Faculty Member

Pingzhao Hu PhD,Statistical Genetics and Bioinformatics

Email Address(es)
pingzhao.hu(at)utoronto.ca
Website(s)
Lab Website: https://phulab.org/
Division(s)/Institute(s)
Biostatistics Division
Position
Associate Professor
SGS Status
Associate Member
Appointment Status
Status Only
Currently Accepting Doctoral Students?
Yes

Research Interests

My research program focuses on developing and applying machine learning and statistical techniques for integrative analysis of big multimodal health data (omics data, imaging data, administrative and electronic medical records) for precision medicine. My group also collaborates very closely with local, national and international life science scientists and clinicians on different omics projects. Current PhD/MSc students’ thesis projects are in the research areas: radiogenomics, medical imaging, drug discovery, microbiome, single cell RNA sequencing and omics data integration.

  • Genetic epidemiology and statistical genetics
  • Machine Learning and big data science
  • Deep learning-driven drug design and drug discovery
  • Machine learning-empowered radiomic and radiogenomics
  • Model-based multiomic (multimodal) data integration
  • Machine learning based spatial transcriptomics and single cell genomics

Leadership in Professional Association

2023 July  –   Now:             Chair of Student Research Presentation Award Committee, SSC

2018 August – 2020 July:   Chair of Case Studies in Data Analysis Committee, SSC                                          

Other affiliations

Tenured Associate Professor, Department of Biochemistry, Department of Computer Science, Western University

Member of Temerty Centre for AI Research and Education in Medicine (T-CAIREM)
Temerty Faculty of Medicine,  University of Toronto

Honors & Awards

  • 2022 November, Tier 2 Canada Research Chair in Computational Approaches to Health Research.
  • 2021 September, University of Manitoba Graduate Students’ Association Teaching Award. University of Manitoba Graduate Students’ Association (UMGSA), University of Manitoba. The purpose of the award is to recognize faculty members who have made, in the estimate of their graduate students, a significant contribution to their teaching profession. It is awarded to only one faculty member at the University of Manitoba in each year.
  • 2021 Jun, Ed Kroeger Mentorship Award. Health Sciences Graduate Student Association (HSGSA), University of Manitoba. It recognizes excellence and distinction in mentorship, teaching, and research. It is awarded to only one faculty member in Rady Faculty of Health Sciences in each year.
  • 2020 September, Terry G. Falconer Memorial Rh Institute Foundation Emerging Researcher Award (Interdisciplinary Category). The Winnipeg Rh Institute Foundation and the University of Manitoba. The most prestigious award for junior faculty members at the University of Manitoba, which is awarded to the only one who made Outstanding Contributions to Scholarship and Research in the Interdisciplinary Category at the University of Manitoba in each year.
  • 2020 May, MMSF Allen Rouse Basic Science Career Development Research Award. The Manitoba Medical Service Foundation (MMSF). It is awarded to only one or two of the most promising health researchers in basic
  • science in Manitoba in every three years.
  • 2019 March, Best Oral Presentation Paper Award (Corresponding author) in 2019 IEEE 7th International Conference on Bioinformatics and Computational Biology.
  • 2019 February, Best Presentation Paper Award (Corresponding author) in 2019 11th International Conference on Machine Learning and Computing.
  • 2018 May, The Interstellar Initiative Award by New York Academy of Sciences and Japan Agency for Medical Research and Development. The award recognizes “the world’s most promising Early Career Investigators in the fields of cancer, neuroscience, and artificial intelligence”.
  • 2015 Jul, New Principal Investigator Award, Careers in Cancer Research Development Program by CIHR Institute of Cancer Research and Canadian Cancer Society Research The award recognizes the research excellence of new investigators in Canada.

Selected Grants

2024 Jan – 2025 Dec: Developing an artificial intelligence-based online tool for automatic joint detection and scoring of radiographic joint damage among patients with Rheumatoid arthritis using continual learning. Arthritis Society Canada, Ignite Innovation Grants, Nominated Principal Investigator

2023 Oct – 2028 Sep: A deep-learning approach to identify inhibitors of adherent-invasive Escherichia coli in the pathogenesis of inflammatory bowel disease. CIHR, 2023 Spring Project Grants, Nominated Principal Investigator

2023 Feb – 2025 Jan: Artificial intelligence-empowered approach towards the development of combination therapies for HER2+/ER+ breast cancer. CIHR, 2022 Fall Project Grants (Priority Grant), Nominated Principal Investigator

2021 Apr – 2026 Mar: Flexible and robust deep learning models for integrative analysis of single cell RNA sequencing data. Natural Science and Engineering Research Council of Canada (NSERC), Individual Discovery Grants, Nominated Principal Investigator

Selected External Grant Reviews

  • 2021 -Now:  Canada Research Chair Tier I/Tier 2, Canadian Institute of Health Research.
  • 2021-Now:  Project Grant Competitions, Canadian Institute of Health Research.
  • 2019 -Now:  Individual Discovery Grant, Natural Sciences and Engineering Research Council.
  • 2023:  Biotechnology and Biological Sciences Research Council, UK Research and Innovation.
  • 2022: European Science Foundation.
  • 2022: NWO (Dutch Research Council) Talent Programme, Netherlands.
  • 2022: FLDOH (Florida Department of Health) Biomedical Research Programs, USA.
  • 2021:  National Institute for Health Research (NIHR), UK
  • 2021:  Breast Cancer Foundation, New Zealand.
  • 2020: National Science Center, Poland.
  • 2019:  Juvenile Diabetes Research Foundation, USA
  • 2019:  Wellcome Trust / DBT Fellowship.
  • 2019:  Deutsche Forschungsgemeinschaft (German Research Foundation), German.

Training of Highly Qualified Personnel (HQP)

2023 Oct – Now:  Supervisor. Jacqueline Jia. MSc in Artificial Intelligence Stream (Division of Biostatistics, Dalla Lana School of Public Health, University of Toronto).

2023 Oct – Now:  Supervisor. Yan Yi Li. MSc in Artificial Intelligence Stream (Division of Biostatistics, Dalla Lana School of Public Health, University of Toronto).

2023 Oct – Now:  Supervisor. Yutong Lu. MSc in Artificial Intelligence Stream (Division of Biostatistics, Dalla Lana School of Public Health, University of Toronto).

2023 Jan – Now:  Supervisor. Shijie Min. MSc Candidate in Artificial Intelligence Stream (Division of Biostatistics, Dalla Lana School of Public Health, University of Toronto). Co-supervisor: Dr. Divya Sharma, Dalla Lana School of Public Health, University of Toronto.

2023 Jan – Now:  Co-supervisor. Yuxin Shi. MSc Candidate in Artificial Intelligence Stream (Division of Biostatistics, Dalla Lana School of Public Health, University of Toronto). Supervisor: Dr. Frank Wendt, Dalla Lana School of Public Health, University of Toronto.

2022 Oct – Now:   Supervisor. Yu Shi. PhD Candidate in Artificial Intelligence Stream in the Division of Biostatistics, Dalla Lana School of Public Health, University of Toronto. Co-supervisor: Dr. Wei Xu, Dalla Lana School of Public Health, University of Toronto.

2021 Feb – Now:  Co-supervisor. Jiahui Zhang. Ph.D. Candidate in the Division of Biostatistics, Dalla Lana School of Public Health, University of Toronto. Supervisor: Dr. Wei Xu, Dalla Lana School of Public Health, University of Toronto.

2022 Oct – 2023 Aug:  Supervisor. ZhenHuan Xu. MSc in Artificial Intelligence Stream (Division of Biostatistics, Dalla Lana School of Public Health, University of Toronto).

2021 Sep – 2022 Aug:  Supervisor. Eric Lin. MSc in Artificial Intelligence Stream in the Division of Biostatistics, Dalla Lana School of Public Health, University of Toronto. Software engineer in Google, USA.

2021 Sep – 2022 Aug: Supervisor. Boyuan Liu. MSc in Artificial Intelligence Stream in the Division of Biostatistics, Dalla Lana School of Public Health, University of Toronto. Current Position: Computational Biologist in Roche Canada, Toronto

2020 Oct – 2021 Aug:  Supervisor. Haiying Zhu. MSc in Artificial Intelligence Stream in the Division of Biostatistics, Dalla Lana School of Public Health, University of Toronto.

2020 Oct – 2021 Aug:   Supervisor. Xu Li. MSc in Artificial Intelligence Stream in the Division of Biostatistics, Dalla Lana School of Public Health, University of Toronto. Current Position: Data Scientist in Just Energy, Toronto

2020 Oct – 2021 Aug:  Supervisor. Yao Li. PhD Candidate in Artificial Intelligence Stream in the Division of Biostatistics, Dalla Lana School of Public Health, University of Toronto. Co-supervisor: Dr. Wei Xu, Dalla Lana School of Public Health, University of Toronto

2019 Oct – 2021 Sep:  Supervisor. Zhongyuan Zhang. MSc in Artificial Intelligence Stream in the Division of Biostatistics, Dalla Lana School of Public Health, University of Toronto. Co-supervisor: Dr. Wei Xu, Dalla Lana School of Public Health, University of Toronto. Current Position: PhD Candidate in Biostatistics, Dalla Lana School of Public Health, University of Toronto.

2019 Oct – 2020 Aug:  Supervisor. Bowen Cheng. MSc in Artificial Intelligence Stream in the Division of Biostatistics, Dalla Lana School of Public Health, University of Toronto. Current Position: PhD Student at the Toronto Metropolitan University.

2019 Oct – 2020 Aug:  Supervisor. Andrew Tran. MSc in Artificial Intelligence Stream in the Division of Biostatistics, Dalla Lana School of Public Health, University of Toronto. Co-supervisor: Dr. Claudia DosSantos, Faculty of Medicine, University of Toronto. Current Position: Medical school (BSc Medicine) student at the University of Ottawa.

 2018 Oct – 2019 Jul:   Supervisor. Fei Zuo. MSc in the Division of Biostatistics, Dalla Lana School of Public Health, University of Toronto. Co-supervisor: Dr. Wei Xu, Dalla Lana School of Public Health, University of Toronto. Current Position: Research Biostatistician in St. Michael’s Hospital, Toronto

2017 Oct – 2019 Jul:   Supervisor. Jiahui Zhang. MSc in the Division of Biostatistics, Dalla Lana School of Public Health, University of Toronto. Co-supervisor: Dr. Wei Xu, Dalla Lana School of Public Health, University of Toronto. Current Position: PhD Candidate in Biostatistics, Dalla Lana School of Public Health, University of Toronto.

2016 Sep – 2017 Jul:   Supervisor. Wenxin Jiang. MSc in the Division of Biostatistics, Dalla Lana School of Public Health, University of Toronto. Co-supervisor: Dr. Wei Xu, Dalla Lana School of Public Health, University of Toronto. Current Position: Analyst in Canada Institute for Health Information.

2015 Oct – 2016 Jan:  Supervisor. Bingqing Shen. MSc in the Biostatistics Division, University of Toronto. Current Position: Information Management Analyst in Institute for Clinical Evaluative Sciences, Toronto.

Selected Publications

* indicates co-first author or equal contribution and ** indicates co-corresponding author. Boldface underlined names are the HQP I supervised or co-supervised and boldface name is my name.

  1. E Lin*, B Liu*, L Lac*, DLX Fung, CK Leung, P Hu (2023). scGMM-VGAE: A Gaussian mixture model-based variational graph autoencoder algorithm for clustering single-cell RNA-seq data. Machine Learning: Science and Technology. 4035013.
  2. DLX Fung, X Li, CKS Leung, P Hu (2023). A self-knowledge distillation-driven CNN-LSTM model for predicting disease outcomes using longitudinal microbiome data. Bioinformatics Advances, 3:vbad059.
  3. Q Liu, S Huang, D Desautels, KJ McManus, L Murphy, P Hu (2023). Development and validation of a prognostic 15-gene signature for stratifying HER2+/ER+ breast cancer. Computational and Structural Biotechnology Journal. 21:2940-2949.
  4. C Liu, Y Sun, R Davis, ST Cardona, P Hu (2023). ABT-MPNN: An atom-bond Transformer-based message passing neural network for molecular property prediction. Journal of Cheminformatics. 15:29.
  5. Q Liu, P Hu (2023). Radiogenomic association of deep MR imaging features with genomic profiles and clinical characteristics in breast cancer. Biomarker Research. 11:9.
  6. S Huang, P Hu**, TM Lakowski** (2023). Bioinformatics driven discovery of small molecule compounds to modulate the FOXM1 and PPARA pathway activities in breast cancer. The Pharmacogenomics Journal. 23:61-72.
  7. Q Liu, M Reed, H Zhu, Y Cheng, J Almeida, G Fruhbeck, R Ribeiro, P Hu (2022). Epigenome-wide DNA methylation and transcriptome profiling of localized and locally advanced prostate cancer: uncovering new molecular markers. Genomics, 114(5):110474.
  8. MM Islam, N Mohammed, Y Wang, P Hu (2022). Differential private deep learning models for analyzing breast cancer omics data. Frontiers in Oncology, 12:879607.
  9. Y Su, Q Liu, W Xie, P Hu. (2022) YOLO-LOGO: A transformer-based YOLO segmentation model for breast mass detection and segmentation in digital mammograms. Computer Methods and Programs in Biomedicine, 221:106903.
  10. Q Liu, P Hu (2022) An integrative computational framework for breast cancer radiogenomic biomarker discovery. Computational and Structural Biotechnology Journal, 20:2484-2494.
  11. C Liu, AM Hogan, H Sturm, MW Khan, MM Islam, ASMZ Rahman, R Davis, ST Cardona, P Hu (2022). Deep learning – driven prediction of drug mechanism of action from large-scale chemical-genetic interaction profiles. Journal of Cheminformatics, 14:12.
  12. Z Zhang, W Xu**, P Hu**. (2022) Tightly integrated multiomics-based deep tensor survival model for time-to-event prediction. Bioinformatics, 33:3259-3266.
  13. J Zammit, DLX Fung, Q Liu, CKS Leung, P Hu (2022). Semi-Supervised COVID-19 CT Image Segmentation Using Deep Generative Models. BMC Bioinformatics, 23:343.
  14. H Hadipour, C Liu, R Davis, ST Cardona, P Hu (2022). Deep clustering of small molecules at large-scale via variational autoencoder embedding and K-means. BMC Bioinformatics, 23:132.
  15. Y Li, Charles N Bernstein, W Xu, P Hu (2022). Polygenic risk and causal inference of psychiatric comorbidity in inflammatory bowel disease among patients with European ancestry. Journal of Translational Medicine, 20:43.
  16. N Feizi, Q Liu, L Murphy, P Hu (2022). Computational prediction of the pathogenic status of cancer-specific somatic variants. Frontiers in Genetics. 12:805656.
  17. Q Liu, P Hu (2022). Interpretable and extendable deep learning for pan-cancer radiogenomics research. Current Opinion for Chemical Biology. 66:102111.
  18. Q Liu*,B Cheng*, Y Jin, P Hu (2022). Bayesian tensor factorization-driven breast cancer subtyping by integrating multi-omics data. Journal of Biomedical Informatics. 125:103958.
  19. Q Liu*, D LX Fung*, L Lac*, P Hu (2021). A novel matrix profile-guided attention LSTM model for forecasting COVID-19 cases in USA. Frontiers in Public Health. 9:741030.
  20. D LX Fung*, Q Liu*, J Zammit, CK Leung, P Hu (2021). Self-supervised deep learning model for COVID-19 lung CT image segmentation highlighting putative causal relationship among age, underlying disease and COVID-19. Journal of Translational Medicine. 19:318
  21. Y Xu, J Kong, P Hu (2021). Computational drug repurposing for Alzheimer’s disease using risk genes from GWAS and single-cell RNA sequencing studies. Frontiers in Pharmacology, 12:617537.
  22. S Huang, P Hu**, T Lakowski**. (2021). Predicting breast cancer drug response using a multiple-layer cell line drug response network model. BMC Cancer. 21:648. [SRA] **Co-corresponding authors.
  23. Cruz de Jesus, MW Khan, BA Mittermuller, K Duan, P Hu**, RJ Schroth**, P Chelikani** (2021) Characterization of supragingival plaque and oral swab microbiomes in children with severe early childhood caries. Frontiers in Microbiology, 12:683685 [SRA] ** Co-corresponding author.
  24. Jia, P Hu (2021). ChrNet: A re-trainable chromosome-based 1D convolutional neural network for predicting immune cell types. Genomics. 113:2023-2031.
  25. A Bruinooge*, Q Liu*, Y Tian, W Jiang, Y Li, W Xu, CN Bernstein, P Hu (2021). Genetic predictors of gene expression associated with psychiatric comorbidity in patients with inflammatory bowel disease – a pilot study. Genomics. 113: 919-932.
  26. Ajwad, M Domaratzki, Q Liu, N Feizi, P Hu (2021). Identification of significantly mutated subnetworks in the breast cancer genome. Scientific Reports. 11:642.
  27. ​A Tran, CJ Walsh (*), Jane Batt, CC dos Santos (**), P Hu  (2020). A machine learning-based clinical tool for diagnosing myopathy using multi-cohort microarray expression profiles. Journal of Translational Medicine. 18:454.
  28. Q Liu, CK Leung, P Hu (2020). A two-dimensional sparse matrix profile DenseNet for COVID-19 diagnosis using chest CT image. IEEE Access. 8:213718-213728.
  29. Y Jin, S Jia, A Ashraf, P Hu (2020). Integrative data augmentation with U-net segmentation masks improves detection of lymph node metastases in breast cancer patients. Cancers. 12:2934.
  30. MM Islam, S Huang, R Ajwad, C Chi, Y Wang, P Hu (2020). An integrative deep learning framework for classifying molecular subtypes of breast cancer. Computational and Structural Biotechnology Journal. 18:2185-2199.
  31. Z Sun, S Huang, P Jiang, P Hu. (2020). DTF: Deep tensor factorization for predicting anticancer drug synergy. Bioinformatics. 36:4483-4489.
  32. YW Jin, P Hu (2020). Tumor-Infiltrating CD8 T Cells Predict Clinical Breast Cancer Outcomes in Young Women. Cancers. 12:1076.
  33. P Jiang*, S Huang*, Z Sun, Z Fu, T Lakowski, P Hu. (2020) Deep graph embedding for prioritizing synergistic anticancer drug combinations. Computational and Structural Biotechnology Journal. 18:427-438.
  34. VCruz de Jesus, R Shikder, D Oryniak, K Mann, A Alamri, BA Mittermuller, K Duan, P Hu**, RJ Schroth** and P Chelikani** (2020).Sex-based diverse plaque microbiota in children with severe caries. Journal of Dental Research. 99(6):703-712.
  35. S Frenkel, CN Bernstein, M Sargent, W Jiang, Q Kuang, W Xu, P Hu (2020). Copy number variation-based gene set analysis reveals cytokine signaling pathways associated with psychiatric comorbidity in patients with inflammatory bowel disease. Genomics, 112(1): 683-693.
  36. Q Liu, A Junker, K Murakami, P Hu (2019). Automated counting of cancer cell by ensembling deep features. Cells, 8:1019.
  37. S Frenkel, CN Bernstein, M Sargent, Q Kuang, W Jiang, J Wei, B Thiruvahindrapuram, B Spriggs, SW Scherer, P Hu (2019). Genome-wide analysis identifies rare copy number variations associated with inflammatory bowel disease. Plos One. 14(6):e0217846.
  38. R Shikder, P Thulasiraman, P Irani, P Hu (2019). A openAM-based tool for finding longest common subsequence in bioinformatics. BMC Research Notes, 12:220.
  39. Q Liu, P Hu (2019). Association analysis of deep genomic features extracted by denoising autoencoders with breast cancer clinical outcomes. Cancers, 11:494; doi:10.3390/cancers11040494.
  40. J You, R McLeod, P Hu (2019). Predicting drug-target interaction network using deep learning model. Computational Biology and Chemistry, 80:90-101.
  41. L Grenier, P Hu (2019). Computational drug repurposing for inflammatory bowel disease using genetic information. Computational and Structural Biotechnology Journal, 17: 127-135.
  42. J Zhang, X Ye, C Wu, H Fu**, W Xu**, P Hu** (2019). Modelling gene-environment interaction for the risk of non-Hodgkin lymphoma. Frontiers in Oncology, 8:657. **Co-corresponding authors.
  43. L Zhang*, N Feizi*, C Chi, P Hu (2018). Association analysis of somatic copy number alteration burden with breast cancer survival. Frontiers in Genetics, 9:421. *Co-first author.
  44. Y Chen, C Monteiro, A Matos, J You, A Fraga, C Pereira, V Catalán, A Rodríguez, J Gómez-Ambrosi, G Frühbeck, R Ribeiro**, P Hu** (2018). Epigenome-wide DNA methylation profiling of periprostatic adipose tissue in prostate cancer patients with excess adiposity – a pilot study. Clinical Epigenetics, 10:54. **Co-corresponding authors.
  45. C Chi, LC Murphy, P Hu (2018). Recurrent copy number alterations in young women with breast cancer. Oncotarget, 9:11541-11558.
  46. PC Havugimana*, P Hu*, A Emili (2017). Protein complexes: big data, machine learning and integrative proteomics: lessons learned over a decade of systematic analysis of protein interaction networks. Expert Review of Proteomics, 14:845-855. *Co-first author.
  47. MM Islam*, Y Tian*, Y Chen, Y Wang, P Hu. A deep learning regression model for phenotype prediction based on GAW20 genome-wide DNA methylation data. Genetic Analysis Workshop (GAW) 20. San Diego, CA, USA, March 2017. BMC Proceedings, 12(Suppl 9):21. *Co-first author.
  48. C Chi, R Ajwad, Q Kuang, P Hu (2016). A graph-based algorithm for detecting recurrent copy number variants in cancer studies. Cancer Informatics, Suppl2: 43-50.
  49. P Hu, AD Paterson (2014). Dynamic pathway analysis of genes associated with blood pressure using whole genome sequence data. BMC Proceedings 8(Suppl 1): S106. Special issue of Genetic Analysis Workshop (GAW18), Stevenson, WA, USA, October 2012.
  50. P Hu*, X Wang*, JJ Haitsma, S Furmli, H Masoom, M Liu, AS Slutsky, J Beyene, CM Greenwood, CC dos Santos (2012). Microarray meta-analysis identifies acute lung injury biomarkers in donor lungs that predict development of primary graft failure in recipients. Plos One 7:e45506.
  51. P Hu, S Bull, H Jiang (2012). Gene network modular-based classification of microarray samples. BMC Bioinformatics13 (Suppl 10): S17.
  52. W Wang, W Hu, F Hou, P Hu, Z Wei (2012). SNVerGUI: A desktop tool for variant analysis of next-generation sequencing data. Journal of Medical Genetics 12:753-755.
  53. PC Havugimana*, GT Hart*, T Nepusz*, H Yang*, AL Turinsky, Z Li, PI Wang,, DR Boutz, V Fong , S Phanse, M Babu, SA Craig, P Hu, C Wan, J Vlasblom, V Dar, A Bezginov, GW Clark, GC Wu, SJ Wodak, ERM Tillier, A Paccanaro, EM Marcotte, A Emili (2012). A census of human soluble protein complexes. Cell 150:1068-1081.
  54. Z Wei, W Wang, P Hu, GJ Lyon, H Hakonarson (2011). SNVer: a statistical tool for variant calling in analysis of pooling or individual next-generation sequencing data. Nucleic Acids Research 39:e132.
  55. P Hu, H Jiang, A Emili (2010). Predicting protein functions by relaxation labeling protein interaction network. BMC Bioinformatics 11(Suppl):S64.
  56. P Hu, CMT Greenwood, J Beyene (2009). Using the ratio of means as the effect size measure in combining results of microarray experiments. BMC System Biology 3:106.
  57. P Hu*, SC Janga*, M Babu*, JJ Diaz-Mejia*, G Butland*, W Yang, O Pogoutse, X Guo, S Phanse, P Wong, S Chandran, C Christopoulos, A Nazarians-Armavil, NK Nasseri, G Musso, M Ali, N Nazemof, V Eroukova, A Golshni, A Paccanaro, JF Greenblatt, G Moreno-Hagelseib, A Emili (2009). Global functional atlas of Escherichia coli encompassing previously uncharacterized proteins. PLoS Biology 7:e96.
  58. P Hu, G Bader, DA Wigle, A Emili (2007). Computational Prediction of cancer gene function. Nature Reviews Cancer 7:23-34.
  59. T Kislinger*, B Cox*, A Kannan*, C Chung, P Hu, A Ignatchenko, MS Scott, A Gramolini, Q Morris, T Hughes, J Rossant, B Frey, A Emili (2006). Global survey of organ and organelle protein expression in mouse: combined proteomic and transcriptomic profiling. Cell 125:173-186.
  60. P Hu, J Beyene, CMT Greenwood (2006). Testing for differential gene expression in oligonucleotide microarray experiments using weights. BMC Genomics 7:33.
  61. P Hu, CMT Greenwood, J Beyene (2005). Integrative analysis of multiple gene expression profiles with quality-adjusted effect size models. BMC Bioinformatics 6:128.
Hung, Rayjean J.
Hsieh, John
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Hung, Rayjean J.
Hsieh, John