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Journal Publication (Refereed)

  1. C. Lee, M. Whooley, K. Niitsu, and W. Kim, Network Motif Detection in the Network of Inflammatory Markers and Depression Symptoms among Patients with Stable Coronary Heart Disease: Insights from the Heart and Soul Study, Psychology International, no. 2 (2024): 440-453
  2. W. Kim, Y. Hsu, Z. Li, P. Mar, and Y. Wang NemoSuite: Web-based Network Motif Analytic Suite, Advances in Science, Technology and Engineering Journal, vol. 5, Issue 6, pp. 1545-1554 (2020)
  3. T. Huynh, S. Mbadiwe and W. Kim. NemoMap: Improved Motif-centric Network Motif Discovery Algorithm, Advances in Science, Technology and Engineering Journal, vol. 3, no. 5, pp. 186-199 (2018)
  4. W. Kim, L. Haukap, NemoProfile as an efficient approach to network motif analysis with instance collection, BMC Bioinformatics, 18(Suppl12):423, 2017
  5. V. Verma, P. Kwon, A. Joglekar, and W. Kim, Network Motif Analysis in Clouds - Subgraph Enumeration with Iterative Hadoop MapReduce, International Journal of Cloud Computing, Services Transactions on Cloud Computing (STCC), 4(4), 2016, pp. 28-40.
  6. W. Kim, S. Kurmar, Sensible Method for Updating Motif Instances in an Increased Biological Network, Elsevier METHODS, Volume 83, 15 July 2015, pp. 71-79, ISSN 1046-2023, http://dx.doi.org/10.1016/j.ynetg.2015.04.007, 2015
  7. B. Chen, M. Kim, M. Johnson, W. Kim and Y. Pan, Protein Local Tertiary Structure Prediction by Super Granule Support Vector Machines with Chou-Fasman Parameter, International Journal of Computational Biology, vol. 1, num. 1, p. 14-27, Apr 2014.
  8. W. Kim, M. Diko and K. Rawson, Network Motif Detection: Algorithms, Parallel and Cloud Computing, and Related Tools, Bioinformatics and Computational Biology of Tsinghua Science and Technology Journal, Volume 18, issue 5, pp. 469-489, 2013
  9. W. Kim, Prediction of essential proteins using topological properties in GO-pruned PPI network based on machine learning methods, Bioinformatics and Computational Biology of Tsinghua Science and Technology Journal, Volume 17, no:6, pp645-658, Dec. 2012
  10. W.Kim, M. Li, J. Wang and Y. Pan, Biological Network Motif Detection and Evaluation, BMC Systems Biology, 5 (Suppl 3), pp. s5, 2011.
  11. W. Kim, B. Chen, J. Kim Y. Pan , H. Park, Sparse Nonnegative Matrix Factorization for ProteinSequence Motif Discovery, Expert Systems with Applications, Volume 38, Issue 10, Pages 13198-13207. ISSN 0957-4174, DOI: 10.1016/j.eswa.2011.04.133, 15 September 2011.

Conference Publication (Refereed)

  1. M. Boddam and W. Kim, Interpretable Deep Learning Models With Concept Whitening Layers, In 2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1824-1827. IEEE, 2023.
  2. K. Sanyour and W. Kim, Machine Learning Algorithms in Gene Editing, 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Las Vegas, Nevada, USA, December 6-8, 2022, pp. 799-802, doi:10.1109/BIBM55620.2022.9995135.
  3. Z. Li and W. Kim, Investigating statistical analysis for network motifs, In the 12th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics 2021, Gainesville, Florida, Article No.:40, Pages 1-6
  4. Y. Xiao and W. Kim. Identification of essential genes with NemoProfile and various machine learning models, 16th International Symposium on Bioinformatics Research and Applications (ISBRA), pp. 319-326. Springer International Publishing, 2020
  5. W. Kim and Z. A. Brader. NemoLib: Network Motif Libraries for network motif detection and analysis, 16th International Symposium on Bioinformatics Research and Applications (ISBRA), pp. 327-334. Springer International Publishing, 2020
  6. N. Rohde and W. Kim, Benchmarking of Structural Variant Detection Tools for Microorganism,2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), San Diego, CA, USA, pp. 2005-2012, 2019
  7. F. Li, S. Smith, and W. Kim, VIA-QMI: A visualized data analytic tool for Quantitative Multiplex Co-Immunoprecipitation(QMI) Platform,2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 2136-2143, Madrid, Spain, 2018
  8. S. Mbadiwe and W. Kim. ParaMODA: Improving Motif-Centric Subgraph Pattern Search in PPI Networks, IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1723-1730, Kansas City, MO., 2017
  9. A. Andersen and W. Kim. NemoLib: A Java Library for Efficient Network Motif Detection, 13th International Symposium on Bioinformatics Research and Applications (ISBRA), Honolulu, HI, USA, May 29 June 2, 2017, Proceedings.Springer International Publishing, pp. 403-407, 2017.
  10. A. Andersen, W. Kim, and M. Fukuda, MASS-Based NemoProfile Construction for an Efficient Network Motif Search, 2016 IEEE International Conferences on Big Data and Cloud Computing (BDCloud), Social Computing and Networking (SocialCom), Sustainable Computing and Communications (SustainCom) (BDCloud-SocialCom-SustainCom), pp. 601-606. Atlanta, GA, 2016
  11. M. Kipps, W. Kim, M. Fukuda, Agent and Spatial Based Parallelization of Biological Network Motif Search, IEEE High Performance Computing and Communications (HPCC), pp. 786-791, New York, NY, 2015
  12. V. Verma, P. Kwon, and W. Kim, Iterative Hadoop MapReduce-Based Subgraph Enumeration in Network Motif Analysis, in Calton Pu & Ajay Mohindra, ed., IEEE CLOUD, pp. 893-900 , 2015
  13. W. Kim, S. Kurmar, Efficient Updates of network motif instances in the extended protein-protein inter action network, In IEEE International Conference on Bioinformatics and Biomedicine, pp. 119-124,2014
  14. W.Kim, M. Li, J. Wang and Y. Pan, , Essential Protein Discovery based on Network Motif and Gene Ontology, IEEE International Conference on Bioinformatics and Biomedicine, pp. 470~475, 2011.
  15. W. Kim and J.M. Rehg, Detection of Unnatural Movement Using Epitomic Analysis, in Proc. ICMLA, pp.271-276, 2008

Conference Proceeding - short paper

  1. A. Singh and W. Kim, Detection of Diabetic Blindness with Deep-Learning, The 4th International Workshop on Deep Learning in Bioinformatics, Biomedicine, and Healthcare Informatics (DLB2H 2020), 2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Seoul, Korea (South), 2020, pp. 2440-2447, doi: 10.1109/BIBM49941.2020.9313392
  2. N. Rohde and W. Kim, NemoCluster: Graph Clustering Algorithm Utilizing Higher Order Network Motifs, 16th International Symposium on Bioinformatics Research and Applications (ISBRA), 2020
  3. W. Kim, and L. Haukap, NemoProfile: effective representation for network motifs and their instances, 12th International Symposium on Bioinformatics Research and Applications (ISBRA), 2016

Poster Publication

  1. M. Lakic, B. Nega and W. Kim. Web-QuateXelero: Web-based efficient network motif detection tool, 2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2020.
  2. P. Mar and W. Kim. NemoMapPy: Motif-centric network motif search on a web, 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2019.
  3. Y. M. Seah, W. Kim, J. Stopnisek, D. Stahl, K. Hillesland. Elucidating the Selection Dynamics of a Coevolving Microbial Mutualism, The Gordon Research Seminar on Ecological and Evolutionary Genomics, 2019.
  4. Hoi Yan Wu, Conard Faraon, Lisa Kim, Sheng Lin, Wai Kwan Shum, Yee Mey Seah, Kristina Hillesland and W. Kim, Web-based Database System to Detect Fluctuating Selection in a Microbial Mutualism, International Conference on Research in Computational Molecular Biology (Recomb) 2019
  5. W. Kim. A. Andersen, J. Baker, R. Berge, D. Dehaas, and C. Luong. NemoLib: Network Motif Library in C++, Java and Python. Intelligent Systems for Molecular Biology 2018.
  6. Y. M. Seah, W. Kim, and K. Hillesland. Detecting Fluctuating Selection in a Nascent Microbial Mutualism, Poster presentation in Evo-Bio, 2018.
  7. S. Nareddy, E. Westover, K. Hillesland, and W. Kim, Genome Dynamics in Coevolved Genomes: Database Man agement System for Tracing Mutations, In The 5th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics, 2014
  8. W. Kim, N. Jojic and J. M. Rehg , Epitomic Analysis of Human Motion, The Learning Workshop @ snowbird, Computational and biological learning society, 2005.

Software Program

  1. Network Motif Library (Network-based approach)
    • NemoLibC++ DOI
    • NemoLib Java DOI
    • NemoLib Python DOI
  2. NemoMap DOI : Improved ParaMODA
  3. ParaMODA DOI: Improved Grochow-Kellis' method

 
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