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Xuan Guo

Title: Associate Professor

Department: Computer Science and Engineering

College: College of Engineering

Curriculum Vitae

Curriculum Vitae Link

Education

  • PhD, Georgia State University, 2015
    Major: Computer Science
    Specialization: Bioinformatics
  • MS, Wuhan University, 2011
    Major: Computer Science
  • BS, Southwest University, 2009
    Major: Computer Science

Current Scheduled Teaching

CSCE 6950.832Doctoral DissertationFall 2024
CSCE 6900.832Special ProblemsFall 2024

Previous Scheduled Teaching

CSCE 6940.830Individual ResearchSummer 10W 2024
CSCE 5150.002Analysis of Computer AlgorithmsSpring 2024 SPOT
CSCE 6950.922Doctoral DissertationSpring 2024
CSCE 3110.002Data Structures and AlgorithmsFall 2023 Syllabus SPOT
CSCE 6950.832Doctoral DissertationFall 2023
CSCE 4999.732Senior ThesisFall 2023
CSCE 5150.002Analysis of Computer AlgorithmsSpring 2023 Syllabus SPOT
CSCE 6950.922Doctoral DissertationSpring 2023
CSCE 5150.003Analysis of Computer AlgorithmsFall 2022 Syllabus SPOT
CSCE 3110.002Data Structures and AlgorithmsFall 2022 Syllabus SPOT
CSCE 4890.732Directed StudyFall 2022
CSCE 6950.832Doctoral DissertationFall 2022
CSCE 6940.832Individual ResearchFall 2022
CSCE 4890.761Directed StudySpring 2022
CSCE 6950.922Doctoral DissertationSpring 2022
CSCE 6940.713Individual ResearchSpring 2022
CSCE 3110.002Data Structures and AlgorithmsFall 2021 Syllabus SPOT
CSCE 6950.713Doctoral DissertationFall 2021
CSCE 6940.713Individual ResearchFall 2021
CSCE 3110.001Data Structures and AlgorithmsSpring 2021 Syllabus SPOT
CSCE 3110.004Data Structures and AlgorithmsSpring 2021 Syllabus SPOT
CSCE 6950.713Doctoral DissertationSpring 2021
CSCE 6940.713Individual ResearchSpring 2021
CSCE 3110.002Data Structures and AlgorithmsFall 2020 Syllabus SPOT
CSCE 3110.005Data Structures and AlgorithmsFall 2020 Syllabus SPOT
CSCE 6950.713Doctoral DissertationFall 2020
CSCE 6940.713Individual ResearchFall 2020
CSCE 6900.713Special ProblemsFall 2020
CSCE 3110.001Data Structures and AlgorithmsSpring 2020 Syllabus
CSCE 6940.713Individual ResearchSpring 2020
CSCE 6940.735Individual ResearchSpring 2020
BIOL 4810.001BioComputingFall 2019 Syllabus SPOT
BIOL 5810.001BioComputingFall 2019 SPOT
CSCE 4810.001BioComputingFall 2019 Syllabus SPOT
CSCE 4810.201BioComputingFall 2019 Syllabus SPOT
CSCE 5810.001BioComputingFall 2019 SPOT
CSCE 5810.600BioComputingFall 2019 SPOT
MATH 4810.001BiocomputingFall 2019 Syllabus SPOT
CSCE 6940.713Individual ResearchFall 2019
CSCE 6940.713Individual ResearchSummer 10W 2019
CSCE 3110.001Data Structures and AlgorithmsSpring 2019 Syllabus SPOT
CSCE 6940.713Individual ResearchSpring 2019
BIOL 4810.001BioComputingFall 2018 Syllabus SPOT
BIOL 5810.001BioComputingFall 2018 SPOT
CSCE 4810.001BioComputingFall 2018 Syllabus SPOT
CSCE 4810.201BioComputingFall 2018
CSCE 5810.001BioComputingFall 2018 SPOT
MATH 4810.001BiocomputingFall 2018 Syllabus SPOT
CSCE 6940.713Individual ResearchFall 2018
CSCE 4600.001Introduction to Operating SystemsSpring 2018 Syllabus SPOT
CSCE 4600.201Introduction to Operating SystemsSpring 2018 SPOT
CSCE 4600.203Introduction to Operating SystemsSpring 2018 SPOT
CSCE 4600.204Introduction to Operating SystemsSpring 2018 SPOT
CSCE 4600.205Introduction to Operating SystemsSpring 2018 SPOT
BIOL 5810.001BioComputingFall 2017 SPOT
CSCE 4810.001BioComputingFall 2017 Syllabus SPOT
CSCE 4810.201BioComputingFall 2017 Syllabus
CSCE 5810.001BioComputingFall 2017 SPOT
MATH 4810.001BiocomputingFall 2017 Syllabus SPOT
MATH 5700.001Selected Topics in Contemporary MathematicsFall 2017 SPOT

Published Intellectual Contributions

    Conference Proceeding

  • Zhuang, C., Yuan, X., Guo, X., Wei, Z., Xu, J., Fan, Y. (2023). Improved Convolutional Neural Networks by Integrating High-frequency Information for Image Classification. Proceedings of the 2023 2nd Asia Conference on Algorithms, Computing and Machine Learning. 429--434.
  • Zhuang, C., Yuan, X., Guo, X., Wei, Z., Xu, J., Fan, Y. (2023). Improved Convolutional Neural Networks by Integrating High-frequency Information for Image Classification. Proceedings of the 2023 2nd Asia Conference on Algorithms, Computing and Machine Learning. 429--434.
  • Ebrahimi, S., Guo, X. (2023). Transformer-based de novo peptide sequencing for data-independent acquisition mass spectrometry. 2023 IEEE 23nd International Conference on Bioinformatics and Bioengineering (BIBE). 1--4.
  • He, J., Liu, O., Guo, X. (2022). Deep Learning Based MS2 Feature Detection for Data-Independent Shotgun Proteomics. 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). 2342--2348.
  • Wang, H., Qiao, C., Guo, X., Sha, Y., Gong, Z. (2022). Exploiting Anomalous Structural Nodes in Dynamic Social Networks. Companion Proceedings of the Web Conference 2022. 388--388.
  • Wang, S., Feng, S., Pan, C., Guo, X. (2022). FineFDR: Fine-grained Taxonomy-specific False Discovery Rates Control in Metaproteomics. 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). 287--292.
  • Li, J., Pan, C., Guo, X. (2022). IDIA: An Integrative Signal Extractor for Data-Independent Acquisition Proteomics. 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). 266--269.
  • Li, J., Guo, X., Yuan, X. (2020). Semi-global Alignment of Range Videos. International Conference on Urban Intelligence and Applications. 18--26.
  • Hosseini, S., Guo, X. (2019). Deep Convolutional Neural Network for Automated Detection of Mind Wandering Using EEG Signals. Proceedings of the 10th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics. 314–319. New York, NY, USA, Association for Computing Machinery.
  • Liu, B., Guo, X., Zhang, J. (2018). Bayesian Bi-Cluster Change-Point Model for Exploring Functional Brain Dynamics. (1-60132-471-5) 85-91. Int'l Conf. Bioinformatics and Computational Biology.
  • Yu, N., Gu, F., Guo, X., He, Z. (2015). A fine-grained flow control model for cloud-assisted data broadcasting. Proceedings of the 18th Symposium on Communications & Networking. 24–31.
  • Guo, X., Zhang, J., Cai, Z., Du, D., Pan, Y. (2015). DAM: A Bayesian method for detecting genome-wide associations on multiple diseases. International Symposium on Bioinformatics Research and Applications. 96–107.
  • Yu, N., Guo, X., Gu, F., Pan, Y. (2015). DNA AS X: An information-coding-based model to improve the sensitivity in comparative gene analysis. International Symposium on Bioinformatics Research and Applications. 366–377.
  • Lian, Z., Li, X., Pan, Y., Guo, X., Chen, L., Chen, G., Wei, Z., Liu, T., Zhang, J. (2015). Dynamic Bayesian brain network partition and connectivity change point detection. Computational Advances in Bio and Medical Sciences (ICCABS), 2015 IEEE 5th International Conference on. 1–6.
  • Yu, N., Guo, X., Zelikovsky, A., Pan, Y. (2015). GaussianCpG: A Gaussian model for detection of human CpG island. Computational Advances in Bio and Medical Sciences (ICCABS), 2015 IEEE 5th International Conference on. 1–1.
  • Guo, X., Ding, X., Meng, Y., Pan, Y. (2013). Cloud computing for de novo metagenomic sequence assembly. International Symposium on Bioinformatics Research and Applications. 185–198.
  • Zeng, T., Guo, X., Liu, J. (2010). Discovering negative correlated gene sets from integrative gene expression data for cancer prognosis. Bioinformatics and Biomedicine (BIBM), 2010 IEEE International Conference on. 489–492.
  • Journal Article

  • Wang, H., Mi, J., Guo, X., Hu, P. (2023). Meta-learning adaptation network for few-shot link prediction in heterogeneous social networks. Other. 60 (5) 103418. Elsevier.
  • Feng, S., Ji, H., Wang, H., Zhang, B., Sterzenbach, R., Pan, C., Guo, X. (2022). MetaLP: An integrative linear programming method for protein inference in metaproteomics. PLOS Computational Biology. 18 (10) e1010603. Public Library of Science San Francisco, CA USA.
  • Jain, K.G., Zhao, R., Liu, Y., Guo, X., Yi, G., Ji, H. (2022). Wnt5a/$\beta$-catenin axis is involved in the downregulation of AT2 lineage by PAI-1. Other. 323 (5) L515--L524. American Physiological Society Rockville, MD.
  • Feng, S., Sterzenbach, R., Guo, X. (2021). Deep learning for peptide identification from metaproteomics datasets. Other. 247 104316. Elsevier.
  • Wang, H., Qiao, C., Guo, X., Fang, L., Sha, Y., Gong, Z. (2021). Identifying and Evaluating Anomalous Structural Change-based Nodes in Generalized Dynamic Social Networks. Other. 15 (4) 1--22. ACM New York, NY.
  • Zhou, J., Li, Y., Guo, X. (2021). Predicting psoriasis using routine laboratory tests with random forest. PLOS One. 16 (10) e0258768. Public Library of Science San Francisco, CA USA.
  • Wu, G., Guo, X., Xu, B. (2020). BAM: A block-based Bayesian method for detecting genome-wide associations with multiple diseases. Other. 25 (5) 678--689. TUP.
  • Gin, S., Guo, X., Jm, D., Angeli, F., Damodaran, K., Testud, V., Du, J., Kerisit, K. (2020). Insights into the mechanisms controlling the residual corrosion rate of borosilicate glasses. Other. 1--9. Springer US. http://dx.doi.org/10.1038/s41529-020-00145-2
  • Guo, X. (2020). JS-MA: A Jensen-Shannon Divergence Based Method for Mapping Genome-wide Associations on Multiple Diseases. Other. 11 1251. Frontiers.
  • Zhang, J., Guo, X., Gonzales, S., Yang, J., Wang, X. (2020). TS: a powerful truncated test to detect novel disease associated genes using publicly available gWAS summary data. BMC Bioinformatics. 21 1--15. Springer.
  • Liu, B., Feng, S., Guo, X., Zhang, J. (2019). Bayesian analysis of complex mutations in HBV, HCV, and HIV studies. Other. 2 (3) 145--158. TUP.
  • Li, Z., Yao, Q., Guo, X., Crits-Christoph, A., Mayes, M.A., Hervey, W.J., Lebeis, S.L., Banfield, J.F., Hurst, G.B., Hettich, R.L., Pan, C. (2019). Genome-Resolved Proteomic Stable Isotope Probing of Soil Microbial Communities Using (CO2)-C-13 and C-13-Methanol. Frontiers in Microbiology. 10
  • Li, Z., Yao, Q., Guo, X., Crits-Christoph, A., Mayes, M.A., Lebeis, S.L., Banfield, J.F., Hurst, G.B., Hettich, R.L., Pan, C., others. (2019). Genome-resolved proteomic stable isotope probing of soil microbial communities using 13CO2 and 13C-methanol. Frontiers in Microbiology. 10 2706. Frontiers.
  • Zhang, J., Zhao, Z., Guo, X., Guo, B., Wu, B. (2019). Powerful statistical method to detect disease-associated genes using publicly available genome-wide association studies summary data. Genetic Epidemiology. 43 (8) 941-951.
  • Ding, X., Guo, X. (2018). A Survey of SNP Data Analysis. Other. 1 (3) 173--190. TUP.
  • Yao, Q., Li, Z., Song, Y., Wright, S.J., Guo, X., Tringe, S.G., Tfaily, M.M., Pa\vsa-Toli\'c, Ljiljana, Hazen, T.C., Turner, B.L., others. (2018). Community proteogenomics reveals the systemic impact of phosphorus availability on microbial functions in tropical soil. Other. 1. Nature Publishing Group.
  • Akwafuo, S., Guo, X., Mikler, A.R. (2018). Epidemiological modelling of vaccination and reduced funeral rites interventions on the peproduction number, R0 of Ebola virus disease in West Africa. Other. 3 9--11.
  • Guo, X., Li, Z., Yao, Q., Mueller, R.S., Eng, J.K., Tabb, D.L., Hervey, IV ,William Judson, Pan, C. (2018). Sipros Ensemble improves database searching and filtering for complex metaproteomics. Other. 34 (5) 795-802. http://dx.doi.org/10.1093/bioinformatics/btx601
  • Yu, N., Guo, X., Zelikovsky, A., Pan, Y. (2017). GaussianCpG: a Gaussian model for detection of CpG island in human genome sequences. BMC Genomics. 18 (4) 392. BioMed Central.
  • Guo, X., Zhang, J., Cai, Z., Du, D., Pan, Y. (2017). Searching genome-wide multi-locus associations for multiple diseases based on Bayesian Inference. Other. 14 (3) 600–610. IEEE.
  • Guo, X., Liu, B., Chen, L., Chen, G., Pan, Y., Zhang, J. (2016). Bayesian inference for functional dynamics exploring in fMRI data. Other. 2016 Hindawi Publishing Corporation.
  • Nguyen, K., Guo, X., Pan, Y. (2016). Phylogeny in Multiple Sequence Alignments. Other. 103–112. John Wiley & Sons, Inc..
  • Nguyen, K., Guo, X., Pan, Y. (2016). Protein/DNA/RNA Pairwise Sequence Alignment. Other. 11–23. John Wiley & Sons, Inc..
  • Nguyen, K., Guo, X., Pan, Y. (2016). Quantifying Sequence Alignments. Other. 25–58. John Wiley & Sons, Inc..
  • Nguyen, K., Guo, X., Pan, Y. (2016). Sequence Analysis Services. Other. 133–143. John Wiley & Sons, Inc..
  • Yu, N., Guo, X., Gu, F., Pan, Y. (2016). Signalign: An ontology of DNA as signal for comparative gene structure prediction using information-coding-and-processing techniques. Other. 15 (2) 119–130. IEEE.
  • Fu, Y., Chen, G., Guo, X., Zhang, J., Pan, Y. (2015). Analyzing the effects of pretreatment diversity on HCV drug treatment responsiveness using Bayesian partition methods. Other. 1 (1) 1. NIH Public Access.
  • Guo, X., Yu, N., Ding, X., Wang, J., Pan, Y. (2015). Dime: A novel framework for de novo metagenomic sequence assembly. Other. 22 (2) 159–177. Mary Ann Liebert, Inc. 140 Huguenot Street, 3rd Floor New Rochelle, NY 10801 USA.
  • Guo, X. (2015). Searching Genome-wide Disease Association Through SNP Data.
  • Ding, X., Wang, J., Zelikovsky, A., Guo, X., Xie, M., Pan, Y. (2015). Searching high-order SNP combinations for complex diseases based on energy distribution difference. Other. 12 (3) 695–704. IEEE Computer Society Press.
  • Guo, X., Meng, Y., Yu, N., Pan, Y. (2014). Cloud computing for detecting high-order genome-wide epistatic interaction via dynamic clustering. BMC Bioinformatics. 15 (1) 102. BioMed Central.
  • Guo, X., Yu, N., Gu, F., Ding, X., Wang, J., Pan, Y. (2014). Genome-wide interaction-based association of human diseases-a survey. Other. 19 (6) 596–616. TUP.
  • Zeng, T., Guo, X., Liu, J. (2014). Negative correlation based gene markers identification in integrative gene expression data. International Journal of Data Mining and Bioinformatics. 10 (1) 1–17. Inderscience Publishers Ltd.
  • Guo, X. (2012). Cloud Computing and Parallel Strategy for Bioinformatics: A Review.
  • Other

  • Nguyen, K., Guo, X., Pan, Y. (2016). Multiple Biological Sequence Alignment: Scoring Functions, Algorithms and Evaluation. John Wiley & Sons.
  • Technical Report

  • Ebrahimi, S., Guo, X. (2022). Deep Active Learning for De Novo Peptide Sequencing from Data-Independent-Acquisition Mass Spectrometry. EasyChair.

Contracts, Grants and Sponsored Research

    Grant - Research

  • Guo, X. (Co-Principal), Pan, C. (Principal), Mueller, R. (Co-Principal), "Proteomic Stable Isotope Probing as a Novel Approach for Linking Prebiotics with Active Gut Microbiota," sponsored by NIH, Federal, $1781476 Funded. (2021 - 2025).
  • Guo, X. (Principal), Mikler, A.R. (Co-Principal), "A Computational Framework for Protein Identification and Quantification in Metaproteomics Using Data-Independent Acquisition," sponsored by NIH, Federal, $361302 Funded. (2020 - 2024).
  • Guo, X., "Google Cloud Platform research credit program," sponsored by Google, Private, $10000 Funded. (2019 - 2019).
  • Guo, X. (Principal), "A Computational Framework for Protein Identification and Quantification in Metaproteomics Using Data-Independent Acquisition," sponsored by National Institutes of Health, FED, Funded. (2020 - 2023).
  • Grant - Teaching

  • Guo, X., "CDER Parallel and Distributed Computing Early Adopter Summer 2020 Training Program," sponsored by NSF-supported Center for Parallel and Distributed Computing Curriculum Development and Educational Resources, Other, $4500 Funded. (2020 - 2020).
  • Guo, X., "Workshop: Integrating Parallel and Distributed Computing in Introductory Programming Classes," sponsored by Tennessee Tech University, State, $3000 Funded. (2020 - 2020).
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Overall
Summative Rating
Challenge and
Engagement Index
Response Rate

out of 5

out of 7
%
of
students responded
  • Overall Summative Rating (median):
    This rating represents the combined responses of students to the four global summative items and is presented to provide an overall index of the class’s quality. Overall summative statements include the following (response options include a Likert scale ranging from 5 = Excellent, 3 = Good, and 1= Very poor):
    • The course as a whole was
    • The course content was
    • The instructor’s contribution to the course was
    • The instructor’s effectiveness in teaching the subject matter was
  • Challenge and Engagement Index:
    This rating combines student responses to several SPOT items relating to how academically challenging students found the course to be and how engaged they were. Challenge and Engagement Index items include the following (response options include a Likert scale ranging from 7 = Much higher, 4 = Average, and 1 = Much lower):
    • Do you expect your grade in this course to be
    • The intellectual challenge presented was
    • The amount of effort you put into this course was
    • The amount of effort to succeed in this course was
    • Your involvement in course (doing assignments, attending classes, etc.) was
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