Yuè Li - Computational Biology Research

Publications (also in Google Scholar):

trainnes/mentees

^†

2025

Preprints:

Shadi Zabad, Chirayu Anant Haryan, Simon Gravel^*, Sanchit Misra, Yue Li^*. (2025) Towards whole-genome inference of polygenic scores with fast and memory-efficient algorithms. bioRxiv 2025.01.19.633789; doi: https://doi.org/10.1101/2025.01.19.633789
Qifei Wang^†, He Zhu^†, Yiwen Hu, Yanjie Chen, Yuwei Wang, Xuegong Zhang, James Zou, Manolis Kellis, Yue Li^*, Dianbo Liu^*, Lian Jiang^*. (2024) CellMemory: Hierarchical Interpretation of Out-of-Distribution Cells Using Bottlenecked Transformer. bioRxiv 2024.12.17.628533; doi: https://doi.org/10.1101/2024.12.17.628533
Ziyang Song, Qincheng Lu, He Zhu, David L. Buckeridge and Yue Li^* (2024) TrajGPT: Irregular Time-Series Representation Learning for Health Trajectory Analysis. arXiv preprint arXiv:2410.02133
Pei Liu, Xiao Liang, Yue Li^*, Jiawei Luo*. (2024) ConvNTC: Convolutional neural tensor completion for predicting the disease-related miRNA pairs and cell-related drug pairs. bioRxiv 2024.10.21.619432; doi: https://doi.org/10.1101/2024.10.21.619432
Yimin Fan, Yu Li, Jun Ding^*, Yue Li*. (2024) GFETM: Genome Foundation-based Embedded Topic Model for scATAC-seq Modeling. Preprint at bioRxiv
Vishvak Raghavan, Yue Li*, Jun Ding* Harnessing Agent-Based Modeling in CellAgentChat to Unravel Cell-Cell Interactions from Single-Cell Data doi: https://doi.org/10.1101/2023.08.23.554489 (under revision at Genome Research)
Elliot Layne, Shadi Zabad, Yue Li^*, Mathieu Blanchette^*. Multi-ancestry polygenic risk scores using phylogenetic regularization. https://doi.org/10.1101/2024.02.14.580313

Peer-reviewed:

Conference papers: To appear

Journal papers:

Anjali Chawla, Doruk Cakmakci, Wenmin Zhang, Malosree Maitra, Reza Rahimian, Haruka Mitsuhashi, MA Davoli, Jenny Yang, Gary Gang Chen, Ryan Denniston, Deborah Mash, Naguib Mechawar, Matthew Suderman, Yue Li*, Corina Nagy*, Gustavo Turecki*. Differential Chromatin Architecture and Risk Variants in Deep Layer Excitatory Neurons and Grey Matter Microglia Contribute to Major Depressive Disorder. bioRxiv 2023.10.02.560567; doi: https://doi.org/10.1101/2023.10.02.560567 (accepted at Nature Genetics)

2024

Peer-reviewed:

Conference papers:

Wenmin Zhang*, Robert Sladek, Yue Li, Hamed S. Najafabadi, and Josee Dupuis*. Accounting for genetic effect heterogeneity in fine-mapping and improving power to detect gene-environment interactions with SharePro. bioRxiv (2023): 2023-07. (accepted at Nature Communications)
Ziyang Song, Qincheng Lu, He Zhu, David L. Buckeridge and Yue Li* (2024) TrajGPT: Irregular Time-Series Representation Learning for Health Trajectory Analysis. NeurIPS 2024 TSALM Workshop https://openreview.net/forum?id=pjRHvP6WcU
Xinyu Yuan, Zhihao Zhan, Zuobai Zhang, Manqi Zhou, Jianan Zhao, Boyu Han, Yue Li*, Jian Tang*. Cell ontology guided transcriptome foundation model. arXiv preprint arXiv:2408.12373 Accepted at NeurIPS 2024 as a spotlight paper
Ziyang Song, Qincheng Lu, He Zhu, David L. Buckeridge and Yue Li* (2024) TimelyGPT: Extrapolatable Transformer Pre-training for Long-term Time-Series Forecasting in Healthcare. In Proceedings of The 15th ACMConference on Bioinformatics, Computational Biology, and Health Informatics(ACM BCB ’24). ACM, Shenzhen, Guangdong, PR China, 16 pages. https://doi.org/10.1145/3698587.3701364 (acceptance rate: 35%)
Michael Huang and Yue Li*. (2024) scMoE: single-cell mixture of experts forlearning hierarchical, cell-type-specific, and interpretable representationsfrom heterogeneous scRNA-seq data. In Proceedings of The 15th ACM Con-ference on Bioinformatics, Computational Biology, and Health Informatics(ACM BCB ’24). ACM, New York, NY, USA, 8 pages. https://doi.org/10.1145/3698587.3701374 (acceptance rate: 35%)
Ruohan Wang, Zilong Wang, Ziyang Song, David L. Buckeridge and Yue Li*. (2024) MixEHR-Nest: Identifying Subphenotypes within Electronic HealthRecords through Hierarchical Guided-Topic Modeling. In Proceedings ofThe 15th ACM Conference on Bioinformatics, Computational Biology, andHealth Informatics (ACM BCB ’24). ACM, New York, NY, USA, 8 pages. https://doi.org/10.1145/3698587.3701368 (acceptance rate: 35%)
Pei Liu, Ying Liu, Jiawei Luo*, Yue Li*. (2024) MiRGraph: a hybrid deep learning approach to identify miRNA-target interactions. IEEE International Conference on Bioinformatics and Biomedicine 2024 (IEEE BIBM 2024) (21% acceptance; 730 total submissions) Preprint at bioRxiv 2023.11.04.565620; https://doi.org/10.1101/2023.11.04.565620
Ziyang Song, Qincheng Lu, He Zhu and Yue Li* (2024) Bidirectional Generative Pre-training for Improving Time Series Representation Learning. In the Proceedings of the 9th Machine Learning for Healthcare Conference (MLHC) and the Proceeding of Machine Learning Research (PMLR). Volume 252 (preprint available at arXiv arXiv:2402.09558)
Yimin Fan, Yu Li, Jun Ding*, Yue Li*. (2024). GFETM: Genome Foundation-Based Embedded Topic Model for scATAC-seq Modeling. In: Ma, J. (eds) Research in Computational Molecular Biology. RECOMB 2024. Lecture Notes in Computer Science, vol 14758. Springer, Cham. Springer Link

Journal papers:

Ruiyang Wang, Yingying Su, Kieran O'Donnell, Jean Caron, Michael Meaney, Xiangfei Meng*, and Yue Li* (2024) Differential interactions between gene expressions and stressors across the lifespan in major depressive disorder. Journal of Affective Disorders Volume 362. Pages 688-697. https://doi.org/10.1016/j.jad.2024.07.069
Yixuan Li, Archer Y. Yang*, Ariane Marelli* and Yue Li*. (2024) MixEHR-SurG: a joint proportional hazard and guided topic model for inferring mortality-associated topics from electronic health records. Journal Biomedical Informatics. 153 (104638). doi.org/10.1016/j.jbi.2024.104638
Harry Moroz, Yue Li*, Ariane Marelli*. (2024) hART: Deep Learning-Informed Lifespan Heart Failure Risk Trajectories. International Journal of Medical Informatics (In press) https://doi.org/10.1016/j.ijmedinf.2024.105384
Ariane J. Marelli*, Chao Li, Aihua Liu, Hanh Nguyen, Harry Moroz, James M Brophy, Liming Guo, David L Buckeridge, Jian Tang, Joelle Pineau, Yi Yang, Yue Li. (2024) Machine learning informed diagnosis for Congenital Heart Disease in Large Claims Data Source. JACC: Advance 3(2), p.100801.
Wenmin Zhang*, Tianyuan Lu, Robert Sladek, Yue Li, Hamed S. Najafabadi, and Josee Dupuis*. (2024) SharePro: an accurate and efficient genetic colocalization method accounting for multiple causal signals. Bioinformatics. doi: 10.1093/bioinformatics/btae295
Liam, Hodgson, Yue Li, Yasser Iturria-Medina, Yasser Iturria-Medina, Jo Anne Stratton, Guy Wolf, Smita Krishnaswamy, David A. Bennett and Danilo Bzdok*. Supervised latent factor modeling isolates cell-type-specific transcriptomic modules that underlie Alzheimer’s disease progression. Commun Biol 7, 591 (2024). https://doi.org/10.1038/s42003-024-06273-8

2023

Wenmin Zhang*, Hamed Najabadi, Yue Li*. (2023) SparsePro: an efficient genome-wide fine-mapping method integrating summary statistics and functional annotations. PLOS Genetics 19(12): e1011104. https://doi.org/10.1371/journal.pgen.1011104
Lakshmipuram Seshadri Swapna, Michael Huang, and Yue Li*. (2023) GTM-decon: guided-topic modeling of single-cell transcriptomes enables sub-cell-type and disease-subtype deconvolution of bulk transcriptomes. Genome Biology 24, 190. https://doi.org/10.1186/s13059-023-03034-4
Manqi Zhou, Hao Zhang, Zilong Bai, Dylan Mann-Krzisnik, Fei Wang*, Yue Li*. (2023) Single-cell multi-omics topic embedding reveals cell-type-specific and COVID-19 severity-related immune signatures, Cell Reports Methods 3 (100563) https://doi.org/10.1016/j.crmeth.2023.100563
Shadi Zabad, Simon Gravel*, Yue Li*. (2023) Fast and Scalable Polygenic Risk Modeling with Variational Inference. American Journal of Human Genetics doi:10.1016/j.ajhg.2023.03.009. (available here)
Chang Su; Yu Hou; Haotan Zhang; Zilong Bai; Anthony Cuturrufo; Winston Guo; Fayzan Chaudhry; Gregory Ghahramani; Jian Tang; Feixiong Cheng; Yue Li; Rui Zhang; Fei Wang. (2023) Biomedical Discovery through the integrative Biomedical Knowledge Hub (iBKH). iScience 26, 106460
Shin-Chieh Fuh, Laura M. Fiori, Gustavo Turecki, Corina Nagy*, Yue Li*. (2023) Multi-omic modeling of antidepressant response implicates dynamic immune and inflammatory changes in individuals who respond to treatment. PloS ONE 18(5), p.e0285123. https://doi.org/10.1371/journal.pone.0285123
Yuening Wang, Audrey Grant, and Yue Li*. (2023) Graph-embedded topic modelling of population-level electronic health records by leveraging biomedical knowledge graph. Star Protoc 4, 101966
Yue Li†, Gregory Fonseca†, Jun Ding†,*. (2023) Machine Learning Methods for Multi-Omics Data Integration. In Machine Learning Methods for Multi-Omics Data Integration, pp. 39-74. Cham: Springer International Publishing, 2023. doi:10.1007/978-3-031-36502-7_4.

2022

Yuesong Zou, Ahmad Pesaranghader, Aman Verma, David Buckeridge, and Yue Li*. (2022) Modeling electronic health record data using a knowledge-graph-embedded topic model. Scientific Reports 12, 17868. doi.org/10.1038/s41598-022-22956-w
Yuri Anjuha*, Yuesong Zou, Aman Verma, David Buckeridge*, Yue Li*. (2022) MixEHR-Guided: A guided multi-modal topic modeling approach for large-scale automatic phenotyping using the electronic health record. Journal Biomedical Informatics 104190 doi:10.1016/j.jbi.2022.104190
Ziyang Song, Yuanyi Hu Aman Verma, David Buckeridge, and Yue Li*. (2022) Automatic phenotyping by a seed-guided topic model. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD ’22), August 14–18, 2022, Washington, DC, USA. ACM, New York, NY, USA, 12 pages. https://doi.org/10.1145/3534678.3542675 (KDD HealthDay2022 Best Paper award)
Zhi Wen†, Jingfu Zhang†, Guido Powell, Imane Chafi, David Buckeridge*, Yue Li*. (2022) EpiTopics: A dynamic machine learning model to predict and inform non-pharmacological public health interventions from global news reports. Star Protocols 3, 101463
Xiangfei Meng*, Michelle Wang, Kieran O'Donnell, Jean Caron, MJ Meaney, Yue Li*. (2022) Integrative PheWAS analysis in risk categorization of major depressive disorder and identifying their associations with genetic variants using a latent topic model approach. Translational Psychiatry 12, 240. https://doi.org/10.1038/s41398-022-02015-8
Yuening Wang, Rodrigo Benavides, Luda Diatchenko, Audrey Grant*, Yue Li*. (2022) A graph-embedded topic model enables characterization of diverse pain phenotypes among UK Biobank individuals. iScience 25, 104390 doi: https://doi.org/10.1016/j.isci.2022.104390
Zhi Wen†, Guido Powell†, Imane Chafi, David Buckeridge*, Yue Li*. (2022) Inferring global-scale temporal latent topics from news reports to predict public health interventions for COVID-19. Patterns 100435. doi:10.1016/j.patter.2022.100435.

2021

Yifan Zhao†, Huiyu Cai†, Zuobai Zhang, Jian Tang*, Yue Li*. (2021) Learning interpretable cellular and gene signature embeddings from single-cell transcriptomic data. Nature Communications, 12(1), 5261. https://doi.org/10.1038/s41467-021-25534-2
Ziyang Song, Xavier Sumba Toral, Yixin Xu, Aihua Liu, Liming Guo, Guido Powell, Aman Verma, David Buckeridge*, Ariane Marelli*, and Yue Li*. (2021) Supervised Multi-Specialist Topic Model With Applications on Large-Scale Electronic Health Record Data. In 12th ACM Conference on Bioinformatics,Computational Biology, and Health Informatics (ACM-BCB) August 1–4, 2021, Virtual due to COVID-19. ACM, New York, NY, USA, 26 pages. https://doi.org/10.1145/1122445.11224561
Shadi Zabad, Aaron P. Ragsdale, Rosie Sun, Yue Li*, Simon Gravel*. (2021) Assumptions about frequency-dependent architectures of complex traits bias measures of functional enrichment. Biorxiv 2020.10.23.352427 (2021)
Gerhard-Paul Diller, Alexandra Arvanitaki, Alexander R. Opotowsky, Kathy Jenkins, Philip Moons, Alexander Kempny, Animesh Tandon, Andrew Redington, Paul Khairy, Seema Mital, Michael A. Gatzoulis, Yue Li, Ariane Marelli. (2021) Lifespan Perspective on Congenital Heart Disease Research JACC State-of-the-Art Review. J Am Coll Cardiol 77, 2219–2235.
Zhi Wen†, Pratheeksha Nair†, Chih-Ying Deng, Xing Han Lu, Edward Moseley, Naomi George, Charlotta Lindvall*, Yue Li* (2021) Mining heterogeneous clinical notes by multi-modal latent topic model. Plos ONE (†: equal contribution; *co-corresponding author)
Xing Han Lu†, Aihua Liu†, Shih-Chieh Fuh, Yi Lian, Liming Guo, Yi Yang, Ariane Marelli*, Yue Li* (2021). Recurrent disease progression networks for modelling risk trajectory of heart failure. PloS One, 16(1), e0245177–15. http://doi.org/10.1371/journal.pone.0245177

2020

Spreng, R. N., Dimas, E., Mwilambwe-Tshilobo, L., Dagher, A., Koellinger, P., Nave, G., et al.* (2020). The default network of the human brain is associated with perceived social isolation. Nature Communications, 1–11. http://doi.org/10.1038/s41467-020-20039-w (*contributing author)
Bahrami, M., Maitra, M., Nagy, C., Turecki, G., Rabiee, H. R., and Li, Y.* (2020). Deep feature extraction of single-cell transcriptomes by generative adversarial network. Bioinformatics (Oxford, England). http://doi.org/10.1093/bioinformatics/btaa976
Zhang, W., Li, S. Y., Liu, T., and Li, Y.* (2020). Partitioning gene-based variance of complex traits by gene score regression. PloS One, 15(8), e0237657–15. http://doi.org/10.1371/journal.pone.0237657
Marelli, A., Li, C., Liu, A., Nguyen, H., Brophy, J., Guo, L., Buckeridge, D., Tang, J., Pineau, J., Yang, Y., and Li, Y. (2020) Machine Learning to Automate Clinician Designed Empirical Manual for Congenital Heart Disease Identification in Large Claims Database. Machine Learning for Health Care (MLHC) 2020
Li, Y.*, Nair, P., Wen, Z., Chafi, I., Okhmatovskaia, A., Powell, G., Buckeridge, D.* (2020). Global Surveillance of COVID-19 by Mining News Media Using a Multi-Source Dynamic Embedded Topic Model. Presented at the Proceedings of the 11th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics, New York, NY, USA: Association for Computing Machinery. http://doi.org/10.1145/3388440.3412418 (*co-corresponding)
Layne, E., Dort, E., Hamelin, R., Li, Y., Blanchette, M. (2020) Supervised learning on phylogenetically distributed data. Bioinformatics Oxford Proceedings of the 19th European Conference on Computational Biology (ECCB) (in press)
ENCODE Project Consortium^* (2020). Expanded encyclopaedias of DNA elements in the human and mouse genomes. Nature, 583(7818), 699–710. http://doi.org/10.1038/s41586-020-2493-4. (*contributing author)
Li, Y.^*, Nair, P., Lu, XH., Wen, Z., Wang,Y., Dehaghi, AAK., Miao, Y., Liu, W., Ordog, T., Biernacka, J., Ryu, E., Olson, J., Frye, M. A., Liu, A., Guo, L., Marelli, A., Ahuja, Y., Davila-Velderrain, J., and Kellis, M.^* (2020). Inferring multimodal latent topics from electronic health records. Nature Communications 1–17. http://doi.org/10.1038/s41467-020-16378-3 (*co-corresponding author)

2019

Mudge, J. M., Jungreis, I., Hunt, T., Gonzalez, J. M., Wright, J. C., Kay, M., et al^*. (2019). Discovery of high-confidence human protein-coding genes and exons by whole-genome PhyloCSF helps elucidate 118 GWAS loci. Genome Research, 29(12), 2073–2087. http://doi.org/10.1101/gr.246462.118 (*contributing author)
Liu, M.^*, Jiang, Y.^*, Wedow, R.^*, Li, Y.^*, ..., Liu, D., Vrieze, S. (2019). Association studies of up to 1.2 million individuals yield new insights into the genetic etiology of tobacco and alcohol use. Nature Genetics 51, 237-244 (*equal contribution)

2018

Wang, Y., Li, Y., Yue, M., Wang, J., Kumar, S., Wechsler-Reya, RJ, Zhang, Z., Ogawa, Y., Kellis, M., Duester, G., Zhao, JC. (2018) N⁶-methyladenosine RNA modification regulates embryonic neural stem cell self-renewal through histone modifications. Nature Neuroscience 21, 195–206

2017

Li, Y.*, Davila-Velderrain, J., and Kellis, M.* (2017). A probabilistic framework to dissect functional cell-type-specific regulatory elements and risk loci underlying the genetics of complex traits. BioRxiv. https://doi.org/https://doi.org/10.1101/059345
Li, Y.*, Shi, A. H., Tewhey, R., Sabeti, P. C., Ernst, J., and Kellis, M.* (2017). Genome-wide regulatory model from MPRA data predicts functional regions, eQTLs, and GWAS hits. BioRxiv, 110171. https://doi.org/10.1101/110171 doi:10.1101/110171.
Kreimer, A., Zeng, H., Edwards, M. D., Guo, Y., Tian, K., Shin, S., Welch, R., Wainberg, M., Mohan, R., Sinnott-Armstrong, N. A., Li, Y., Eraslan, G., AMIN, T. B., Goke, J., Mueller, N. S., Kellis, M., Kundaje, A., Beer, M. A., Keles, S., Gifford, D. K. and Yosef, N. (2017), Predicting gene expression in massively parallel reporter assays: a comparative study. Human Mutation. doi:10.1002/humu.23197

2016

Li, Y. and Kellis, M. (2016). Joint Bayesian inference of risk variants and tissue-specific epigenomic enrichments across multiple complex human diseases. Nucleic Acids Research. http://doi.org/10.1093/nar/gkw627
Olsen, J. B., Wong, L., Deimling, S., Miles, A., Guo, H., Li, Y., Zhang, Z., Greenblatt, J., Emili, A., Tropepe., V. (2016). G9a and ZNF644 Physically Associate to Suppress Progenitor Gene Expression during Neurogenesis. Stem Cell Reports, 7(3), 454–470. http://doi.org/10.1016/j.stemcr.2016.06.012
Paul, J.^*, Toosi, B.^*, Vizeacoumar, F.^*, Bhanumathy, K., Li, Y., Gerger, C., Zawily, A., Freywald, T., Anderson, D., Mousseau, D., Kanthan, R., Zhang, Z., Vizeacoumar, F., & Freywald, A. (2016). Targeting synthetic lethality between the SRC kinase and the EPHB6 receptor may benefit cancer treatment. Oncotarget. http://doi.org/10.1093/nar/gkw627
Zhao, D.Y., Gish, G.^*, Braunschweig, U.^*, Li, Y., Ni, Z., Schmitges, F.W., Zhong, G., Liu, K., Li, W., Moffat, J., Vedadi, M., Min, J., Pawson, T., Blencowe, B., and Greenblatt, J. (2016). SMN and symmetric arginine dimethylation of RNA polymerase II C-terminal domain control termination. Nature 529(7584), pp.48-53.
Wong, K. C., Li, Y., & Peng, C. A Comparison Study for DNA Motif Modeling on Protein Binding Microarray. IEEE/ACM Transactions on Computational Biology and Bioinformatics. doi:10.1109/TCBB.2015.2443782.(2016)
Wong, K. C., Peng, C., & Li, Y. Probabilistic Inference on Multiple Normalized Signal Profiles from Next Generation Sequencing: Transcription Factor Binding Sites. IEEE/ACM Transactions on Computational Biology and Bioinformatics. doi:10.1109/TCBB.2015.2424421. (2016)

2015

Wong, K. C., Li, Y., & Peng, C. (2015). Identification of coupling DNA motif pairs on long-range chromatin interactions in human K562 cells. Bioinformatics, btv555 (Advanced Online).
Wong, K. C., Li Y., Peng, C., Moses, A. M., & Zhang, Z. (2015). Computational learning on specificity-determining residue-nucleotide interactions. Nucleic acids research, gkv1134 (Advanced Online).
Li Y^†, Wang Y^†, Zhang Z, Zamudio AV, Zhao JC. Genome-wide detection of high abundance N⁶-methyladenosine sites by microarray. RNA. 2015. (Advanced Online)
Li Y and Zhang Z. Computational Biology in microRNA. WIREs RNA. 2015. doi: 10.1002/wrna.1286
Liang C^†, Li Y^†, Luo J, Zhang Z. A novel motif-discovery algorithm to identify co-regulatory motifs in large transcription factor and microRNA co-regulatory networks in human. Bioinformatics. 2015. (Advance Online)

2014

Li Y and Zhang Z. Potential microRNA-mediated oncogenic intercellular communication revealed by pan-cancer analysis. Scientific Report 2014;4:7097.
Li Y, Liang M, Zhang Z. Regression Analysis of Combined Gene Expression Regulation in Acute Myeloid Leukemia. PLoS Computational Biology. 2014;10(10):e1003908.
Li Y^†, Liang C^†, Easterbrook S, Luo J, Zhang Z. Investigating functional implication of reinforcing feedback loops in transcriptional regulatory network. Molecular BioSystem. 2014;10(12):3238-3248.
Wong K-C, Peng C, Li Y, Chan T-M. Herd Clustering: A synergistic data clustering approach using collective intelligence. Applied Soft Computing. 2014;23:61-75.
Wong, KC, Li, Y., Peng, C., Zhang, Z. SignalSpider: Probabilistic Pattern Discovery on Multiple Normalized ChIP-Seq Signal Profiles. Bioinformatics 2014;31(1):17-24.
Li Y^†, Liang C^†, Wong K-C, Luo J, Zhang Z. Mirsynergy: detecting synergistic miRNA regulatory modules by overlapping neighbourhood expansion. Bioinformatics. 2014;30(18):2627–2635.
Li Y, Liang C, Wong K-C, Jin K, Zhang Z. Inferring probabilistic miRNA-mRNA interaction in cancers: a role-switch approach. Nucleic Acids Research. 2014;42(9):e76.
Li Y, Goldenberg A, Wong K-C, Zhang Z. A probabilistic approach to explore human miRNA targetome by integrating miRNA-overexpression data and sequence information. Bioinformatics. 2014;30(5):621–628.
Wang Y, Li Y, Toth JI, Petroski MD, Zhang Z, Zhao JC. N(6)-methyladenosine modification destabilizes developmental regulators in embryonic stem cells. Nature Cell Biology. 2014;16(2):1-10.
Wong K-C, Chan T-M, Peng C, Li Y, Zhang Z. DNA motif elucidation using belief propagation. Nucleic Acids Research. 2013;41(16):e153.

2013

Li Y, Zhao DY, Greenblatt JF, Zhang Z. RIPSeeker: a statistical package for identifying protein-associated transcripts from RIP-seq experiments. Nucleic Acids Research. 2013;41(8):e94–e94.

2012

Arsenault RJ, Li Y, Potter A, Griebel PJ, Kusalik A, Napper S. Induction of ligand-specific PrP (C) signaling in human neuronal cells. Prion. 2012;6(5):477-488.
Arsenault RJ, Li Y, Bell, K., Doig, K., Potter, A., Griebel, P. J., Kusalik, A., and Napper, S. Mycobacterium avium subsp. paratuberculosis Inhibits Interferon Gamma-Induced Signaling in Bovine Monocytes. Insights into the Cellular Mechanisms of Johne's Disease. Infect Immunity 2012.
Arsenault RJ, Li Y, Maattanen, P., Scruten, E., Doig, K., Potter, A., Griebel, P., Kusalik, A., and Napper, S. Altered Toll-like receptor 9 signaling in Mycobacterium avium subsp. paratuberculosis-infected bovine monocytes reveals potential therapeutic targets. Infect Immunity. 2012;81(1):226-237.
Li Y, Arsenault RJ, Trost, B., Slind, J., Griebel, P. J., Napper, S., Kusalik, A. A Systematic Approach for Analysis of Peptide Array Kinome Data. Science Signaling. 2012;5(220):pl2-pl2.

(*=equal contribution)

Book chapters:

Wong, K. C., Li, Y., & Zhang, Z. (2015). Unsupervised Learning in Genome Informatics. arXiv preprint arXiv:1508.00459.
Zhao, D, Li Y, Greenblatt, J, Zhang, Z (2014). ncRNA–Protein Interactions in Development and Disease from the Perspective of High-Throughput Studies. In A. Emili, J. Greenblatt, & S. Wodak (Eds.), Systems Analysis of Chromatin-Related Protein Complexes in Cancer (pp. 87-115). Springer New York.