Publications

[preprints] [journal papers] [conference] [books & book chapters]

Preprints

  1. [NEW] Cong Fu, Yuchao Lin, Zachary Krueger, Wendi Yu, Xiaoning Qian, Byung-Jun Yoon, Raymundo Arróyave, Xiaofeng Qian, Toshiyuki Maeda, Maho Nakata, Shuiwang Ji, “A Benchmark for Quantum Chemistry Relaxations via Machine Learning Interatomic Potentials,” arXiv:2506.23008 [q-bio.QM]
  2. [NEW] Nazmus Saadat As-Saquib, ANM Nafiz Abeer, Hung-Ta Chien, Byung-Jun Yoon, Suhas Kumar, Su-in Yi, “Forward Target Propagation: A Forward-Only Approach to Global Error Credit Assignment via Local Losses,” arXiv:2506.11030 [cs.LG]
  3. Alif Bin Abdul Qayyum, Susan D. Mertins, Amanda K. Paulson, Nathan M. Urban, Byung-Jun Yoon, “Pathway-Guided Optimization of Deep Generative Molecular Design Models for Cancer Therapy,” arXiv:2411.03460 [cs.LG]
  4. Alif Bin Abdul Qayyum, Xihaier Luo, Nathan M. Urban, Xiaoning Qian, Byung-Jun Yoon, “Multi-modal Representation Learning for Cross-modal Prediction of Continuous Weather Patterns from Discrete Low-Dimensional Data,” arXiv:2401.16936 [cs.LG]

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Journal Papers

  1. [NEW] Puhua Niu, Byung-Jun Yoon, Xiaoning Qian, “Epidemiological Model Calibration via Graybox Bayesian Optimization,” Infectious Disease Modelling, accepted.
  2. [NEW] Yi Huang, Yeonju Go, Jin Huang, Shuhang Li, Xihaier Luo, Thomas Marshall, Joseph Osborn, Christopher Pinkenburg, Yihui Ren, Evgeny Shulga, Shinjae Yoo, Byung-Jun Yoon, “Variable Rate Neural Compression for Sparse Detector Data,” Patterns, accepted.
    Preprint: arXiv:2411.11942 [physics.ins-det]
  3. [NEW] A N M Nafiz Abeer, Sanket Jantre, Nathan M Urban, and Byung-Jun Yoon, “Enhancing Generative Molecular Design via Uncertainty-guided Fine-tuning of Variational Autoencoders,” Molecular Systems Design & Engineering, 2025, https://doi.org/10.1039/D5ME00081E.
  4. [NEW] A N M Nafiz Abeer, Bong-Seong Koo, and Byung-Jun Yoon, “In Silico Design of Immunogenic Antigen Cocktail via Affinity Maturation Guided Optimization”, Bioinformatics Advances, vbaf182, https://doi.org/10.1093/bioadv/vbaf182.
  5. [NEW] Gilchan Park, Byung-Jun Yoon, Xihaier Luo, Vanessa López-Marrero, Patrick Johnstone, Shinjae Yoo, Francis J. Alexander, “Comparative Performance Evaluation of Large Language Models for Extracting Molecular Interactions and Pathway Knowledge,” Journal of Computational Biology, 2025, doi: 10.1089/cmb.2025.0078.
  6. [NEW] Fardeen H. Mozumder, Byung-Jun Yoon, “An Effective Pipeline for Training Variational Autoencoders for Synthesizable and Optimized Molecular Design,” IEEE Access, vol. 13, pp. 529-540, 2025, doi: 10.1109/ACCESS.2024.3523531.
  7. [NEW] A N M Nafiz Abeer, Nathan Urban, M Ryan Weil, Francis J. Alexander, Byung-Jun Yoon, “Multi-Objective Latent Space Optimization of Generative Molecular Design Models”, Patterns (2024), https://doi.org/10.1016/j.patter.2024.101042.
    Preprint: arXiv:2203.00526 [cs.LG].
  8. [NEW] Marinka Zitnik et al., “Current and future directions in network biology,” Bioinformatics Advances, Volume 4, Issue 1, 2024, vbae099, https://doi.org/10.1093/bioadv/vbae099.
    Preprint: arXiv:2309.08478 [q-bio.MN]
  9. Natalie M. Isenberg, Susan D. Mertins, Byung-Jun Yoon, Kristofer Reyes, Nathan M. Urban, “Identifying Bayesian Optimal Experiments for Uncertain Biochemical Pathway Models,” Scientific Reports, 14, 15237 (2024). https://doi.org/10.1038/s41598-024-65196-w
    Preprint: arXiv:2309.06540 [q-bio.MN]
  10. Xihaier Luo, Seyednami Niyakan, Patrick Johnstone, Sean Mccorkle, Gilchan Park, Vanessa López-Marrero, Shinjae Yoo, Edward R Dougherty, Xiaoning Qian, Francis J Alexander, Shantenu Jha, Byung-Jun Yoon, “Pathway-based analyses of gene expression profiles at low doses of ionizing radiation,” Frontiers in Bioinformatics, 4:1280971 (2024). doi: 10.3389/fbinf.2024.1280971.
  11. Semyoung Oh, Byung-Jun Yoon, Hangue Park, “Location-based Electrotactile Feedback Localizes Hitting Point in Virtual-Reality Table Tennis Game,” Biomedical Engineering Letters (2024) https://doi.org/10.1007/s13534-024-00354-7.
  12. Shuhan He, Paul Chong, Byung-Jun Yoon, Pei-Hung Chung, David Chen, Sammer Marzouk, Kameron Black, Wilson Sharp, Joshua Goldstein, Ali Raja, Jarone Lee, “Entropy Removal of Medical Diagnostics“, Scientific Reports, 14, 1181 (2024).
    https://doi.org/10.1038/s41598-024-51268-4.
  13. Francis J. Alexander, Meifeng Lin, Xiaoning Qian, and Byung-Jun Yoon, “Accelerating scientific discoveries through data-driven innovations,” Patterns, volume 4, issue 11, 100876, Nov. 10, 2023. https://doi.org/10.1016/j.patter.2023.100876
  14. Hyun-Myung Woo, Xiaoning Qian, Li Tan, Shantenu Jha, Francis J. Alexander, Edward R. Dougherty, Byung-Jun Yoon, “Optimal Decision Making in High-Throughput Virtual Screening Pipelines,” Patterns, volume 4, issue 11, 100875, Nov. 10, 2023. https://doi.org/10.1016/j.patter.2023.100875
    Preprint: arXiv:2109.11683 [math.OC].
  15. Xiaoning Qian, Byung-Jun Yoon, Raymundo Arróyave, Xiaofeng Qian,
    Edward R. Dougherty, “Knowledge-Driven Learning, Optimization, and Experimental Design under Uncertainty for Materials Discovery“, Patterns, volume 4, issue 11, 100863, Nov. 10, 2023. https://doi.org/10.1016/j.patter.2023.100863
  16. Line Pouchard, Kristofer G. Reyes, Francis J. Alexander Byung-Jun Yoon, “A Rigorous Uncertainty-Aware Quantification Framework Is Essential for Reproducible and Replicable Machine Learning Workflows,” Digital Discovery, 2023, 2, 1251-1258. DOI: 10.1039/D3DD00094J.
    Preprint: arXiv:2301.05763 [cs.LG].
  17. Martin G. Frasch, Byung-Jun Yoon, Dario-Lucas Helbing, Gal Snir, Marta C. Antonelli, Reinhard Bauer, “Autism spectrum disorder: a neuro-immunometabolic hypothesis of the developmental origins,” Biology 2023, 12(7), 914; https://doi.org/10.3390/biology12070914
    Preprint: arXiv:1909.05198 [q-bio.GN].
  18. Shuhan He, David Chen, Kameron Black, Paul Chong, Sammer Marzouk, Byung-Jun Yoon, Kendrick Davis, Jarone Lee, “Network Analysis of Academic Medical Center Websites in the United States,” Scientific Data, 10, 245 (2023). https://doi.org/10.1038/s41597-023-02104-3.
  19. Qihua Chen, Xuejin Chen, Hyun-Myung Woo, Byung-Jun Yoon, “Neural Message Passing for Objective-Based Uncertainty Quantification and Optimal Experimental Design,” Engineering Applications of Artificial Intelligence, Volume 123, Part A, 106171, 2023, https://doi.org/10.1016/j.engappai.2023.106171
    Preprint: arXiv:2203.07120 [cs.LG].
  20. Omar Maddouri, Xiaoning Qian, Byung-Jun Yoon, “Geometric Affinity Propagation for Clustering with Network Knowledge,” IEEE Transactions on Knowledge and Data Engineering, vol. 35, no. 11, pp. 11419-11436, 1 Nov. 2023, doi: 10.1109/TKDE.2023.3237630.
    Preprint: arxiv.org/abs/2103.14376 [cs.LG].
    [geometric AP code] [geometric AP example]
  21. Puhua Niu, Maria J. Soto, Shuai Huang, Byung-Jun Yoon, Edward R. Dougherty, Francis J. Alexander, Ian Blaby, Xiaoning Qian, “Sensitivity analysis of genome-scale metabolic flux prediction,” Journal of Computational Biology, vol. 30, no. 7, pp. 751-765, 2023. https://doi.org/10.1089/cmb.2022.0368
  22. Hyun-Myung Woo, Omar Allam, Junhe Chen, Seung Soon Jang, Byung- Jun Yoon, “Optimal high-throughput virtual screening pipeline for efficient selection of redox- active organic materials,” iScience, Volume 26, Issue 1, 105735, 2023, doi: https://doi.org/10.1016/j.isci.2022.105735
  23. Omar Maddouri, Xiaoning Qian, Francis J. Alexander, Edward R. Dougherty, Byung-Jun Yoon, “Synthetic Data for Design and Evaluation of Binary Classifiers in the Context of Bayesian Transfer Learning,” Data in Brief, 2022, 108113, ISSN 2352-3409, https://doi.org/10.1016/j.dib.2022.108113.
  24. Paul Chong, Byung-Jun Yoon, Debbie Lai, Michael Carlson, Jarone Lee, Shuhan He, “Looking Back on Forward-Looking COVID Models,” Patterns, Vol. 3, Issue 7, 2022, https://doi.org/10.1016/j.patter.2022.100492.
  25. Puhua Niu, Maria J. Soto, Byung-Jun Yoon, Edward R. Dougherty, Francis J. Alexander, Ian Blaby, Xiaoning Qian, “Protocol for condition-dependent metabolite yield prediction using the TRIMER pipeline,” STAR Protocols, Volume 3, Issue 1, 2022, 101184, ISSN 2666-1667, https://doi.org/10.1016/j.xpro.2022.101184
  26. Omar Maddouri, Xiaoning Qian, Francis J. Alexander, Edward R. Dougherty, Byung-Jun Yoon, “Robust Importance Sampling for Error Estimation in the Context of Optimal Bayesian Transfer Learning,” Patterns, Vol. 3, Issue 3, 2022, DOI: https://doi.org/10.1016/j.patter.2021.100428
    Preprint: arXiv:2109.02150 [stat.ML]
  27. Hyun-Myung Woo, Youngjoon Hong, Bongsuk Kwon, Byung-Jun Yoon, “Accelerating Optimal Experimental Design for Robust Synchronization of Uncertain Kuramoto Oscillator Model Using Machine Learning,” IEEE Transactions on Signal Processing, vol. 69, pp. 6473-6487, 2021, doi: 10.1109/TSP.2021.3130967.
    Preprint: arXiv:2106.00332 [math.OC]
  28. Omar Maddouri, Xiaoning Qian, Byung-Jun Yoon, “Deep graph representations embed network information for robust disease marker identification,” Bioinformatics, Volume 38, Issue 4, 15 February 2022, Pages 1075–1086, https://doi.org/10.1093/bioinformatics/btab772
    Download data & code: [github repo]
  29. Puhua Niu, Maria J. Soto, Byung-Jun Yoon, Edward R. Dougherty, Francis J. Alexander, Ian Blaby, Xiaoning Qian, “TRIMER: Transcription Regulation Integrated with MEtabolic Regulation,” iScience, Volume 24, Issue 11, 2021, 103218, https://doi.org/10.1016/j.isci.2021.103218
  30. Francis J. Alexander, et al., Byung-Jun Yoon, “Co-design Center for Exascale Machine Learning Technologies (ExaLearn),” The International Journal of High Performance Computing Applications, Sep. 2021. doi:10.1177/10943420211029302
  31. Byung-Jun Yoon, Xiaoning Qian, Edward R. Dougherty, “Quantifying the multi-objective cost of uncertainty“, IEEE Access, vol.9, pp. 80351-80359, 2021, doi: 10.1109/ACCESS.2021.3085486.
    Preprint: arXiv:2010.04653 [math.OC], Graphical Abstract: [view]
  32. Youngjoon Hong, Bongsuk Kwon, Byung-Jun Yoon, “Optimal experimental design for uncertain systems based on coupled differential equations,” IEEE Access, vol. 9, pp. 53804-53810, 2021, doi: 10.1109/ACCESS.2021.3071038.
    Preprint: arXiv:2007.06117 [math.OC], Graphical Abstract: [view]
    Code for Kuramoto model optimal experimental design: https://github.com/yhong2/Sync
  33. Hyun-Myung Woo and Byung-Jun Yoon, “MONACO: accurate biological network alignment through optimal neighborhood matching between focal nodes“, Bioinformatics, Volume 37, Issue 10, 15 May 2021, Pages 1401–1410, https://doi.org/10.1093/bioinformatics/btaa962
  34. Guang Zhao, Xiaoning Qian, Byung-Jun Yoon, Francis J. Alexander, and Edward R. Dougherty, “Model-based robust filtering and experimental design for stochastic differential equation systems“, IEEE Transactions on Signal Processing, vol 68, pp. 3849-3859, 2020, doi: 10.1109/TSP.2020.3001384.
  35. Hyun-Myung Woo, Hyundoo Jeong, and Byung-Jun Yoon, “NAPAbench 2: A network synthesis algorithm for generating realistic protein-protein interaction (PPI) network families,” PLoS ONE, 15(1): e0227598, 2020.
    Access NAPAbench 2 on GitHub: https://github.com/bjyoontamu/NAPAbench
  36. Yijie Wang, Hyundoo Jeong, Byung-Jun Yoon, and Xiaoning Qian, “ClusterM: A scalable algorithm for computational prediction of conserved protein complexes across multiple protein interaction networks,” BMC Genomics, 21, 615 (2020). https://doi.org/10.1186/s12864-020-07010-1
  37. Chun-Chi Chen, Hyundoo Jeong, Xiaoning Qian, and Byung-Jun Yoon, “TOPAS: network-based structural alignment of RNA sequences,” Bioinformatics, Volume 35, Issue 17, Pages 2941–2948, 2019. 
  38. Chun-Chi Chen, Xiaoning Qian, and Byung-Jun Yoon, “RNAdetect: Efficient computational detection of novel noncoding RNAs,” Bioinformatics, Volume 35, Issue 7,  Pages 1133–1141, 2019.
  39. Man Kim, Huan Zhang, Huijuan Yan, Byung-Jun Yoon, and Won-Bo Shim, “Characterizing co-expression networks underpinning maize stalk rot virulence in Fusarium verticillioides through computational subnetwork module analyses,” Scientific Reports, vol. 8, Article number: 8310, 2018.
  40. Mansuck Kim, Huan Zhang, Charles Woloshuk, Won-Bo Shim, and Byung-Jun Yoon, “Computational prediction of pathogenic network modules in Fusarium verticillioides,” IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 15, no. 2, pp. 506 – 515, 2018.
  41. Noushin Ghaffari, Osama A. Arshad, Hyundoo Jeong, John Thiltges, Michael F. Criscitiello, Byung-Jun Yoon, Aniruddha Datta, Charles D. Johnson, “Examining de novo transcriptome assemblies via a quality assessment pipeline,” IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 15, no. 2, pp. 494-505, 2018.
  42. Hyundoo Jeong, Xiaoning Qian, and Byung-Jun Yoon, “CUFID-query: accurate network querying through estimation of network flow between query and target networks based on random walk,” BMC Bioinformatics, 18(Suppl 14):500, 2017.
  43. Chun-Chi Chen, Xiaoning Qian, and Byung-Jun Yoon, “Effective computational detection of piRNAs using n-gram models and support vector machine,” BMC Bioinformatics, 18(Suppl 14):517. 2017.
  44. Chun-Chi Chen, Noushin Ghaffari, Xiaoning Qian, and Byung-Jun Yoon, “Optimal hybrid sequencing and assembly: feasibility conditions for accurate genome reconstruction and cost minimization strategy,” Computational Biology and Chemistry, 69, pp. 153–163, 2017.
  45. Hyundoo Jeong and Byung-Jun Yoon, “SEQUOIA: Significance enhanced network querying through context-sensitive random walk and minimization of network conductance,” BMC Systems Biology, 11(Suppl 3):20, 2017.
  46. Hyundoo Jeong, Xiaoning Qian, and Byung-Jun Yoon, “Effective comparative analysis of protein-protein interaction networks by measuring the steady-state network flow using a Markov model,” BMC Bioinformatics, 17(Suppl 13):395, 2016.
    * Best Paper Runner-Up, 13th MCBIOS Conference, Memphis, Tennessee, USA, March 2016.
  47. Navadon Khunlertgit and Byung-Jun Yoon, “Incorporating topological information for predicting robust cancer subnetwork markers in human protein-protein interaction network,” BMC Bioinformatics, 17(Suppl 13):351, 2016.
  48. Mansuck Kim, Huan Zhang, Charles Woloshuk, Won-Bo Shim, and Byung-Jun Yoon, “Computational identification of genetic subnetwork modules associated with maize defense response to Fusarium verticillioides,“BMC Bioinformatics, 16(Suppl 13):S12, 2015.
  49. Roozbeh Dehghannasiri, Byung-Jun Yoon, and Edward R. Dougherty, “Efficient experimental design for uncertainty reduction in gene regulatory networks,” BMC Bioinformatics, 16(Suppl 13):S2, 2015.
    * Best Paper Award, 12th MCBIOS Conference, Little Rock, Arkansas, USA, March 2015.
  50. Ariana Broumand, Mohammad Shahrokh Esfahani, Byung-Jun Yoon, Edward R. Dougherty, “Discrete optimal Bayesian classification with error-conditioned sequential sampling,” Pattern Recognition, vol. 48, no. 11, pp 3766–3782, 2015.
  51. Roozbeh Dehghannasiri, Byung-Jun Yoon, and Edward R. Dougherty, “Optimal experimental design for gene regulatory networks in the presence of uncertainty,” IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol.12, no.4, pp.938-950, 2015.
  52. Hyundoo Jeong and Byung-Jun Yoon, “Accurate multiple network alignment through context-sensitive random walk,” BMC Systems Biology, 9(Suppl 1):S7, 2015.
  53. Hyundoo Jeong and Byung-Jun Yoon, “Effective estimation of node-to-node correspondence between different graphs,” IEEE Signal Processing Letters, vol. 22, no. 6, pp. 661-665, 2015.
  54. Navadon Khunlertgit and Byung-Jun Yoon, “Simultaneous identification of robust synergistic subnetwork markers for effective cancer prognosis,” EURASIP Journal on Bioinformatics and Systems Biology, 2014:19, 2014.
  55. Byung-Jun Yoon, “Sequence alignment by passing messages,” BMC Genomics, 15(Suppl 1):S14, 2014.
  56. Sayed Mohammad Ebrahim Sahraeian and Byung-Jun Yoon, “SMETANA: accurate and scalable algorithm for probabilistic alignment of large-scale biological networks,” PLoS ONE, 8(7): e67995, 2013.
  57. Byung-Jun Yoon, Xiaoning Qian, and Edward R. Dougherty, “Quantifying the objective cost of uncertainty in complex dynamical systems,” IEEE Transactions on Signal Processing, vol. 61, no. 9, pp. 2256-2266, May 2013.
  58. Mohammad Shahrokh Esfahani, Jason Knight, Amin Zollanvari, Byung-Jun Yoon, and Edward R. Dougherty, “Classifier design given an uncertainty class of feature distributions via regularized maximum likelihood and the incorporation of biological pathway knowledge in steady-state phenotype classification,” Pattern Recognition, vol. 46, no. 10, pp. 2783–2797, 2013.
  59. Navadon Khunlertgit and Byung-Jun Yoon, “Identification of robust pathway markers for cancer through rank-based pathway activity inference,” Advances in Bioinformatics, vol. 2013, Article ID 618461, 8 pages, 2013.
  60. Mansuck Kim and Byung-Jun Yoon, “Adaptive reference update (ARU) algorithm: a stochastic search algorithm for efficient optimization of multi-drug cocktails,” BMC Genomics, 13(Suppl 6):S12, 2012.
  61. Sayed Mohammad Ebrahim Sahraeian and Byung-Jun Yoon, “A network synthesis model for generating protein interaction network families,”
    PLoS ONE, 7(8): e41474, 2012.
    Access NAPAbench 1 on GitHub: https://github.com/bjyoontamu/NAPAbench
  62. Sayed Mohammad Ebrahim Sahraeian and Byung-Jun Yoon, “RESQUE: Network reduction using semi-Markov random walk scores for efficient querying of biological networks,” Bioinformatics, 28 (16): 2129-2136, 2012.
  63. Byung-Jun Yoon, Xiaoning Qian, and Sayed Mohammad Ebrahim Sahraeian, “Comparative analysis of biological networks: hidden Markov model and Markov chain-based approach,” IEEE Signal Processing Magazine, 29(1):22-34, 2012.
  64. Mohammad Shahrokh Esfahani, Byung-Jun Yoon, and Edward R. Dougherty, “Probabilistic reconstruction of the tumor progression process in gene regulatory networks in the presence of uncertainty,” BMC Bioinformatics, 12(Suppl 8):S9, 2011.
  65. Xiaoning Qian, Sayed Mohammad Ebrahim Sahraeian, and Byung-Jun Yoon, “Enhancing the accuracy of HMM-based conserved pathway prediction using global correspondence scores,” BMC Bioinformatics, 12(Suppl 8):S6, 2011.
  66. Sayed Mohammad Ebrahim Sahraeian and Byung-Jun Yoon, “PicXAA-Web: a web-based platform for non-progressive maximum expected accuracy alignment of multiple biological sequences,” Nucleic Acids Research, 39 (Suppl 2): W8-W12, 2011.
  67. Xiaoning Qian and Byung-Jun Yoon, “Comparative analysis of protein interaction networks reveals that conserved pathways are susceptible to HIV-1 interception,” BMC Bioinformatics, 12(Suppl 1):S19, 2011.
  68. Sayed Mohammad Ebrahim Sahraeian and Byung-Jun Yoon, “PicXAA-R: efficient structural alignment of multiple RNA sequences using a greedy approach,” BMC Bioinformatics, 12(Suppl 1):S38, 2011.
    * Best Paper Award, 9th Asia Pacific Bioinformatics Conference (APBC), Incheon, Korea, January 2011.
  69. Byung-Jun Yoon, “Enhanced stochastic optimization algorithm for finding effective multi-target therapeutics,” BMC Bioinformatics, 12(Suppl 1):S18, 2011.
  70. Sayed Mohammad Ebrahim Sahraeian and Byung-Jun Yoon, “A novel low-complexity HMM similarity measure,” IEEE Signal Processing Letters, vol.18, no.2, pp. 87-90, Feb. 2011.
  71. Junjie Su, Byung-Jun Yoon, and Edward R. Dougherty, “Identification of diagnostic subnetwork markers for cancer in human protein-protein interaction network,” BMC Bioinformatics, 11(Suppl 6):S8, 2010.
  72. Sayed Mohammad Ebrahim Sahraeian and Byung-Jun Yoon, “PicXAA: greedy probabilistic construction of maximum expected accuracy alignment of multiple sequences,” Nucleic Acids Research, 38(15): 4917-4928, 2010.
  73. Junjie Su, Byung-Jun Yoon, and Edward R. Dougherty, “Accurate and reliable cancer classification based on probabilistic inference of pathway activity,” PLoS ONE, 4(12): e8161, 2009.
  74. Xiaoning Qian and Byung-Jun Yoon, “Effective identification of conserved pathways in biological networks using hidden Markov models,” PLoS ONE, 4(12): e8070, 2009.
  75. Ying Wang, Vinayak Brahmakshatriya, Huifeng Zhu, Blanca Lupiani, Sanjay M. Reddy, Byung-Jun Yoon, Preethi H. Gunaratne, Jong Hwan Kim, Rui Chen, Junjun Wang, and Huaijun Zhou, “Identification of differentially expressed miRNAs in chicken lung and trachea after avian influenza virus infection by a deep sequencing approach,” BMC Genomics, 10:512, 2009.
  76. Byung-Jun Yoon, “Hidden Markov models and their applications in biological sequence analysis,” Current Genomics, vol. 10, no. 6, pp. 402-415, Sep. 2009.
  77. Byung-Jun Yoon, “Efficient alignment of RNAs with pseudoknots using sequence alignment constraints,” EURASIP Journal on Bioinformatics and Systems Biology, vol. 2009, Article ID 491074, 13 pages, 2009.
  78. Xiaoning Qian, Sing-Hoi Sze, and Byung-Jun Yoon, “Querying pathways in protein interaction networks based on hidden Markov models,” Journal of Computational Biology, vol. 16, no. 2, pp. 145-157, Feb. 2009.
  79. Byung-Jun Yoon and P. P. Vaidyanathan, “Fast structural alignment of RNAs by optimizing the adjoining order of profile-csHMMs,” IEEE Journal of Selected Topics in Signal Processing, vol. 2, pp. 400-411, June 2008.
  80. Byung-Jun Yoon and P. P. Vaidyanathan, “Structural alignment of RNAs using profile-csHMMs and its application to RNA homology search: Overview and new results,” IEEE Transactions on Automatic Control (Joint Special Issue on Systems Biology with IEEE Transactions on Circuits and Systems: Part-I), vol. 53, pp. 10-25, Jan. 2008.
  81. Byung-Jun Yoon and P. P. Vaidyanathan, “Computational identification and analysis of noncoding RNAs – Unearthing the buried treasures in the genome,” IEEE Signal Processing Magazine, vol. 24, no. 1, pp. 64-74, Jan. 2007.
  82. Byung-Jun Yoon and P. P. Vaidyanathan, “Context-sensitive hidden Markov models for modeling long-range dependencies in symbol sequences,” IEEE Transactions on Signal Processing, vol. 54, pp. 4169-4184, Nov. 2006.
  83. Byung-Jun Yoon and Henrique S. Malvar, “A practical approach for the design of nonuniform lapped transforms,” IEEE Signal Processing Letters, vol. 13, pp. 469-452, Aug. 2006.
  84. Byung-Jun Yoon and P. P. Vaidyanathan, “A multirate DSP model for estimation of discrete probability density functions,” IEEE Transactions on Signal Processing, vol. 53, pp. 252-264, Jan. 2005.
  85. P. P. Vaidyanathan and Byung-Jun Yoon, “The role of signal-processing concepts in genomics and proteomics,” Journal of the Franklin Institute (invited paper), vol. 341, pp. 111-135, 2004.

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Selected Conference Publications

  1. [NEW] Amir Hossein Rahmati, Sanket Jantre, Weifeng Zhang, Yucheng Wang, Byung-Jun Yoon, Nathan Urban, Xiaoning Qian, “C-LoRA: Contextual Low-Rank Adaptation for Uncertainty Estimation in Large Language Models,” The Thirty-Ninth Annual Conference on Neural Information Processing Systems (NeurIPS 2025), San Diego, CA, USA, Dec. 2 – Dec. 7, 2025.
  2. [NEW] Zhiyuan Wang, Jinwoo Go, Byung-Jun Yoon, Nathan Urban, Xiaoning Qian, “A Plug-and-Play Query Synthesis Active Learning Framework for Neural PDE Solvers,” The Thirty-Ninth Annual Conference on Neural Information Processing Systems (NeurIPS 2025), San Diego, CA, USA, Dec. 2 – Dec. 7, 2025.
  3. [NEW] A N M Nafiz Abeer, Junhe Chen, Alif Bin Abdul Qayyum, Zhihao Feng, Hyun-Myung Woo, Seung Soon Jang, and Byung-Jun Yoon, “Enabling machine learning-assisted discovery of polyamines for solid-state CO₂ capture”, NeurIPS 2025 Workshop on Tackling Climate Change with Machine Learning, 2025.
  4. [NEW] Junhe Chen, A N M Nafiz Abeer, Alif Bin Abdul Qayyum, Zhihao Feng, Hyun-Myung Woo, Byung-Jun Yoon, and Seung Soon Jang, “Accelerated discovery of high-performance polyamines for solid-state direct CO₂ capture via efficient simulations and Bayesian optimization”, NeurIPS 2025 Workshop on AI for Accelerated Materials Design (AI4Mat), 2025
  5. [NEW] Alif Bin Abdul Qayyum and Byung-Jun Yoon, “PolUQBench: A Benchmark Study on Uncertainty Quantification of Polymer Property Prediction,” NeurIPS 2025 Workshop on AI for Accelerated Materials Design (AI4Mat), 2025.
  6. [NEW] Alif Bin Abdul Qayyum, Amir Hossein Rahmati, Xiaoning Qian, Byung-Jun Yoon, “ImmUQBench: A Benchmark on Uncertainty Quantification of Protein Immunogenicity Prediction.” NeurIPS 2025 Workshop on Structured Probabilistic Inference & Generative Modeling, 2025.
  7. [NEW] Alif Bin Abdul Qayyum, Amir Hossein Rahmati, Xiaoning Qian, Byung-Jun Yoon, “Uncertainty Weighted Deep Ensemble to Enhance Protein Property Prediction,” NeurIPS 2025 Workshop on Frontiers in Probabilistic Inference: Learning Meets Sampling, 2025.
  8. [NEW] Alif Bin Abdul Qayyum and Byung-Jun Yoon, “FlowGINO: Continuous Reconstruction from Sparse Observations along with Aleatoric and Epistemic Uncertainty Estimation,” NeurIPS 2025 ML×OR Workshop: Mathematical Foundations and Operational Integration of Machine Learning for Uncertainty-Aware Decision-Making, 2025.
  9. [NEW] Alif Bin Abdul Qayyum, Xihaier Luo, Nathan Urban, Xiaoning Qian, Byung-Jun Yoon, “One Stone Three Birds: Three-Dimensional Implicit Neural Network for Compression and Continuous Representation of Multi-Altitude Climate Data,” NeurIPS 2025 Workshop on Tackling Climate Change with Machine Learning, 2025.
  10. [NEW] Nicholas Jeon, Xiaoning Qian, Lamin SaidyKhan, Paul de Figueiredo, Byung-Jun Yoon, “LoRA-BERT: a Natural Language Processing Model for Robust and Accurate Prediction of long non-coding RNAs,” International Conference on Intelligent Biology and Medicine (ICIBM 2025), Columbus, OH, USA, Aug. 3-5, 2025.
    Preprint: arXiv:2411.08073 [q-bio.GN]
  11. [NEW] Doyoung Kwak, Raiyan Chowdhury, Byung-Jun Yoon, Xiaoning Qian, “Efficient and Valid Large Molecule Generation via Self-supervised Generative Models,” International Conference on Intelligent Biology and Medicine (ICIBM 2025), Columbus, OH, USA, Aug. 3-5, 2025.
  12. [NEW] Siyuan Xu, Yucheng Wang, Xihaier Luo, Byung-Jun Yoon, Xiaoning Qian, “Scale-Invariant Implicit Neural Representations For Object Counting,” CVPR Workshop EarthVision, Nashville, TN, USA, June 11th, 2025.
  13. [NEW] Sanket Jantre, Tianle Wang, Gilchan Park, Kriti Chopra, Nicholas Jeon, Xiaoning Qian, Nathan M. Urban, Byung-Jun Yoon, “Uncertainty-Aware Adaptation of Large Language Models for Protein-Protein Interaction Analysis,” 47th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Copenhagen, Denmark, July 14-17, 2025.
    Preprint: arXiv:2502.06173 [cs.LG]
  14. [NEW] Guang Zhao, Byung-Jun Yoon, Gilchan Park, Shantenu Jha, Shinjae Yoo, Xiaoning Qian, “Pareto Prompt Optimization,” 13th International Conference on Learning Representations (ICLR), Singapore, Apr 24-28, 2025.
  15. [NEW] Pei-Hung Chung, Junxiang Zheng, Hangue Park and Byung-Jun Yoon, “An ML-based Colonic Peristalsis Excitement Detector for Electrocolonogram Signal,” Workshop on Deep Learning in Multimodal Bioinformatics Analysis (DL-MBio), Shenzhen, China, Nov. 22, 2024. (in conjunction with ACM-BCB 2024)
  16. [NEW] Gilchan Park, Paul Baity, Byung-Jun Yoon, Adolfy Hoisie, “Enhancing Future Link Prediction in Quantum Computing Semantic Networks through LLM-Initiated Node Features,” The 31st International Conference on Computational Linguistics (COLING 2025), Abu Dhabi, UAE, Jan. 19-24, 2025.
    Preprint: arXiv:2410.04251 [cs.LG]
  17. Kibaek Kim, Krishnan Raghavan, Olivera Kotevska, Matthieu Dorier, Ravi Madduri, Minseok Ryu, Todd Munson, Thomas Flynn, Ai Kagawa, Byung-Jun Yoon, Christian Engelmann, Farzad Yousefian, “Privacy-Preserving Federated Learning for Science: Challenges and Research Directions,” IEEE International Conference on Big Data, Washington DC, USA, Dec. 15-18, 2024.
  18. Xihaier Luo, Samuel Lurvey, Yi Huang, Yihui Ren, Jin Huang, Byung-Jun Yoon, “Efficient Compression of Sparse Accelerator Data Using Implicit Neural Representations and Importance Sampling”, NeurIPS 2024 Workshop on Machine Learning and Compression, Vancouver, Canada, Dec. 15, 2024.
    Preprint: arXiv:2412.01754 [cs.AI]
  19. Amir Hossein Rahmati, Nathan Urban, Byung-Jun Yoon, Xiaoning Qian, “Cost-effective Reduced-Order Modeling via Bayesian Active Learning”, NeurIPS 2024 Workshop on Bayesian Decision-making and Uncertainty (BDU), Vancouver, Canada, Dec. 14, 2024.
  20. Pei-Hung Chung, Shuhan He, Norawit Kijpaisalratana, Abdel-badih el Ariss, Byung-Jun Yoon, “Neural machine translation of clinical procedure codes for medical diagnosis and uncertainty quantification,” NeurIPS 2024 Workshop on Advancements In Medical Foundation Models: Explainability, Robustness, Security, and Beyond (AIM-FM), Vancouver, Canada, Dec. 14, 2024.
    [preprint] arXiv:2402.10940 [cs.CL]
  21. Puhua Niu, Byung-Jun Yoon, Xiaoning Qian, “Epidemiological Model Calibration with Bayesian Decision-Making,” IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI), Houston, TX, Nov. 10-13, 2024.
  22. Amir Hossein Rahmati, Mingzhou Fan, Ruida Zhou, Nathan M. Urban, Byung-Jun Yoon, and Xiaoning Qian, “When Uncertainty-based Active Learning May Fail?”, The 27th International Conference on Pattern Recognition (ICPR), Kolkata, India, Dec. 1-5, 2024.
  23. Alif Bin Abdul Qayyum, Xihaier Luo, Nathan M. Urban, Xiaoning Qian, Byung-Jun Yoon, “Implicit Neural Representations for Simultaneous Reduction and Continuous Reconstruction of Multi-altitude Climate Data“, The 34th IEEE International Workshop on Machine Learning for Signal Processing (MLSP), London, UK, September 22-25, 2024.
  24. A N M Nafiz Abeer, Sanket Jantre, Nathan Urban, Byung-Jun Yoon, “Leveraging Active Subspaces to Capture Epistemic Model Uncertainty in Deep Generative Models for Molecular Design“, The 34th IEEE International Workshop on Machine Learning for Signal Processing (MLSP), London, UK, September 22-25, 2024.
    Preprint: arXiv:2405.00202 [cs.LG]
  25. Xihaier Luo, Xiaoning Qian, Byung-Jun Yoon, “Hierarchical Neural Operator Transformer with Learnable Frequency-aware Loss Prior for Arbitrary-scale Super-resolution,” The 41st International Conference on Machine Learning (ICML 2024), Vienna, Austria, July 21-27, 2024.
    [HiNOTE Project Website] https://xihaier.github.io/projects/ICML-2024-HiNOTE/
  26. Mingzhou Fan, Byung-Jun Yoon, Edward Dougherty, Francis Alexander, Nathan Urban, Raymundo Arroyave, Xiaoning Qian, “Multi-fidelity Bayesian Optimization with Multiple Information Sources of Input-dependent Fidelity,” The 40th Conference on Uncertainty in Artificial Intelligence (UAI 2024), Barcelona, Spain, July 15-19, 2024.
  27. Seyednami Niyakan, Xihaier Luo, Byung-Jun Yoon, Xiaoning Qian, “Biologically Interpretable VAE with Supervision for Transcriptomics Data Under Ordinal Perturbations,” ICLR 2024 Workshop on Machine Learning for Genomics Explorations (MLGenX), Vienna, Austria, May 11, 2024.
  28. Siyuan Xu, Yucheng Wang, Mingzhou Fan, Byung-Jun Yoon, Xiaoning Qian, “Uncertainty-aware Continuous Implicit Neural Representations for Remote Sensing Object Counting“, 27th International Conference on Artificial Intelligence and Statistics (AISTATS), Valencia, Spain, May 2-4, 2024.
  29. Sanket Jantre, Nathan M. Urban, Xiaoning Qian, and Byung-Jun Yoon. “Learning Active Subspaces for Effective and Scalable Uncertainty Quantification in Deep Neural Networks,” IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2024), Seoul, Korea, April 14-19, 2024.
    Preprint: arXiv:2309.03061.
  30. Gilchan Park, Byung-Jun Yoon, Xihaier Luo, Vanessa López-Marrero, Patrick Johnstone, Shinjae Yoo, Francis J. Alexander, “Comparative Performance Evaluation of Large Language Models for Extracting Molecular Interactions and Pathway Knowledge,” The 8th International Workshop on Computational Network Biology: Modeling, Analysis, and Control (CNB-MAC 2023), Houston, TX, 2023.
    Preprint: arXiv:2307.08813 [cs.CL]
  31. Xihaier Luo, Xiaoning Qian, Nathan Urban, Byung-Jun Yoon, “Reinstating Continuous Climate Patterns From Small and Discretized Data,” Synergy of Scientific and Machine Learning Modeling Workshop (SynS & ML) at ICML 2023, Honolulu, HI, 2023.
  32. Gilchan Park, Byung-Jun Yoon, Xihaier Luo, Vanessa López-Marrero, Patrick Johnstone, Shinjae Yoo, Francis J. Alexander, “Automated Extraction of Molecular Interactions and Pathway Knowledge using Large Language Model, Galactica: Opportunities and Challenges,” The 22nd BioNLP Workshop, July 13, 2023, Toronto, Canada (in conjunction with ACL’23).
  33. Xihaier Luo, Sean McCorkle, Gilchan Park, Vanessa Lopez-Marrero, Shinjae Yoo, Edward Dougherty, Xiaoning Qian, Francis Alexander, Byung-Jun Yoon, “Comprehensive analysis of gene expression profiles to radiation exposure reveals molecular signatures of low-dose radiation response,” IEEE Workshop on High-Performance Computing, Big Data Analytics and Integration for Multi-Omics Biomedical Data (HPC-BOD), Dec. 6, 2022, Las Vegas, NV USA, (In conjunction with IEEE BIBM 2022)
    Preprint: arXiv:2301.01769 [q-bio.GN]
    Download code & data: [github repo]
  34. Mingzhou Fan, Byung-Jun Yoon, Francis J. Alexander, Edward R. Dougherty, Xiaoning Qian, “Adaptive Group Testing with Mismatched Models,” IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Singapore, May 22-27, 2022.
    Preprint: arXiv:2110.02265 [stat.ME].
  35. Guang Zhao, Edward Dougherty, Byung-Jun Yoon, Francis Alexander, Xiaoning Qian, “Efficient Active Learning for Gaussian Process Classification by Error Reduction,” Thirty-fifth Conference on Neural Information Processing Systems (NeurIPS), Dec. 6 – Dec. 14, 2021.
  36. Woo Seok Kim, Hyun-Myung Woo, M. Ibrahim Khot, Sungcheol Hong, David G. Jayne, Byung-Jun Yoon, Sung Il Park “AI-Enabled High-Throughput Wireless Telemetry for Effective Photodynamic Therapy,” Asilomar Conference on Signals, Systems, and Computers, Pacific Grove, CA, Oct.31 – Nov. 3, 2021.
  37. Guang Zhao, Edward Dougherty, Byung-Jun Yoon, Francis Alexander, Xiaoning Qian, “Bayesian Active Learning by Soft Mean Objective Cost of Uncertainty,” 24th International Conference on Artificial Intelligence and Statistics (AISTATS), April 13 – 15, 2021.
    [download supplementary document]
  38. Guang Zhao, Edward Dougherty, Byung-Jun Yoon, Francis Alexander, Xiaoning Qian, “Uncertainty-aware Active Learning for Optimal Bayesian Classifier,” 9th International Conference on Learning Representations (ICLR), May 4-8, 2021.
  39. Ziyu Xiang, Mingzhou Fan, Guillermo Vázquez Tovar, William Trehem, Byung-Jun Yoon, Xiaofeng Qian, Raymundo Arroyave, Xiaoning Qian, “Physics-constrained Automatic Feature Engineering for Predictive Modeling in Materials Science“, 35th AAAI Conference on artificial intelligence (AAAI-21), Feb. 2-9, 2021.
  40. Hyun-Myung Woo and Byung-Jun Yoon, “Network-Based RNA Structural Alignment Through Optimal Local Neighborhood Matching,” Asilomar Conference on Signals, Systems, and Computers, Pacific Grove, CA, Nov. 1-4, 2020.
  41. Hyun-Myung Woo, Woo Seok Kim, Sungcheol Hong, Vivekanand Jeevakumar, Clay M. Smithhart, Theodore J. Price, Byung-Jun Yoon, and Sung Il Park, “Machine Learning Enabled Adaptive Wireless Power Transmission System for Neuroscience Study,” Asilomar Conference on Signals, Systems, and Computers, Pacific Grove, CA, Nov. 1-4, 2020.
  42. Hyundoo Jeong and Byung-Jun Yoon, “Accurate multiple network alignment through context-sensitive random walk,” Asia Pacific Bioinformatics Conference (APBC), HsinChu, Taiwan, January 2015.
  43. Byung-Jun Yoon, “Sequence alignment by passing messages,” Asia Pacific Bioinformatics Conference (APBC), Shanghai, China, January 2014.
  44. Sayed Mohammad Ebrahim Sahraeian and Byung-Jun Yoon, “RESQUE: Network reduction using semi-Markov random walk scores for efficient querying of biological networks,” Annual International Conference on Research in Computational Molecular Biology (RECOMB), Barcelona, April 2012.
  45. Sayed Mohammad Ebrahim Sahraeian and Byung-Jun Yoon, “PicXAA-R: efficient structural alignment of multiple RNA sequences using a greedy approach,” Asia Pacific Bioinformatics Conference (APBC), Incheon, Korea, January 2011.
  46. Byung-Jun Yoon, “Enhanced stochastic optimization algorithm for finding effective multi-target therapeutics,” Asia Pacific Bioinformatics Conference (APBC), Incheon, Korea, January 2011.
  47. Xiaoning Qian and Byung-Jun Yoon, “Comparative analysis of protein interaction networks reveals that conserved pathways are susceptible to HIV-1 interception,” Asia Pacific Bioinformatics Conference (APBC), Incheon, Korea, January 2011.
  48. Xiaoning Qian and Byung-Jun Yoon, “Shape matching based on graph alignment using hidden Markov models,” IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Dallas, TX, March 2010.
  49. Junjie Su and Byung-Jun Yoon, “Identifying reliable subnetwork markers in protein-protein interaction network for classification of breast cancer metastasis,” IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Dallas, TX, March 2010.
  50. Junjie Su, Byung-Jun Yoon, and Edward R. Dougherty, “Accurate and reliable cancer classification based on probabilistic inference of pathway activity,” RECOMB Systems Biology, Boston, MA, Dec. 2009.
  51. Xiaoning Qian and Byung-Jun Yoon, “Effective identification of conserved pathways in biological networks using hidden Markov models,” RECOMB Systems Biology, Boston, MA, Dec. 2009.
  52. Xiaoning Qian, Sing-Hoi Sze, and Byung-Jun Yoon, “Querying pathways in protein interaction networks based on hidden Markov models,” RECOMB Systems Biology, Boston, MA, Oct. 2008.
  53. Byung-Jun Yoon, Ivan Tashev, and Alex Acero, “Robust adaptive beamforming algorithm using instantaneous direction of arrival with enhanced noise suppression capability,” IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Honolulu, HI, April 2007.
  54. Byung-Jun Yoon and P. P. Vaidyanathan, “Profile context-sensitive HMMs for probabilistic modeling of sequences with complex correlations,” IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Toulouse, May 2006.
  55. Byung-Jun Yoon and P. P. Vaidyanathan, “HMM with auxiliary memory: a new tool for modeling RNA secondary structures,” Asilomar Conference on Signals, Systems, and Computers, Pacific Grove, CA, Nov. 2004.
  56. Byung-Jun Yoon and P. P. Vaidyanathan, “Wavelet-based denoising by customized thresholding,” IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Montreal, May 2004.
  57. P. P. Vaidyanathan and Byung-Jun Yoon, “Digital filters for gene prediction applications,” Asilomar Conference on Signals, Systems, and Computers, Pacific Grove, CA, Nov. 2002.
  58. P. P. Vaidyanathan and Byung-Jun Yoon, “Gene and exon prediction using allpass-based filters,” IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS), Raleigh, NC, Oct. 2002.

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Books & Book Chapters

  1. [NEW] Francis J Alexander, Kristofer-Roy Reyes, Lav R Varshney, and Byung-Jun Yoon, “AI for Optimal Experimental Design and Decision Making,” in Alok Choudhary, Geoffrey Fox, and Tony Hey (Eds.), Artificial Intelligence for Science – A Deep Learning Revolution, World Scientific, 2023.
    [see at World Scientific] [see at Amazon.com]
  2. [NEW] Chun-Chi Chen, Hyundoo Jeong, Xiaoning Qian, and Byung-Jun Yoon, “Network-Based Structural Alignment of RNA Sequences Using TOPAS,” in Risa Karakida Kawaguchi, Junichi Iwakiri (Eds.), RNA Structure Prediction, Humana, 2023.
    [see at Springer]
  3. Byung-Jun Yoon and Xiaoning Qian (Eds.), Recent Advances in Biological Network Analysis, Springer, 2021.
    [see at Amazon.com]
  4. Xiaoning Qian, Byung-Jun Yoon, and Edward R. Dougherty, “Inference of gene regulatory networks: validation and uncertainty,” in Shuguang (Robert) Cui, Alfred O. Hero III, Zhi-Quan (Tom) Luo, and Jose M. F. Moura (Eds.), Big Data over Networks, Cambridge University Press, Chapter 12, 2015.
    [see at Amazon.com]
  5. Sayed Mohammad Ebrahim Sahraeian, and Byung-Jun Yoon, “PicXAA: A Probabilistic Scheme for Finding the Maximum Expected Accuracy Alignment of Multiple Biological Sequences,” in David J. Russell (Ed.), Multiple Sequence Alignment Methods, Humana Press, Chapter 13, pp. 203-210, 2014.
    [see at Amazon.com]

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