International Journal Papers

• Domain adapted broiler density map estimation using negative-patch data augmentation, T. Kim, D.-H. Lee, W.-S. Kim, B.-T. Zhang, Biosystesm Engineering, 231, 865-177, 2023. [PDF]
• Video Turing Test: A first step towards human-level AI, M. Lee, Y.-J. Heo, S. Choi, W.-S. Choi, B.-T. Zhang, AI Magazie, 44, 537-554, 2023. [PDF]

• Team Tidyboy at the WRS 2020: a modular software framework for home service robots, T. Kang, D.-W Song, J. Yi, J.-Y. Kim, C.-Y. Lee, Y. Yoo, M. Kim, H.-J. Jo, B.-T. Zhang, J. Song, and S.-J. Yi, Team Tidyboy at the WRS 2020: a modular software framework for home service robots, Advanced Robotics, 36:17-18, 836-849, 2022. [PDF]

• M2FN: Multi-step modality fusion for advertisement image assessment, K.-W. Park, J.-W. Ha, J.-H. Lee, S.-Y. Kwon, K.-M. Kim, B.-T. Zhang, Applied Soft Computing, 103, 107116, May 2021. [PDF]
• Data-driven experimental design and model development using Gaussian process with active learning, J. Chang, J.-S. Kim, B.-T. Zhang, M. A. Pitt, J. I. Myung, Cognitive Psychology, 125, 101360, March 2021. [PDF]
• Leveraging node neighborhoods and egograph topology for better bot detection in social graphs, B. Bebensee, N. Nazarov, B.-T. Zhang, Social Network Analysis and Mining, 11(1), 1-14, January 2021. [PDF]

• Bayesian evolutionary hypernetworks for interpretable learning from high-dimensional data, S.-J. Kim, J.-W. Ha, H. Kim, and B.-T. Zhang, Applied Soft Computing, 81, 105477, August 1, 2019. [PDF]
• Enzymatic weight update algorithm for DNA-based molecular learning, C. Baek, S.-W. Lee, B.-J. Lee, D.-H. Kwak, and B.-T. Zhang, Molecules, 24, 1409, April 10, 2019. [PDF]

• Identifying DNA methylation modules associated with a cancer by probabilistic evolutionary learning, J.-K. Rhee, S.-J. Kim, and B.-T. Zhang, IEEE Computational Intelligence Magazine, 13(3): 12–19, August 2018. [PDF]
• Deep ECGNet: An optimal deep learning framework for monitoring mental stress using ultra short-term ECG signals, B. Hwang, J. You, T. Vaessen, I. Myin-Germeys, C. Par, and B.-T. Zhang, Telemedicine and e-Health, [PDF] (Online Ahead of Print: February 8, 2018)
• Molecular associative memory with spatial auto-logistic model for pattern recall, D. Punithan, and B.-T. Zhang, Procedia Computer Science, 123: 373–379, 2018. [PDF]
• Generative local metric learning for nearest neighbor classification, Y.-K. Noh, B.-T. Zhang, and D.-D. Lee, IEEE Transactions on Pattern Analysis and Machine Intelligence, 40(1): 106-118, 2018. [PDF]
• Fluid dynamic models for Bhattacharyya-based discriminant analysis, Y.-K. Noh, F. C. Park, B.-T. Zhang, and D.-D. Lee, IEEE Transactions on Pattern Analysis and Machine Intelligence, 40(1): 92-105, 2018. [PDF]

• In vitro molecular machine learning algorithm via symmetric internal loops of DNA, J.-H. Lee, S.H. Lee, C. Baek, H.-S. Chun, J.-H. Ryu, J.-W. Kim, R. Deaton, and B.-T. Zhang, Biosystems, 158:1-9, 2017. [PDF]
• Dual-memory neural networks for modeling cognitive activities of humans via wearable sensors, S.-W. Lee, C.-Y. Lee, D.-H. Kwak, J.-W. Ha, J. Kim, and B.-T. Zhang, Neural Networks, 92:17-28, 2017. [PDF]

• Humans and machines in the evolution of AI in Korea, B.-T. Zhang, AI Magazine, 37(2):108-112, 2016. [PDF][Link]
• Whole-body balancing walk controller for position controlled humanoid robots, S.-J. Yi, B.-T. Zhang, D. W. Hong, and D. D. Lee, International Journal of Humanoid Robotics, 13(1):1550047, 2016. [PDF]
• Survey of computational haplotype determination methods for single individual, J.-K. Rhee, H. Li, J.-G. Joung, K.-B. Hwang, B.-T. Zhang,and S.-Y. Shin, Genes & Genomics, 38:1-12, 2016. [PDF]
• Special issue: First International Conference on Big Data and Smart Computing (BigComp2014), J. Kim, J. Kwok, K. Sumiya, B.-T. Zhang, Data & Knowledge Engineering, 104:15-16, 2016. [PDF]

• Molecular learning with DNA kernel machines, Y.-K. Noh, D.D. Lee, K.-A. Yang, C. Kim, and B.-T. Zhang, BioSystems, 137:73-83, 2015. [PDF]
• Consensus analysis and modeling of visual aesthetic perception, T.-S. Park and B.-T. Zhang, IEEE Transactions on Affective Computing, 6(3):272-285, 2015. [PDF]
• Team THOR's entry in the DARPA robotics challenge trials 2013, S.-J. Yi, S.G. McGill, L. Vadakedathu, Q. He, I. Ha, J. Han, H. Song, M. Rouleau, B.-T. Zhang, D. Hong, M. Yim, and D.D. Lee, Journal of Field Robotics, 32(3):315-335, 2015. [PDF]
• Characteristic molecular vibrations of adenosine receptor ligands, H. K. Chee, J.-S. Yang, J.-G. Joung, B.-T. Zhang, S. J. Oh, FEBS Letters, 589:548-552, 2015. [PDF]
• Editorial: Special issue on advanced intelligent system - Preface, J.-W. Jung, H. Wakuya, and B.-T. Zhang, Soft Computing, 19(4):813-814, 2015. [PDF]

• Bayesian evolutionary hypergraph learning for predicting cancer clinical outcomes, S.-J Kim, J.-W. Ha, and B.-T. Zhang, Journal of Biomedical Informatics, 49:101-111, 2014. [PDF]
• The demand for quantitative techniques in biomedical image informatics, H.-Y. Jang, H.-R. Kim, M.-S. Kang, M.-H. Kim, and B.-T. Zhang, Biomedical Engineering Letters, 4(4):319-327, 2014. [PDF]

• Biomolecular computation with molecular beacons for quantitative analysis of target nucleic acids, H.-W. Lim, S.H. Lee, K.-A. Yang, S-I. Yoo, T.H. Park, and B.-T. Zhang, Biosystems, 111(1):11-17, 2013. [PDF]
• Rule-based in vitro molecular classification and visualization, S.-Y. Shin, K.-A. Yang, I.-H. Lee, S.H. Lee. T.H. Park, and B.-T. Zhang, BioChip Journal, 7(1), 2013. [PDF]
• Constructing higher-order miRNA-mRNA interaction networks in prostate cancer via hypergraph-based learning, S.-J. Kim, J.-W. Ha, and B.-T. Zhang, BMC Systems Biology, 7:47, 2013. [PDF]
• Non-linear molecular pattern classification using molecular beacons with multiple targets, I.-H. Lee, S.H. Lee. T.H. Park, and B.-T. Zhang, BioSystems, 114(3):206-213, 2013. [PDF]
• Integrated analysis of genome-wide DNA methylation and gene expression profiles in molecular subtypes of breast cancer, J.-K. Rhee, K, Kim, H, Chae, J, Evans, P, Yan, B.-T. Zhang, J, Gray, P, Spellman, T, H.-M. Huang, K, P. Nephew, and S. Kim, Nucleic Acids Research, 41(18):8464-8474, 2013. [PDF]
• Construction of microRNA functional families by a mixture model of position weight matrices, J.-K. Rhee, S.-Y. Shin and B.-T. Zhang,  PeerJ, 1:e199, 2013. [PDF]
• Bayesian evolutionary methods for learning higher-order graphical models from high-dimensional data, J.-W Ha and B.-T. Zhang, (submitted).

• Biomolecular theorem proving on a chip: A novel microfluidic solution to a classical logic problem, S.H. Lee, D. van Noort, K.-A. Yang, I.-H. Lee, B.-T. Zhang and T.H. Park, Lab on a Chip, 12(10):1841-1847, 2012. [PDF]
• A probabilistic coevolutionary biclustering algorithm for discovering coherent patterns in gene expression dataset, J.-G. Joung, S.-J. Kim, S.-Y. Shin and B.-T. Zhang, BMC Bioinformatics, 13(Suppl 17):S12, 2012. [PDF]
• Approximating Bhattacharyya-based discriminant analysis with fluid model, Y.-K. Noh, J. Hamm, F.C. Park, B.-T. Zhang, and D.D. Lee, (submitted)
• Active balancing walk controller for position controlled humanoid robots, S.-J. Yi, B.-T. Zhang, D. Hong, and D. D. Lee, (submitted).
• Hypernetworks on evolvable hardware, J,-K. Kim, K.-Y. Wang, S.-Y. Shin, D.J. Chung, and B.-T. Zhang, (submitted).
• Bayesian estimation of distribution via random hypergraph models, S.E. Lee, I.-H. Lee, and B.-T. Zhang, (submitted).
• Learning in vitro with DNA kernel machines, Y.-K. Noh, D.D. Lee, K.-A. Yang, C. Kim, and B.-T. Zhang, (submitted).

• Feature relevance network-based transfer learning for indoor location estimation, H.-S. Seok,  K.-B. Hwang, and B.-T. Zhang, IEEE Transactions on Systems, Man, and Cybernetics-Part C: Applications and Reviews, 41(5):711-719, 2011. [PDF]
• A DNA assembly model of sentence generation, J.-H. Lee, S. H. Lee, W.-H. Chung, E. S. Lee, T. H. Park, R. Deaton, and B.-T. Zhang, BioSystems, 106:51-56, 2011.[PDF]
• Ensemble learning with active example selection for imbalanced biomedical data classification, S. Oh, M.S. Lee, and B.-T. Zhang, IEEE/ACM Transactions on Computational Biology and Bioinformatics, 8(2):316-325,2011.[PDF]

• Fluid dynamics models for low rank discriminant analysis, Y.-K. Noh, B.-T. Zhang, and D.D. Lee, Journal of Machine Learning Research - Proceedings Track, 9:565-572, 2010.[PDF]
• In vitro molecular pattern classification via DNA-based weighted sum operation, H.-W. Lim, S.H. Lee, K.-A. Yang, J.Y. Lee, S.-I. Yoo, T. H. Park, and B.-T. Zhang, BioSystems, 100(1):1-7, 2010.[PDF]

• Identification of cell cycle-related regulatory motifs using a kernel canonical correlation analysis, J.-K. Rhee, J.-G. Joung, J.-H. Chang, Z. Fei and B.-T. Zhang, BMC Genomics, 10(Suppl 3):S29, 2009. [PDF]
• EvoOligo: Oligonucleotide probe design with multiobjective evolutionary algorithms, S.-Y. Shin, I.-H. Lee, Y.-M. Cho, K.-A. Yang, and B.-T. Zhang, IEEE Transactions on Systems, Man, and Cybernetics Part B: Cybernetics, 39(6):1606-1616, 2009.[PDF]
• Ensembled support vector machines for human papillomavirus risk type prediction from protein secondary structures, S. Kim, J. Kim, and B.-T. Zhang, Computers in Biology and Medicine, 39(2):187-193, 2009. [PDF]
• Effective mixing in a microfluidic chip using magnetic particles, S.-W. Lee, D. van Noort, J.-Y. Lee, B.-T. Zhang, T.-H. Park, Lab on a Chip, 9:479-482, 2009. [PDF]

• Hypernetworks: A molecular evolutionary architecture for cognitive learning and memory, B.-T. Zhang, IEEE Computational Intelligence Magazine, 3(3):49-63, 2008.[PDF] [LINK]
• The use of gold nanoparticle aggregation for DNA computing and logic-based biomolecular detection, I.-H. Lee, K.-A. Yang, J.-H. Lee, J.-Y. Park, Y. G. Chai, J.-H. Lee, and B.-T. Zhang, Nanotechnology, 19:395103, 2008.[PDF]
• Introducing meta-services for biomedical information extraction, F. Leitner, M. Krallinger, C. Rodriguez-Penagos, J. Hakenberg, C. Plake, C.-J. Kuo, C.-N. Hsu, R. T. Tasi, H.-C. Hung, W. W. lau, C. A. Johnson, R. Satre, K. Yoshida, Y. H. Chen, S. Kim, S.-Y. Shin, B.-T. Zhang, W. A. Baumgartner, Jr., L. Hunter, B. Haddow, M. Matthews, X. Wang, P. Ruch, F. Ehrler, A. Ozgur, G. Erkan, D. R. Radev, M. Krauthammer, T. Luong, R. Hoffmann, C. Sander, and A. Valencia, Genome Biology, 9(suppl 2):S6, 2008. [PDF]
• Characterization of biological effect of 1763 MHz radiofrequency exposure on auditory hair cells, T.-Q. Huang, M.S. Lee, E. Oh, B.-T. Zhang, J.-S. Seo, W.-Y. Park, International Journal of Radiation Biology, 84(11):909-915, 2008.[PDF]
• Molecular responses of Jurkat T-cells to 1763 MHz radiofrequency radiation, T.-Q. Huang, M.S. Lee, E. Oh, B.-T. Zhang, J.-S. Seo, W.-Y. Park, International Journal of Radiation Biology, 84(9):734-741, 2008.[PDF]
• PIE: An online prediction system for protein-protein interactions from text, S. Kim, S.-Y. Shin, I.-H. Lee, S.-J. Kim, R. Sriram, and B.-T. Zhang, Nucleic Acids Research, 35:W411-W415, 2008. [PDF]
• AptaCDSS-E: A classifier ensemble-based clinical decision support system for cardiovascular disease level prediction, J.-H. Eom, S.C. Kim, and B.-T. Zhang, Expert Systems with Applications, 34(4):2465-2479, 2008.[PDF]
• An evolutionary Monte Carlo algorithm for predicting DNA hybridization, J.-S. Kim, J.-W. Lee, Y.-K. Noh, J.-Y. Park, D.-Y. Lee, K.-A. Yang, Y.G. Chai, J.-C. Kim, and B.-T. Zhang, BioSystems, 91(1):69-75, 2008.[PDF]

• A global minimization algorithm based on a geodesic of a Lagrangian formulation of Newtonian dynamics, J.-S. Kim, J.-C. Kim, J.-M. O, and B.-T. Zhang, Neural Processing Letters, 26(2):121-131, 2007.[PDF]
• Discovery of microRNA-mRNA modules via population-based probabilistic learning, J.-G. Joung, K.-B. Hwang, J.-W. Nam, S.-J. Kim and B.-T. Zhang, Bioinformatics, 23(9):1141-1147, 2007.[PDF]
• Identification and characterization of small RNAs from vernalized Arabidopsis thaliana, Oh, M., Lee, H., Kim, Y.-K., Nam, J.-W., Rhee, J.-K., Zhang, B.-T., Kim, V.N., Lee, I., Journal of Plant Biology, 50(5):562-572, 2007. [PDF]

• Molecular basis for the recognition of primary microRNAs by the Drosha-DGCR8 complex, J. Han, Y. Lee, K.H. Yeom, J.-W. Nam, I. Heo, J.-K. Rhee, S. Y. Sohn, Y. Cho, B.-T. Zhang and V.N. Kim, Cell, 125:887-901, 2006. [PDF]
• ProMiR II: A web server for the probabilistic prediction of clustered, nonclustered, conserved and nonconserved microRNAs, J.-W. Nam, J.-H. Kim, S.-K. Kim, B.-T. Zhang, Nucleic Acids Research, 34:W455-W458, 2006. [PDF]
• miTarget: microRNA target-gene prediction using a Support Vector Machine, S.-K. Kim, J.-W. Nam, J.-K. Rhee, W.-J. Lee and B.-T. Zhang, BMC Bioinformatics, 7(1):411, 2006. [PDF]
• Identification of regulatory modules by co-clustering latent variable models: stem cell differentiation, J.-G. Joung, D. Shin, R. H. Seong, and B.-T. Zhang, Bioinformatics, 22(16):2005-2011, 2006.[PDF]
• Identification of biochemical networks by S-tree based genetic programming, D.-Y. Cho, K.-H. Cho, B.-T. Zhang, Bioinformatics, 22(13):1631-1640, 2006.[PDF]
• Construction of phylogenetic trees by kernel-based comparative analysis of metabolic networks, S. J. Oh, J.-G, Joung, J.-H. Chang, and B.-T. Zhang, BMC Bioinformatics, 7:284, 2006. [PDF]
• DNA hypernetworks for information storage and retrieval, B.-T. Zhang and J.-K. Kim, Lecture Notes in Computer Science, DNA 12, 4287:298-307, 2006. [PDF]
• Molecular learning of wDNF formulae, B.-T. Zhang and H.-Y. Jang, Lecture Notes in Computer Science, DNA 11, 3892:427-437, 2006. [PDF]
• Protein sequence-based risk classification for human papillomaviruses, J.-G. Joung, S. J. O, and B.-T. Zhang, Computers in Biology and Medicine, 36:656-667, 2006. [PDF]
• Simulation and real-time monitoring of polymerase chain reaction for its higher efficiency, Ji Youn Lee, Hee-Woong Lim, Suk-In Yoo, Byoung-Tak Zhang, and Tai Hyun Park, Biochemical Engineering Journal, 29:109-118, 2006. [PDF]
• Learning hierarchical Bayesian networks for large-scale data analysis, K.-B. Hwang, B.-H. Kim, and B.-T. Zhang, Lecture Notes in Computer Science, ICONIP 2006, 4232:670-679, 2006.[PDF]
• Microarray probe design using ε-multi-objective evolutionary algorithms with thermodynamic criteria, S.-Y. Shin, I.-H. Lee, and B.-T. Zhang, Lecture Notes in Computer Science, EvoBio 2006, 3907:184-195, 2006. [PDF]
• Human papillomavirus risk type classification from protein sequences using support vector machines, S. Kim and B.-T. Zhang, Lecture Notes in Computer Science, EvoBio 2006, 3907:57-66, 2006. [PDF]
• Multi-stage evolutionary algorithms for efficient identification of gene regulatory networks, K.-Y. Kim, D.-Y. Cho, and B.-T. Zhang, Lecture Notes in Computer Science, EvoBio 2006, 3907:45-56, 2006. [PDF]
• A tree kernel-based method for protein-protein interaction mining from biomedical literature, J.-H. Eom, S. Kim, S.-H. Kim, and B.-T. Zhang, Lecture Notes in Computer Science, KDLL 2006, 3886:42-52, 2006. [PDF]
• Mining protein interaction from biomedical literature with relation kernel method, J.-H. Eom and B.-T. Zhang, Lecture Notes in Computer Science, ISNN 2006, 3973:642-647, 2006. [PDF]
• Prediction of protein interaction with neural network-based feature association rule mining, J.-H. Eom and B.-T. Zhang, Lecture Notes in Computer Science, ICONIP 2006, 4234:30-39, 2006. [PDF]
• Adaptive stock trading with dynamic asset allocation using reinforcement learning, O, J., Lee, J., Lee, J. W., and Zhang, B.-T., Information Sciences, 176(15):2121-2147, 2006. [PDF]

• A Bayesian algorithm for in vitro molecular evolution of pattern classifiers, B.-T. Zhang and H.-Y. Jang, Lecture Notes in Computer Science, 3384:458-467, 2005. [PDF]
• Crosschip: a system supporting comparative analysis of different generations of Affymetrix arrays, Kong, S. W., Hwang, K.-B., Kim, R. D., Zhang, B.-T., Greenberg, S. A., Kohane, I. S., and Park, P. J., Bioinformatics, 21(9):2116-2117, 2005. [PDF]
• Multi-objective evolutionary optimization of DNA sequences for reliable DNA computing, S.-Y. Shin, I.-H. Lee, D. Kim and B.-T. Zhang, IEEE Transactions on Evolutionary Computation, 9(2):143-158, 2005. [PDF]
• Human microRNA prediction through a probabilistic co-learning model of sequence and structure, Jin-Wu Nam, Ki-Roo Shin, Jinju Han, Yoontae Lee, V. Narry Kim, and Byoung-Tak Zhang, Nucleic Acids Research, 33(11):3570-3581, 2005. [PDF]
• Bayesian model averaging of Bayesian network classifiers over multiple node-orders: application to sparse datasets, K.-B. Hwang and B.-T. Zhang, IEEE Transactions on Systems, Man, and Cybernetics Part B: Cybernetics, 35(6):1302-1310, 2005. [PDF]
• Bayesian network learning with feature abstraction for gene-drug dependency analysis, J.-H. Chang, K.-B. Hwang, S. J. O, and B.-T. Zhang, Journal of Bioinformatics and Computational Biology, 3(1):61-77, 2005. [PDF]
• Efficient initial pool generation for weighted graph problems using parallel overlap assembly, Ji Youn Lee, Hee-Woong Lim, Suk-In Yoo, Tai Hyun Park, and Byoung-Tak Zhang, Lecture Notes in Computer Science, DNA10, , 3384:215-223, 2005. [PDF]
• Prediction of yeast protein-protein interactions by neural feature association rule, Jae-Hong Eom and Byoung-Tak Zhang, Lecture Notes in Computer Science, ICANN 2005, 3697:491-496, 2005. [PDF]
• Extraction of gene/protein interaction from text documents with relation kernel, Jae-Hong Eom and Byoung-Tak Zhang, Lecture Notes in Artificial Intelligence, KES 2005, 3682:936-942, 2005. [PDF]
• Dynamic asset allocation for stock trading optimized by evolutionary computation, Jangmin O, Jongwoo Lee, Jae Won Lee, and Byoung-Tak Zhang, IEICE Transactions on Information and Systems, E88-D(6):1217-1223, 2005. [PDF]
• Identification of Caenorhabditis elegans microRNA targets using a kernel method, W.-J. Lee, J.-W. Nam, S.-K. Kim, and B.-T. Zhang, Genomics & Informatics, 3(1):15-23, 2005. [PDF]

• Solving traveling salesman problems with DNA molecules encoding numerical values, J. Y. Lee, S.-Y. Shin, T. H. Park, and B.-T. Zhang, BioSystems, 78:39-47, 2004. [PDF]
• Co-trained support vector machines for large scale unstructured document classification using unlabeled data and syntactic information, S.-B. Park and B.-T. Zhang, Information Processing & Management, 40(3):421-439, 2004. [PDF]
• Development, evaluation and benchmarking of simulation software for biomolecule-based computing, Derrel Blain and Max Garzon, Soo-Yong Shin and Byoung-Tak Zhang, Satoshi Kashiwamura, Masahito Yamamoto, Atsushi Kameda, and Azuma Ohuchi, Natural Computing, 3(4):427-442, 2004. [PDF]
• PubMiner: machine learning-based text mining system for biomedical information analysis, J.-H. Eom and B.-T. Zhang, Genomics & Informatics, 2(2):99-106, 2004. [PDF]
• Genetic mining of DNA sequence structures for effective classification of the risk types of human papillomavirus(HPV), J.-H. Eom, S.-B. Park, and B.-T. Zhang, Lecture Notes in Computer Science, ICONIP 2004, 3316:1334-1343, 2004. [PDF]
• Searching transcriptional modules using evolutionary algorithms, J.-G. Joung, S. J. Oh, and B.-T. Zhang, Lecture Notes in Computer Science, PPSN 8, 3242:532-540, 2004. [PDF]
• Evolutionary continuous optimization by distribution estimation with variational Bayesian independent component analyzers mixture model, D.-Y. Cho and B.-T. Zhang, Lecture Notes in Computer Science, PPSN 8, 3242:212-221, 2004. [PDF]
• Dynamic asset allocation exploiting predictors in reinforcement learning framework, J. O, J. W. Lee, J.-W. Lee, and B.-T. Zhang, Lecture Notes in Artificial Intelligence, ECML/PKDD 2004, 3201:298-309, 2004. [PDF]
• PubMiner: machine learning-based text mining system for biomedical information mining, J.-H. Eom and B.-T. Zhang, Lecture Notes in Artificial Intelligence, AIMSA 2004, 3192:216-225, 2004. [PDF]
• Prediction of implicit protein-protein interaction by optimal associative feature mining, J.-H. Eom, J.-H. Chang and B.-T. Zhang, Lecture Notes in Computer Science, IDEAL 2004, 3177:85-91, 2004. [PDF]
• PromSearch: a hybrid approach to human core-promoter prediction, B.H. Kim, S.B. Park, and B.-T. Zhang, Lecture Notes in Computer Science, IDEAL 2004, 3177:125-131, 2004. [PDF]
• Stock trading by modelling price trend with dynamic Bayesian networks, J. O, J. W. Lee, S.-B. Park and B.-T. Zhang, Lecture Notes in Computer Science, IDEAL 2004, 3177:794-799, 2004. [PDF]
• Prediction of the risk types of human papillomaviruses by support vector machines, J.-G. Joung, S. J. O, and B.-T. Zhang, Lecture Notes in Artificial Intelligence, PRICAI 2004, 3157:723-731, 2004. [PDF]
• Computational methods for identification of human microRNA precursors, J.-W. Nam, W.-J. Lee, and B.-T. Zhang, Lecture Notes in Artificial Intelligence, PRICAI 2004, 3157:732-741, 2004. [PDF]
• Multi-objective evolutionary probe design based on thermodynamic criteria for HPV detection, I.-H. Lee, S. Kim, and B.-T. Zhang, Lecture Notes in Artificial Intelligence, PRICAI 2004, 3157:742-750, 2004. [PDF]
• Two-step genetic programming for optimization of an RNA common-structure, J.W. Nam, J.G. Joung, Y. S. Ahn, and B.-T. Zhang, Lecture Notes in Computer Science, EvoBio 2004, 3005:73-83, 2004. [PDF]
• Korean compound noun decomposition using syllabic information only, S.-B. Park, J.-H. Chang, and B.-T. Zhang, Lecture Notes in Computer Science, CICLing 2004, 2945:143-154, 2004. [PDF]
• BioPubMiner: machine learning component-based biomedical information analysis platform, J. H. Eom and B.-T. Zhang, Lecture Notes in Computer Science, CIT 2004, 3356:11-20, 2004. [PDF]
• Adaptive neural network-based clustering of yeast protein-protein interactions, J. H. Eom and B.-T. Zhang, Lecture Notes in Computer Science, CIT 2004, 3356:49-57, 2004. [PDF]
• A lab-on-a-chip module for bead separation in DNA-based concept learning, Lim, H.-W., Jang, H.-M., Ha, S.-M., Chai, Y.-G., Yoo, S.-I., and Zhang, B.-T., Lecture Notes in Computer Science, DNA 9, 2943:1-10, 2004. [PDF]
• RCA-based detection methods for resolution refutation, Lee, I.-H., Park, J. Y., Chai, Y.-G., and Zhang, B.-T, Lecture Notes in Computer Science, DNA 9, 2943:32-36, 2004. [PDF]

• Self-organizing latent lattice models for temporal gene expression profiling, Zhang, B.-T, Yang, J. and Chi, S. W., Machine Learning, 52(1/2):67-89, 2003. [PDF]
• Word sense disambiguation by learning decision trees from unlabeled data, Park, S.-B., Zhang, B.-T., and Kim, Y. T., Applied Intelligence, 19(1):27-38, 2003. [PDF]
• Genetic mining of HTML structures for effective web-document retrieval, Kim, S. and Zhang, B.-T., Applied Intelligence, 18(3):243-256, 2003. [PDF]
• Identification of novel anti-angiogenic factors by in silico functional gene screening method, Lee, S.-K., Choi, Y. S., Cha, J., Moon, E.-J., Lee, S.-W., Bae, M.-K., Sohn, T.-K., Won, Y., Ma, S., Kong, E. B., Lee, H., Lim, S., Chang, D., Kim, Y.-J., Kim, C. W., Zhang, B.-T., Kim, K.-W, Journal of Biotechnology, 105(1-2):51-60, 2003. [PDF]
• Mining the risk types of human papillomavirus (HPV) by AdaCost, Park, S.-B., S. Hwang, and Zhang, B.-T., Lecture Notes in Computer Science, 2736:403-412, 2003. [PDF]
• Automatic webpage classification enhanced by unlabeled data, Park, S.-B. and Zhang, B.-T., Lecture Notes in Computer Science, 2690:821-825, 2003. [PDF]
• Classification of the risk types of human papillomavirus by decision trees, Park, S.-B., S. Hwang, and Zhang, B.-T., Lecture Notes in Computer Science, 2690:540-544, 2003. [PDF]
• An empirical study on dimensionality optimization in text mining for linguistic knowledge acquisition, Kim, Y.-S., Chang, J.-H., and Zhang, B.-T., Lecture Notes in Artificial Intelligence, 2637:111-116, 2003. [PDF]
• Large scale unstructured document classification using unlabeled data and syntactic information, Park, S.-B. and Zhang, B.-T., Lecture Notes in Artificial Intelligence, 2637:88-99, 2003. [PDF]
• NACST/Seq: a sequence design system with multiobjective optimization, Kim, D., Shin, S.-Y., Lee, I.-H., and Zhang, B.-T., Lecture Notes in Computer Science, 2568:242-251, 2003. [PDF]
• DNA implementation of theorem proving with resolution refutation in propositional logic, Lee, I.-H., Park, J.-Y., Jang, H.-M., Chai, Y.-G., and Zhang, B.-T., Lecture Notes in Computer Science, 2568:156-167, 2003. [PDF]
• Version space learning with DNA molecules, Lim, H.-W., Yun, J.-E., Jang, H.-M., Chai, Y.-G., Yoo, S.-I., and Zhang, B.-T., Lecture Notes in Computer Science, 2568:143-155, 2003. [PDF]
• Temperature gradient-based DNA computing for graph problems with weighted edges, Lee, J. Y., Shin, S.-Y., Augh, S. J., Park, T. H., and Zhang, B.-T., Lecture Notes in Computer Science, 2568:73-84, 2003. [PDF]
• Classification of human Papillomavirus (HPV) risk type via text mining, Park, S.-B., Hwang, S., and Zhang, B.-T., Genomics & Informatics, 1(2):80-86, 2003. [PDF]
• Gene expression pattern analysis via latent variable models coupled with topographic clustering, Chang, J.-H., Chi, S. W., and Zhang, B.-T., Genomics & Informatics, 1(1): 32-39, 2003. [PDF]

• A Bayesian evolutionary approach to the design and learning of heterogeneous neural trees, Zhang, B.-T., Integrated Computer-Aided Engineering, 9(1):73-86, 2002. [PDF]
• Target word selection using WordNet and data-driven models in machine translation, Kim, Y., Chang, J.-H., and Zhang, B.-T., Lecture Notes in Artificial Intelligence, 2417:607, 2002. [PDF]
• Topic extraction from text documents using multiple-cause networks, Chang, J.-H., Lee, J. W., Kim, Y., and Zhang, B.-T., Lecture Notes in Artificial Intelligence, 2417:434-443, 2002. [PDF]
• Construction of large-scale Bayesian networks by local to global search, Hwang, K.-B., Lee, J. W., Chung, S.-W., and Zhang, B.-T., Lecture Notes in Artificial Intelligence, 2417:375-384, 2002. [PDF]
• Behavior evolution of autonomous mobile robot (AMR) using genetic programming based on evolvable hardware, Sim, K.-B., Lee, D.-W., and Zhang, B.-T., International Journal of Fuzzy Logic and Intelligent Systems, 2(1):20-25, 2002. [PDF]

• Personalized web-document filtering using reinforcement learning, Zhang, B.-T. and Seo, Y.-W., Applied Artificial Intelligence, 15(7):665-685, 2001. [PDF]
• System identification using evolutionary Markov chain monte carlo, Zhang, B.-T. and Cho, D.-Y., Journal of Systems Architecture, 47(7):587-599, 2001. [PDF]
• Learning-based intrasentence segmentation for efficient translation of long sentences, Kim, S.-D., Zhang, B.-T., and Kim, Y. T., Machine Translation, 16(3):151-174, 2001. [PDF]
• Collocation dictionary optimization using WordNet and k-nearest neighbor learning, Kim, Y.-S., Zhang, B.-T., and Kim, Y.-T., Machine Translation, 16(2):89-108, 2001. [PDF]

• Bayesian evolutionary optimization using Helmholtz machines, Zhang, B.-T. and Shin, S.-Y., Lecture Notes in Computer Science, 1917:827-836, 2000. [PDF]
• Building optimal committees of genetic programs, Zhang, B.-T. and Joung, J.-G., Lecture Notes in Computer Science, 1917:231-240, 2000. [PDF]
• Bayesian methods for efficient genetic programming, Zhang, B.-T., Genetic Programming and Evolvable Machines, 1(3):217-242, 2000. [PDF]
• Evolving complex group behaviors using genetic programming with fitness switching, Zhang, B.-T. and Cho, D.-Y., Artificial Life and Robotics, 4(2):103-108, 2000. [PDF]
• Comparison of selection methods for evolutionary optimization, Zhang, B.-T. and Kim, J.-J., Evolutionary Optimization, 2(1):55-70, 2000. [PDF]

• Genetic programming with active data selection, Zhang, B.-T. and Cho, D.-Y., Lecture Notes in Artificial Intelligence, 1585:146-153, 1999. [PDF]

• A Probabilistic model for co-evolutionary emergence of collective intelligence, Zhang, B.-T., Cybernetics and Systems, 1999. (submitted)

• Maximum entropy models for intra-sentence segmentation, Kim, S.-D., Zhang, B.-T., and Kim, Y.T., Natural Language Engineering, 1998. (submitted)

• Efficient model induction by a Bayesian evolutionary algorithm with incremental data inheritance, Zhang, B.-T. and Joung, J.-G., IEEE Transactions on Evolutionary Computation, 1998. (submitted)

• Evolutionary induction of sparse neural trees, Zhang, B.-T., Ohm, P., and Mühlenbein, H., Evolutionary Computation, 5(2):213-236, 1997. [PDF]
• Evolutionary neural trees for modeling and predicting complex systems, Zhang, B.-T., Ohm, P., and Mühlenbein, H., Engineering Applications of Artificial Intelligence, 10(5):473-483, 1997. [PDF]

• Balancing accuracy and parsimony in genetic programming, Zhang, B.-T. and Mühlenbein, H., Evolutionary Computation, vol. 3, no. 1, pp. 17-38, 1995. [PDF]

• Artificial neural nets, adaptive genetic programming, and intelligent learning machines, Zhang, B.-T., VeKNI Nachrichten, vol. 41, pp. 17-31, 1995.

• Accelerated learning by active example selection, Zhang, B.-T., International Journal of Neural Systems, 5(1):67-75, 1994. [PDF]

• Effects of Occam's razor in evolving sigma-pi neural nets, Zhang, B.-T., Lecture Notes in Computer Science, vol. 866, pp. 462-471, 1994.

• Evolving optimal neural networks using genetic algorithms with Occam's razor, Zhang, B.-T. and Mühlenbein, H., Complex Systems, 7(3):199-220, 1993. [PDF] [LINK]

• A genetic neural learning engine for artificial intelligence, Zhang, B.-T., VeKNI Nachrichten, Stuttgart, pp. 239-249, 1993.

• Learning by Genetic Neural Evolution (in German), Zhang, B.-T., PhD Dissertation, University of Bonn, Germany, published as DISKI Series, vol. 16, ISBN 3-929037-16-6, 268 pages, Infix-Verlag, St. Augustin/Bonn, July 1992.