Bernard Chen


Associate Professor
University of Central Arkansas


Graduate Faculty
Department of Computer Science


Email: bchen(at)uca.edu Phone: 501-450-3308
Office: Math Computer Science and Technology Room 304     
Conway, AR 72035


Brief Bio

Dr. Bernard Chen received his B.S. degree in Computer Science and Information Engineering from the Fu Jen Catholic University (Taipei, Taiwan). He recievced his Ph.D. degree in Computer Science from Georgia State university (Atlanta, GA) His research interests includ Data Science, Data Mining, Bioinformatics, Fuzzy logic and granule computing.

Research

Journal Publications:

  1. Bernard Chen, Hai Le, Travis Atkison, Dongsheng Che. "A Wineinformatics Study for White-box Classification Algorithms to Understand and Evaluate Wine Judges", Accepted by Transactions on Machine Learning and Data Mining (Impact Factor 0.62).

  2. Cody Hudson, Bernard Chen, Dongsheng Che, "Hierarchically Cluster HMM (HC-HMM) for Protein Sequence Motif Extraction with Variable Length", Tsinghua Science and Technology journal, vol. 19, issue 6, 2014

  3. Dongsheng Che, Han Wang, John Fazekas, and Bernard Chen, "An Accurate Genomic Island Prediction Method for Sequenced Bacterial and Archaeal Genomes", Journal of Proteomics and Bioinformatics, Journal of Proteomics and Bioinformatics 2014 7:8 (Impact factor 2.56)

  4. Dongsheng Che, Mohammad Shabbir Hasan and Bernard Chen, "Identifying Pathogenicity Islands in Bacterial Pathogenomics Using Computational Approaches", Pathogens 2014, 3(1), 36-56

  5. Bernard Chen, Minwoo Kim, Matthew Johnson, Wooyoung Kim and Yi Pan, "Protein Local Tertiary Structure Prediction by Super Granule Support Vector Machines with Chou-Fasman Parameter", International Journal of Computational Biology, 0002:20-43, 2012.

  6. Mohammand Shabbir Hasan, Qi Liu, Han Wang, John Fazekas, Bernard Chen, Dongsheng Che, "GIST: Genomic island suite of tools for predicting genomic islands in genomic sequences"Bioinformation, 8(4): 203-205 (2012) (Impact factor 1.15)

  7. Wooyoung Kim, Bernard Chen, Jingu Kim, Yi Pan, Haesun Park, "Sparse nonnegative matrix factorization for protein sequence motif discovery", Expert Syst. Appl. 38(10): 13198-13270 (2011) (2011 Impact factor 1.924)

  8. Bernard Chen, Jieyue He, Stephen Pellicer and Yi Pan, "Using Hybrid Hierarchical K-means Clustering Algorithm for Protein Sequence Motif Super-Rule-Tree (SRT) Structure Construction", International Journal of Data Mining and Bioinformatics (SCI indexed), Volume 4 - Issue 3 - 2010, pp.  316-330. 

  9. Sinan Kockara, Mutlu Mete, Bernard Chen, Kemal Aydin, "Analysis of density based and fuzzy c-means clustering methods on lesion border extraction in dermoscopy images", BMC Bioinformatics 2010: Vol. 11, S26 (2010 Impact factor 3.43)

  10. Bernard Chen and Matthew Johnson,  "Protein Local 3D Structure Prediction by Super Granule Support Vector Machines (Super GSVM)", BMC Bioinformatics 2009, 10(Suppl 11):S15 (8 October 2009) (2009 Impact factor 3.78).  

  11. Bernard Chen, Stephen Pellicer, Phang C. Tai, Robert Harrison and Yi Pan, "Novel efficient granular models for protein sequence motifs and structure discovery", International Journal of Computational Biology and Drug Design, Volume 2 - Issue 2 - 2009, pp. 168-186 (Acceptance Rate: 15%)

  12. Bernard Chen, Stephen Pellicer, Phang C. Tai, Robert Harrison and Yi Pan, "Efficient Super Granular SVM Feature Elimination (Super GSVM-FE) Model for Protein Sequence Motif Information Extraction", International Journal of Functional Informatics and Personalised Medicine, 2008 Vol. 1. No. 1, pp. 8-25. 

 

Conference Publications:

  1. Bernard Chen, Christopher Rhodes, Alexander Yu, and Valentin Velchev. "The Computational Wine Wheel 2.0 and the TriMax Triclustering in Wineinformatics", In Industrial Conference on Data Mining, pp. 223-238. Springer International Publishing, 2016.

  2. Bernard Chen, Hai Le, Christopher Rhodes, and Dongsheng Che. "Understanding the Wine Judges and Evaluating the Consistency Through White-Box Classification Algorithms.", In Industrial Conference on Data Mining, pp. 239-252. Springer International Publishing, 2016

  3. Li, Zuqing, Bernard Chen, and Dongsheng Che. "Solving the Subgraph Isomorphism Problem Using Simulated Annealing and Evolutionary Algorithms.", In Proceedings on the International Conference on Artificial Intelligence (ICAI), p. 293. The Steering Committee of The World Congress in Computer Science, Computer Engineering and Applied Computing (WorldComp), 2016.

  4. Che, Dongsheng, Sai Vahini Manikonda, Zuqing Li, and Bernard Chen. "An Anomaly Detection Algorithm for Identifying Alien Gene Clusters in Microbial Genomes.", In Proceedings of the International Conference on Bioinformatics & Computational Biology (BIOCOMP), p. 31. The Steering Committee of The World Congress in Computer Science, Computer Engineering and Applied Computing (WorldComp), 2016.

  5. Bernard Chen, Valentin Velchev, Bryce Nicholson, Joey Garrison, Moani Iwamura, Ryan Battisto, "Wineinformatics: Uncork Napa’s Cabernet Sauvignon by Association Rule Based Classification", In Machine Learning and Applications (ICMLA), 2015 IEEE 14th International Conference on, pp. 565-569. IEEE, 2015.

  6. Bernard Chen, Christopher Rhodes, Aaron Crawford, Lorri Hambuchen "Wineinformatics: Applying Data Mining on Wine Sensory Reviews Processed by the Computational Wine Wheel" , 2014 IEEE Internatioanl Workshop on Domain Driven Data Mining(DDDM 2014), proceeding pp. 142-149.   

  7. Christopher Mitchell, Chenyi Hu,Bernard Chen, Michael Nooner, and Paul Young, "A Computational Study of Interval-Valued Matrix Games", The 2014 International Conference on Computational Science and Computational Intelligence (CSCI 14), proceeding pp. 347-352 (acceptance rate 23%).

  8. Cody Hudson, Bernard Chen, "Variable-Length Protein Sequence Motif Extraction Using Hierarchically-Clustered Hidden Markov Models", 12th International Conference on Machine Learning and Applications (ICMLA 2013), proceeding pp. 173-178. (Regular paper acceptance rate: 26%)   

  9. Bernard Chen, Roshan Doolabh, Fusheng Tang, "Determining Potential Yeast Longevity Genes via PPI Networks and Microarray Data Clustering Analysis", 12th International Conference on Machine Learning and Applications (ICMLA 2013), proceeding pp. 370-373.   

  10. Bernard Chen, Cody Hudson, Aaron Craeford, Minwoo Kim, "Protein Local Tertiary Structure Prediction Using the Adaptively-Branching FGK-DF Model", 12th International Conference on Machine Learning and Applications (ICMLA 2013), proceeding pp. 378-381.   

  11. Bernard Chen, Benjamine Nordin, Chenyi Hu "Discovering Protein Sequence Motifs by the Improved FGK Model", The 6th International Conference on Bioinformatics and Biomedical Engineering (iCBBE 2012), Shanghai, China, Proceeding Vol.2 pp.31~35. 

  12. Bernard Chen, Michael Miller, Matthew Erby, Bradley Taylor "Mining Distance Association Rules from Protein Sequence Motifs", The 6th International Conference on Bioinformatics and Biomedical Engineering (iCBBE 2012), Shanghai, China, Proceeding Vol.2 pp.398~401. 

  13. Bernard Chen, Ben Nordin, Sriram Bobba, Devendar Singireddy, Brad Taylor, Sinan Kockara and Mutlu Mete, "Clustering on Protein Sequence Motifs using SCAN and Positional Association Rule Algorithm", International Conference on Bioinformatics & Computational Biology (BIOCOMP2011), Las Vegas, USA, Proceeding pp.85~90.  (Regular Research Paper (RRP) Acceptance Rate: ~21%)

  14. Bernard Chen, Sait Suer, Muhyeddin Ercan, Rahul Tada, Recep Avci, and Sinan Kockaara, "Constructing Super Rule Tree (SRT) for Protein Motif Clusters Using DBSCAN", International Conference on Bioinformatics & Computational Biology (BIOCOMP2011), Las Vegas, USA, Proceeding pp.79~84.  (Regular Research Paper (RRP) Acceptance Rate: ~21%)

  15. B. Chen, C. Hudson, M. Kim, A. Crawford, C. Write and D. Che, "Protein Sequence Motifs Extraction Using Decision Forest",  International Conference on Bioinformatics & Computational Biology (BIOCOMP2011), Las Vegas, USA, Proceeding pp.96~102.  (Regular Research Paper (RRP) Acceptance Rate: ~21%)

  16. Bernard Chen, Michael Miller, Timothy Montgomery, Terrance Griffin, "Clustering Using Positional Association Rules Algorithm on Protein Sequence Motifs", International Conference on Bioinformatics & Computational Biology (BIOCOMP2010), Las Vegas, USA, pp.75~80.  (Regular Research Paper (RRP) Acceptance Rate: ~27%)

  17. Vincent Yip, Bernard Chen, Sinan Kockara, "Extraction of Protein Sequence Motifs Information by Bi-Clustering Algorithm", International Conference on Bioinformatics & Computational Biology (BIOCOMP2010), Las Vegas, USA, pp.185~190 (Regular Research Paper (RRP) Acceptance Rate: ~27%

  18. Bernard Chen, Christopher Rhodes, Christopher Kline, Luke Irvin, " Protein Sequence Motif Information Generated by Fuzzy - Hybrid Hierarchical K-Means Clustering Algorithm", International Conference on Bioinformatics & Computational Biology (BIOCOMP2010), Las Vegas, USA, pp.198~201 (Short Research Paper (SRP) Accepted) 

  19. Bernard Chen, Mutlu Mete, and Sinan Kockara, "Parameter-free Multi-level Fuzzy C-means Clustering For Unsupervised Structure Detection in Histological Images", SDPS 2010 Transformative Systems Conference, Dallas, USA,   Proceeding Available Under: http://sdps.omnibooksonline.com/2010/index.html

  20. Abhinav Atla, Naveen Singireddy,  Pavan Marupally, Shabbir Ahmed, and Bernard Chen, "Fuzzy BIRCH Clustering Model: An Efficient Way for Protein Sequence Motifs Information Discovery", Acxiom ALAR 2010, Conway AR, USA.

  21. Abhinav Atla, Benjamine Nordin, Matthew Johnson, Bernard Chen, and Chenyi Hu, "An Interval K-means Algorithm and its Application", Acxiom ALAR 2010, Conway AR, USA. (Best student paper award)

  22. Bernard Chen, and Sinan Kockara, "Mining Positional Association Super-Rules on Fixed-Size Protein Sequence motifs", IEEE BIBE 2009, Taichung, Taiwan, proceeding pp. 1-8

  23. T. Halic, S. Kockara, C. Bayrak, R. Rowe, and B. Chen, "Soft Tissue Deformation and Optimized Data Structure for Mass Spring Methods", IEEE BIBE 2009, Taichung, Taiwan, proceeding pp. 45-52 (Acceptance Rate: ~32%)

  24. Bernard Chen, Jieyue He, Stephen Pellicer, and Yi Pan, "Protein Sequence Motif Super-Rule-Tree (SRT) Structure Constructed by Hybrid Hierarchical K-means Clustering Algorithm", IEEE BIBM 2008, Philadelphia, proceeding pp. 98-103 (Acceptance Rate: ~32%)     

  25. Bernard Chen, Stephen Pellicer, Phang C. Tai, Robert Harrison and Yi Pan, "Super Granular Shrink-SVM Feature Elimination (Super GS-SVM-FE) Model for Protein Sequence Motif Information Extraction", IEEE BIBE 2007,Boston, proceeding pp. 379-386 (First Runner-Up Best Student Research Paper)                

  26. Bernard Chen, Stephen Pellicer, Phang C. Tai, Robert Harrison and Yi Pan, "Super Granular SVM Feature Elimination (Super GSVM-FE) Model for  Protein Sequence Motif Information Extraction", IEEE CIBCB 2007, Hawaii, proceeding pp.317-323. 

  27. Xuezheng Fu, Bernard Chen, Yi Pan and Robert Harrison. "Statistical Estimate for the Size of the Protein Structural Vocabulary" ISBRA 2007, Altanta, proceeding pp.530-538

  28. Bernard Chen, Phang C.  Tai, Robert Harrison, and Yi Pan,  ?b>FGK model: A Efficient  Granular Computing Model  for  Protein Sequence  Motifs Information Discovery? IASTED CASB 2006, Dallas, proceeding pp56-61.   

  29. Bernard Chen, Phang C. Tai,  Robert Harrison,  and Yi Pan,  ?b>FIK model:  A Novel Efficient Granular  Computing Model for Protein Sequence Motifs and Structure Information Discovery? IEEE BIBE 2006, Washington D.C. , proceding, pp20-26. 

  30. Bernard Chen, Phang C. Tai, Robert Harrison,  and Yi Pan,   "Novel  Clustering  Algorithm  Combined  With DSSP Post Processing For  Protein Sequence Motif Discovering", IEEE GrC 2006, Atlanta, proceeding, pp.449-452

  31. B. Chen, P. Tai, R. Harrison, and Y. Pan, "Novel Hybrid Hierarchical-Kmeans Clustering (H-K-means) for Microarray analysis," IEEE CSB2005, Stanford,  Workshops and Poster Abstracts, pp.105-108

  32. J. He,  B. Chen,  H Hu,  R. Harrison,  P. Tai,  Y. Dong,  and Y. Pan, "Rule  Clustering  and  Super-Rule  Generation for  Transmembrane  segments prediction," IEEE CSB2005, Stanford, workshops and Poster Abstracts, pp. 224-227

 

Book Chapters:

  1. J. He,  H. Hu, B. Chen,  H Hu,  R. Harrison,  P. Tai,  Y. Dong,  and Y. Pan, "Rule Extraction from SVM for Protein Structure Prediction", Rule Extraction form Support Vector Machines, Chapter 10, pp. 227-252