X. Zhan, and Q. Li*, "A survey of statistical methods for microbiome data analysis," Frontiers in Applied Mathematics and Statistics, 2022, Volume 8, 884810 [link]

39. F. Zhou, K. He, Q. Li, R. S. Chapkin, and Y. Ni, "Bayesian biclustering for microbial metagenomic sequencing data via multinomial matrix factorization," Biostatistics, 2022, Volume 23, Issue 3, pp.891–909 [link]

38. S. Jiang#, G. Xiao, A. Y. Koh, Q. Li*, and X. Zhan, "A Bayesian zero-inflated negative binomial regression model for the integrative analysis of microbiome data," Biostatistics, 2021, Volume 22, Issue 3, pp.522-540 [link] [GitHub]

37. S. Jiang#, G. Xiao, A. Y. Koh, Q. Li*, and X. Zhan, "HARMONIES: A hybrid approach for microbiome networks inference via exploiting sparsity," Frontiers in Genetics, 2020, Volume 11, 445 [link] [GitHub] [web app]

36. G. Jia, X. Wang, Q. Li, W. Lu, X. Tang, I. Wistuba, and Y. Xie, "RCRnorm: An integrated system of random-coefficient hierarchical regression models for normalizing NanoString nCounter data," The Annals of Applied Statistics, 2019, Volume 13, Number 3, pp.1617-1647 [link]

35. M. Zhang, Q. Li^, and Y. Xie, "A Bayesian hierarchical model for analyzing methylated RNA immunoprecipitation sequence data," Quantitative Biology, 2018, Volume 6, Issue 3, pp.275-286 [link][GitHub]

34. Q. Li, A. Cassese, M. Guindani, and M. Vannucci,  "Bayesian negative binomial mixture regression models for the analysis of sequence count and methylation data," Biometrics, 2018, Volume 75, Issue 1, pp.183-192 [link] [GitHub]

33. Q. Li, M. Guindani, B. J. Reich, H. D. Bondell, and M. Vannucci, "A Bayesian mixture model for clustering and selection of feature occurrence rates under mean constraints," Statistical Analysis and Data Mining, 2017, Volume 10, Issue 6, pp.393-409 [link] [GitHub] 

• An earlier version of this paper was recognized through the 2017 ASA Section on Bayesian Statistical Science (SBSS) Student Paper Competition Award

• Top 20 most read paper in Statistical Analysis and Data Mining during 2017-2018

Sequence analysis and its applications

32. Q. Li*, D. B. Dahl, M. Vannucci, H. Joo, and J. W. Tsai , "KScons: A Bayesian approach for protein residue contact prediction using the knob-socket model of protein tertiary structure," Bioinformatics, 2016, Volume 32, Issue 24, pp.3774-3781 [link]

31. Q. Li, D. B. Dahl, M. Vannucci, H. Joo, and J. W. Tsai , "Bayesian modeling of protein primary sequence for secondary structure prediction," PLoS One, 2014, Volume 9, Issue 10, e109832 [link] [GitHub] [web app] 

30. D. B. Dahl, Q. Li, M. Vannucci, H. Joo, and J. W. Tsai, "A Bayesian model for protein secondary structure prediction," The 59th World Statistics Congress (WSC 2013) [link]

29. T. Liang, X. Fan, Q. Li, and S. -Y. R. Li , "Detection of short dispersed tandem repeats by reversible jump Markov chain Monte Carlo," Nucleic Acids Research, 2012, Volume 40, Issue 19, pp. e147 [link]

28. Q. Li, X. Fan, T. Liang, and S. -Y. R. Li , "An Markov chain Monte Carlo algorithm for detecting short adjacent repeats shared by multiple sequences," Bioinformatics, 2011, Volume 27, Issue 13, pp.1772-1779 [link]

27. Q. Li, T. Liang, X. Fan, C. Xu, W. Yu, and S. -Y. R. Li, "An automatic procedure to search highly repetitive sequences in genome as fluorescence in situ hybridization probes and its application on brachypodium distachyon," 2010 IEEE International Conference on Bioinformatics & Biomedicine (BIBM 2010), pp. 563-568 [link]

26. J. Xu, Q. Li, X. Fan, V. O. K. Li, and S. -Y. R. Li, "An evolutionary Monte Carlo algorithm for identifying short adjacent repeats in multiple sequences," 2010 IEEE International Conference on Bioinformatics & Biomedicine (BIBM 2010), pp. 643-648 [link]

25. Q. Li, T. Liang, S. -Y. R. Li, and X. Fan, "Bayesian approach for identifying short adjacent repeats in multiple DNA sequences," 2010 International Conference on Bioinformatics & Computational Biology (BIOCOMP'10), Volume 1, pp. 255-261 [paper]

Collaborative Research

24. D. Luo, S. Robertson, Y. Zhan, R. Rong, S. Wang, X. Jiang#, S. Yang, S. Palmer, L. Jia, Q. Li, G. Xiao, and X. Zhan, "ScopeViewer: A browser-based solution for visualizing large biological images," submitted

23. M. Zhang, S. Jiang#, B. Yao, Q. Li, Y. Chen, D. Luo, D. M. Yang, T. Wang, Y. Xie, X. Zhan, and G. Xiao, "SpaCeV: A visualization tool for cell spatial organization," submitted [web app]

22. A. Chao, Q. Li, and Z. Liu, "Efficacy of Chat GPT correlations vs. co-occurrence networks in deciphering Chinese history," 2024 Digital History, in revision

21. R. Rong, K. Denton, K. W. Jin, J. Kozlitina, Z. Wen, S. Lyon, C. Wise, B. Beutler, D. M. Yang, Q. Li, J. J. Rios, G. Xiao, "Deep learning-based automated measurement of murine bone length in radiographs," 2024, Bioengineering, in revision

20. J. G. Gadhvi, P. R. M. Kenee, K. C. Lutz#, F. Khan, Q. Li, P. Zimmern, and N. J. De Nisco, "Bladder-resident bacteria associated with increased risk of recurrence after electrofulguration in women with antibiotic-recalcitrant urinary tract infection," 2024, eBioMedicine, in revision

19. M. Joseph#, Q. Li, and S. Shin, "Health diagnosis associated with COVID-19 death in the United States: A retrospective cohort study using electronic health records," 2023, PLoS One, revision submitted

18. T. Ebrahimzadeh, K. C. Lutz#, U. Basu, J. Gadhvi, J. V. Komarovsky, Q. Li, P. Zimmern, and N. J. De Nisco, "Inflammatory markers for improved recurrent urinary tract infection diagnosis in women," Life Science Alliance, 2024, accepted [link]

17. K. W. Jin#, Q. Li, Y. Xie, and G. Xiao, "Artificial intelligence in mental healthcare: A scoping review," British Journal of Radiology, 2023, accepted [link]

16. M. L. Neugent, A. Kumar, N. V. Hulyalkar, K. C. Lutz#, V. H. Nguyen, J. Fuentes, C. Zhang#, A. Nguyen, B. M. Sharon, A. Kuprasertkul, A. P. Arute, T. Ebrahimzadeh, N. Natesan, C. Xing, V. Shulaev, Q. Li, P. E. Zimmern, K. L. Palmer, and N. J. De Nisco, "Recurrent urinary tract infection and estrogen shape the taxonomic ecology and functional potential of the postmenopausal urobiome," Cell Report Medicine,  2022, Volume 3, Issue 10, 100753 [link]

15. S. Yang, S. Wang, Y. Wang, R. Rong, J. Kim, B. Li, A. Y. Koh, G. Xiao, Q. Li, D. Liu, and X. Zhan, "MB-SupCon: Microbiome-based predictive models via supervised contrastive learning," Journal of Molecular Biology, 2022, Volume 434, Issue 15, 167693 [link] 

14. A. S. Chao, J. Yang#, Z. Liu, and Q. Li, "Network of words: A co-occurrence analysis of nation-building terms in the writings of Liang Qichao and Chen Duxiu," Journal of Historical Network Research, 2021, Volume 5, pp.154-186 [link]

13. A. Czysz, B. L. Mason, Q. Li, C. Chin-Fatt, A. Minhajuddin, T. Carmody, and M. H. Trivedi, "Comparison of inflammatory markers as moderators of depression outcomes: A COMED study,"  Journal of Affective Disorders, 2021, Volume 295, pp.1066-1071 [link]

12. T. Ebrahimzadeh, A. Kuprasertkul, M. L. Neugent, K. C. Lutz#, J. Fuentes, J. Gadhvi, F. Khan, C. Zhang#, B. Sharon, K. Orth, Q. Li, P. Zimmern, and N. J. De Nisco, "Urinary prostaglandin E2 is a biomarker for recurrent urinary tract infection in postmenopausal women," Life Science Alliance, 2021, Volume 4, Number 7, e202000948 [link]

11. J. Kim, S. Jiang#, Y. Wang, G. Xiao, Y. Xie, D. Liu, Q. Li, A. Y. Koh, and X. Zhan, "MetaPrism: A versatile toolkit for joint taxa/gene analysis of metagenomic sequencing data," G3: Genes, Genomes, Genetics, 2021, Volume 11, Issue 4, jkab046 [link] [GitHub] 

10. R. Rong, S. Jiang#, L. Xu, G. Xiao, Y. Xie, D. Liu, Q. Li, and X. Zhan, "MB-GAN: Microbiome simulation via generative adversarial network," GigaScience,  2021, Volume 10, Issue 2, giab005 [link] [GitHub]

9. M. Zhang, T. Sheffield, X. Zhan, Q. Li, D. M. Yang, Y. Wang, S. Wang, Y. Xie, T. Wang, and G. Xiao, "Spatial molecular profiling: Platforms, applications and analysis tools," Briefings in Bioinformatics, 2021, Volume 22, Issue 3, bbaa145 [link]

8. B. L. Mason, Q. Li, A. Minhajuddin, A. H. Czysz, L. A. Coughlin, S. Hussain, A. Y. Koh, and M. H. Trivedi, "Reduced anti-inflammatory gut microbiota are associated with depression and anhedonia," Journal of Affective Disorders, 2020, Volume 266, pp.394-401 [link]

7. M. Zhang, Q. Li, D. Yu, B. Yao, W. Guo, Y. Xie, and G. Xiao, "GeNeck: A web-based tool for gene network construction and visualization," BMC Bioinformatics, 2019, Volume 20, Number 12 [link] [web app]

6. A. S. Chao, Q. Li, and Z. Liu, "Integrating latent Dirichlet allocation and Poisson graphical model: A deep dive into the writings of Chen Duxiu, co-founder of the Chinese Communist Party," 2018 Digital Humanities (DH 2018) [link]

5. A. S. Chao and Q. Li, "A new and improved method to text-mining in Chinese: Closer language segmentation in detecting the shifting meaning of patriotism," 2017 Digital Humanities (DH 2017) [link]

4. L. Cai, Q. Li, Y. Du, J. Yun, Y. Xie, R. J. DeBerardinis, and G. Xiao, "Genomic regression analysis of coordinated expression," Nature Communications, 2017, Volume 8, Number 2187 [link] [web app]

3. J. Xu, Q. Li, X. Fan, V. O. K. Li, and S. -Y. R. Li, "Improved short adjacent repeat identification using three evolutionary Monte Carlo schemes," International Journal on Data Mining and Bioinformatics, 2013, Volume 8, Number 4, pp.462-479 [link] 

2. Y. Li, M. Chen, Q. Li, and W. Zhang, "Enabling multi-level trust in privacy preserving data mining," IEEE Transaction on Knowledge and Data Engineering, 2012, Volume 24, Issue 9, pp. 1598-1612 [link]

1. J. Xu, A. Y. S. Lam, V. O. K. Li, Q. Li, and X. Fan, "Short adjacent repeat identification based on chemical reaction optimization," 2012 IEEE World Congress on Computational Intelligence (WCCI 2012), pp. 1-8 [link]