Selected Publications

More publications in Google Scholar.

Journal Articles

[29] Feng, S., Zhang, B., Wang, H., Xiong, Y., Tian, A., Yuan, X., Pan, C. and Guo, X., 2025. Enhancing peptide identification in metaproteomics through curriculum learning in deep learning. Nature Communications, 16(1), p.8934. https://www.nature.com/articles/s41467-025-63977-z

[28] Xiong, Y., Mueller, R.S., Feng, S., Guo, X. and Pan, C., 2024. Proteomic stable isotope probing with an upgraded Sipros algorithm for improved identification and quantification of isotopically labeled proteins. Microbiome, 12(1), p.148. https://link.springer.com/article/10.1186/s40168-024-01866-1

[27] Li, J., Xiong, Y., Feng, S., Pan, C. and Guo, X., 2024. CloudProteoAnalyzer: scalable processing of big data from proteomics using cloud computing. Bioinformatics Advances, 4(1), p.vbae024. https://academic.oup.com/bioinformaticsadvances/article/4/1/vbae024/7613684

[26] Wang, H., Mi, J., Guo, X. and Hu, P., 2023. Meta-learning adaptation network for few-shot link prediction in heterogeneous social networks. Information Processing & Management, 60(5), p.103418. https://doi.org/10.1016/j.ipm.2023.103418

[25] Feng, S., Ji, H.L., Wang, H., Zhang, B., Sterzenbach, R., Pan, C. and Guo, X., 2022. MetaLP: An integrative linear programming method for protein inference in metaproteomics. PLOS Computational Biology, 18(10), p.e1010603. https://doi.org/10.1371/journal.pcbi.1010603

[24] Jain, K.G., Zhao, R., Liu, Y., Guo, X., Yi, G. and Ji, H.L., 2022. Wnt5a/β-catenin axis is involved in the downregulation of AT2 lineage by PAI-1. American Journal of Physiology-Lung Cellular and Molecular Physiology, 323(5), pp.L515-L524. https://doi.org/10.1152/ajplung.00202.2022

[23] Zhou, J., Li, Y. and Guo, X., 2021. Predicting psoriasis using routine laboratory tests with random forest. PLOS ONE, 16(10), p.e0258768. https://doi.org/10.1371/journal.pone.0258768

[22] Feng, S., Sterzenbach, R. and Guo, X., 2021. Deep learning for peptide identification from metaproteomics datasets. Journal of Proteomics, 247, p.104316. https://doi.org/10.1016/j.jprot.2021.104316

[21] Wang, H., Qiao, C., Guo, X., Fang, L., Sha, Y. and Gong, Z., 2021. Identifying and evaluating anomalous structural change-based nodes in generalized dynamic social networks. ACM Transactions on the Web, 15(4), pp.1-22. https://doi.org/10.1145/3457906

[20] Zhang, J., Guo, X., Gonzales, S., Yang, J. and Wang, X., 2020. TS: a powerful truncated test to detect novel disease associated genes using publicly available GWAS summary data. BMC bioinformatics, 21, pp.1-15. https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-020-3511-0

[19] Guo, X., 2020. JS-MA: A Jensen-Shannon Divergence Based Method for Mapping Genome-Wide Associations on Multiple Diseases. Frontiers in genetics, 11, p.1251. https://doi.org/10.3389/fgene.2020.507038

[18] Wu, G., Guo, X. and Xu, B., 2020. BAM: A block-based Bayesian method for detecting genome-wide associations with multiple diseases. Tsinghua Science and Technology, 25(5), pp.678-689. https://doi.org/10.26599/TST.2019.9010064

[17] Li, Z., Yao, Q., Guo, X., Crits-Christoph, A., Mayes, M.A., Lebeis, S.L., Banfield, J.F., Hurst, G.B., Hettich, R.L. and Pan, C., 2019. Genome-resolved proteomic stable isotope probing of soil microbial communities using 13CO2 and 13C-methanol. Frontiers in microbiology, 10, p.2706. https://doi.org/10.3389/fmicb.2019.02706

[16] 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. https://doi.org/10.1002/gepi.22251

[15] Liu, B., Feng, S., Guo, X. and Zhang, J., 2019. Bayesian analysis of complex mutations in HBV, HCV, and HIV studies. Big Data Mining and Analytics, 2(3), pp.145-158. https://doi.org/10.26599/BDMA.2019.9020005

[14] Ding, X. and Guo, X., 2018. A Survey of SNP Data Analysis. Big Data Mining and Analytics, 1(3), pp.173-190. https://doi.org/10.26599/BDMA.2018.9020015

[13] Yao, Q., Li, Z., Song, Y., Wright, S.J., Guo, X., Tringe, S.G., Tfaily, M.M., Paša-Tolić, L., Hazen, T.C., Turner, B.L. and Mayes, M.A., 2018. Community proteogenomics reveals the systemic impact of phosphorus availability on microbial functions in tropical soil. Nature ecology & evolution, 2(3), p.499. https://doi:10.1038/s41559-017-0463-5

[12] Guo, X., Li, Z., Yao, Q., Mueller, R.S., Eng, J.K., Tabb, D.L., Hervey IV, W.J. and Pan, C., 2018. Sipros ensemble improves database searching and filtering for complex metaproteomics. Bioinformatics, 34(5), pp.795-802. https://doi.org/10.1093/bioinformatics/btx601

[11] Akwafuo, S., Guo, X. and Mikler, A., 2018. Epidemiological modelling of vaccination and reduced funeral rites interventions on the reproduction number, R0, of Ebola virus disease in West Africa. J Infectious Disease Med Microbiol, 2(3), pp.7-11.

[10] Yu, N., Guo, X., Zelikovsky, A. and Pan, Y., 2017. GaussianCpG: a Gaussian model for detection of CpG island in human genome sequences. BMC genomics, 18(4), p.392. https://doi.org/10.1186/s12864-017-3731-5

[9] Guo, X., Zhang, J., Cai, Z., Du, D.Z. and Pan, Y., 2016. Searching genome-wide multi-locus associations for multiple diseases based on Bayesian inference. IEEE/ACM transactions on computational biology and bioinformatics, 14(3), pp.600-610. DOI: 10.1109/TCBB.2016.2527648

[8] Yu, N., Guo, X., Gu, F. and Pan, Y., 2016. Signalign: an ontology of DNA as signal for comparative gene structure prediction using information-coding-and-processing techniques. IEEE transactions on nanobioscience, 15(2), pp.119-130. DOI: 10.1109/TNB.2016.2537831

[7] Guo, X., Liu, B., Chen, L., Chen, G., Pan, Y. and Zhang, J., 2016. Bayesian inference for functional dynamics exploring in fMRI data. Computational and Mathematical Methods in Medicine, 2016. http://dx.doi.org/10.1155/2016/3279050

[6] Ding, X., Wang, J., Zelikovsky, A., Guo, X., Xie, M. and Pan, Y., 2014. Searching high-order SNP combinations for complex diseases based on energy distribution difference. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 12(3), pp.695-704. doi: 10.1109/TCBB.2014.2363459

[5] Guo, X., Yu, N., Ding, X., Wang, J. and Pan, Y., 2015. Dime: a novel framework for de novo metagenomic sequence assembly. Journal of Computational Biology, 22(2), pp.159-177. https://doi.org/10.1089/cmb.2014.0251

[4] Fu, Y., Chen, G., Guo, X., Zhang, J. and Pan, Y., 2015. Analyzing the effects of pretreatment diversity on HCV drug treatment responsiveness using Bayesian partition methods. Journal of bioinformatics and proteomics review, 1(1), p.1.

[3] Guo, X., Yu, N., Gu, F., Ding, X., Wang, J. and Pan, Y., 2014. Genome-wide interaction-based association of human diseases-a survey. Tsinghua Science and Technology, 19(6), pp.596-616. DOI: 10.1109/TST.2014.6961029

[2] Guo, X., Meng, Y., Yu, N. and Pan, Y., 2014. Cloud computing for detecting high-order genome-wide epistatic interaction via dynamic clustering. BMC bioinformatics, 15(1), p.102. https://doi.org/10.1186/1471-2105-15-102

[1] Zeng, T., Guo, X. and Liu, J., 2014. Negative correlation-based gene markers identification in integrative gene expression data. International Journal of Data Mining and Bioinformatics, 10(1), pp.1-17. https://doi.org/10.1504/IJDMB.2014.062889

Conferences

[14] Ebrahimi, S. and Guo, X., 2023, December. Transformer-based de novo peptide sequencing for data-independent acquisition mass spectrometry. In 2023 IEEE 23rd International Conference on Bioinformatics and Bioengineering (BIBE) (pp. 28-35). IEEE. https://ieeexplore.ieee.org/abstract/document/10431866/

[13] Wang, S., Feng, S., Pan, C. and Guo, X., 2022, December. Finefdr: fine-grained taxonomy-specific false discovery rates control in metaproteomics. In 2022 IEEE international conference on bioinformatics and biomedicine (BIBM) (pp. 287-292). IEEE. https://ieeexplore.ieee.org/abstract/document/9995401

[12] Wang, H., Qiao, C., Guo, X., Sha, Y. and Gong, Z., 2022, April. Exploiting Anomalous Structural Nodes in Dynamic Social Networks. In Companion Proceedings of the Web Conference 2022 (pp. 388-388). https://doi.org/10.1145/3487553.3524189

[11] Meyarian, A.*, Yuan, X., Liang, L., Guo, X., Qiao, Z.* and Runkle, B., 2021. Contour-Net: A Gradient-Based Network for Classification of Contour Levee Agricultural Fields. In International Conference on Urban Intelligence and Applications.

[10] Li, J., Guo, X. and Yuan, X., In International Conference on Urban Intelligence and Applications (pp. 18-26). Springer, Singapore. 2020.

[9] Hosseini, S., and Guo, X., Deep Convolutional Neural Network for Automated Detection of Mind Wandering using EEG Signals. In Proceedings of the 10th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics (pp. 314-319). 2019.

[8] Liu, B., Guo, X. and Zhang, J., Bayesian Bi-Cluster Change-Point Model for Exploring Functional Brain Dynamics. In Int'l Conf. Bioinformatics and Computational Biology (BIOCOMP'18), pp, 85-91. 2018.

[7] Lian Z, Li X, Pan Y, Guo X, Chen L, Chen G, Wei Z, Liu T, Zhang J. Dynamic Bayesian brain network partition and connectivity change point detection. In Computational Advances in Bio and Medical Sciences (ICCABS), 2015 IEEE 5th International Conference on 2015 Oct 15 (pp. 1-6). IEEE.

[6] Yu N, Guo X, Zelikovsky A, Pan Y. GaussianCpG: A Gaussian model for detection of human CpG island. In Computational Advances in Bio and Medical Sciences (ICCABS), 2015 IEEE 5th International Conference on 2015 Oct 15 (pp. 1-1). IEEE.

[5] Yu N, Guo X, Gu F, Pan Y. DNA AS X: An information-coding-based model to improve the sensitivity in comparative gene analysis. In International Symposium on Bioinformatics Research and Applications 2015 Jun 6 (pp. 366-377). Springer, Cham.

[4] Guo X, Zhang J, Cai Z, Du DZ, Pan Y. DAM: A Bayesian method for detecting genome-wide associations on multiple diseases. In International Symposium on Bioinformatics Research and Applications 2015 Jun 6 (pp. 96-107). Springer, Cham.

[3] Yu N, Gu F, Guo X, He Z. A fine-grained flow control model for cloud-assisted data broadcasting. In Proceedings of the 18th Symposium on Communications & Networking 2015 Apr 12 (pp. 24-31). Society for Computer Simulation International.

[2] Guo X, Ding X, Meng Y, Pan Y. Cloud computing for de novo metagenomic sequence assembly. In International Symposium on Bioinformatics Research and Applications 2013 May 20 (pp. 185-198). Springer, Berlin, Heidelberg.

[1] Zeng T, Guo X, Liu J. Discovering negative correlated gene sets from integrative gene expression data for cancer prognosis. In Bioinformatics and Biomedicine (BIBM), 2010 IEEE International Conference on 2010 Dec 18 (pp. 489-492). IEEE.

Books

[2] Nguyen, K., Guo, X., & Pan, Y. (2016). Multiple Biological Sequence Alignment: Scoring Functions, Algorithms and Evaluation. John Wiley & Sons. ISBN: 978-1-118-22904-0

[1] Guo, X., Yu, N., Li, B. and Pan, Y. (2016) Cloud Computing for Next-Generation Sequencing Data Analysis, in Computational Methods for Next Generation Sequencing Data Analysis (eds I. Mandoiu and A. Zelikovsky), John Wiley & Sons, Inc., Hoboken, NJ, USA. doi: 10.1002/9781119272182.ch1