Overview

Dr. Dukka KC is an Associate Professor and Director for Data Analytics in the Department of Electrical Engineering and Computer Science at Wichita State University. Born and raised in the beautiful Himalyan Country Nepal, he went to Japan for his undergraduate studies. He received his B.E. in Computer Science in 2001 from the College of Engineering at Kyoto University. He then received his M.Inf. and Ph.D. in Bioinformatics from Kyoto University in 2003 and 2006 respectively working under Dr. Tatsuya Akutsu. Before he finished his Ph.D., he also worked as a visiting graduate student in the lab of Dr. Sandor Vajda at Boston University. After finishing his Ph.D, he did his first postdoctoral fellowship at Georgia Institute of Technology working under Dr. Jeffrey Skolnick . After that, he was a postdoctoral fellow at UNC-charlotte in the Department of Bioinformatics and Genomics working under Dr. Dennis Livesay . Subsequently, he also did another fellowship as a Cancer Research Training Award Fellow at National Cancer Institute at National Institutes of Heatlh (NIH) under Dr. Byungook Lee Prior to joining Wichita State University, he was an Associate Professor and Graduate Program Director in the Department of Computational Science and Engineering at North Carolina A&T State University (NCA&T).

Information

Academic Interests and Expertise

Education:

  • Ph.D. in Informatics, Kyoto University, Japan (2006)
  • M.S. in Informatics, Kyoto University, Japan (2003)
  • B.S. in Computer Science, Kyoto University, Japan (2001)

Research Interests:

  • Bioinformatics
  • Data Science
  • Big Data
  • Machine Learning
  • Deep Learning
  • High Performance Computing
Publications

Book Chapters

*:Denotes student author

1. MacCarthy E*, Perry D*, KC DB, Advances in proteins super-secondary structure
prediction and application to protein structure prediction, Methods in Molecular Biology:
Protein Supersecondary structure, Editor: Kister A, Methods Mol Biol. 2019; 1958:15-4.
2. Dennis R Livesay, KC DB, David, La, Predicting protein functional sites with
phylogenetic motifs: Past, present and beyond. Omics approaches for
protein function prediction, Kihara D (Ed.), Springer, 2010.
3. KC DB, Dennis R Livesay, A spectrum of phylogenetic-based approaches for predicting
protein functional sites, In Bioinformatics for Systems Biology, Krawetz S (Ed.), Humana
Press. ISBN: 978-1-934115-02-2, 2009.

 

Journal Articles

  • Molecular Omics on Twitter: "Hussam J. AL-barakati, Hiroto Saigo ...

 

  1. Chaudhari M*, Thapa N*, Roy, K, Newan RH, Saigo H, KC DB DeepRMethylSite: A Deep Learning Based Approach for prediction of arginine methylation sites in Proteins, Molecular Omics  2020, DOI: 10.1039/D0MO00025F (In Press)
  2. Thapa N*, Chaudhari M*, McManus S, Roy, K, Newman RH, Saigo H, KC DB  
    DeepSuccinylSite: a deep learning based approach for protein succinylation site prediction,
    BMC Bioinformatics, 21(3), 1-10, 2020.
  3. Al-barakati H*, Thapa N*, Saigo H, Roy K, Newman RH, KC DB                                                             RF-MaloSite and DL-MaloSite: Methods based on random forest and deep learning to identify malonylation sites
    Computational Structural Biotechnology Journal, 18:852-860, 2020.
  4. Al-barakati H*, Saigo H, Newman RH, KC DB
    RF-GlutarySite: a random forest based predictor for glutarylation sites, Molecular Omics, 15, 189-204, 2019 (Cover Article)
  5. Ismail H*, Saigo, H, KC DB
    RF-NR: Random Forest based approach for improved classification of Nuclear Receptors
    IEEE/ACM Transactions on Computational Biology and Bioinformatics, 15(6):1844-1852, 2018.
  6. Albarakati H*, McConnell EW, Hicks LM, Poole LB, Newman RH, KC DB

    SVM-SulfoSite: A support vector machine based predictor for sulfenylations sites
    Scientific Reports, Article Number:11288, 2018.
    [ Article]

  7. Amini H, Wang LJ, Hasemisohi A, Shahbazi A, Bikdash M, KC DB, Yuan WQ An integrated growth kinetics and computational fluid dynamics model for the analysis of algal productivity in open raceway pondsComputers and Electronics in Agriculture , 145, 363-372, 2018.[ Article ]

  8. Chapman CH*, Adami C, Wilke CO, KC DB
    The evolution of logic circuits for the purpose of protein contact map prediction
    PeerJ 5:e3139 https://doi.org/10.7717/peerj.3139, 2017.
    [Article]

  9. KC DB, Recent advances in sequence-based protein structure prediction, Briefings in Bioinformatics , 1:18(6):1021-1032, 2017.

  10. White C*, Ismail H*, Saigo, H, KC DB
    CNN-BLPred: A Convolutional Neural Network based predictor for Beta-lactamases (BL) and their classes
    BMC Bioinformatics, 18(suppl 16):577, 2017.

  11. Ismail H*, Jones A*, JH Kim, Newman RH, KC DB
    RF-Phos: A novel general phosphorylation site prediction tool based on Random Forest
    BioMed Research International, 3281590, 2016.
    [Article]

  12. Ismail H*, Newman RH, KC DB, RF-Hydroxysite: a random forest based predictor for hydroxylation sites, Molecular Biosystems, 12(8):2427-35, 2016, [Article] [Server]

  13. Jha A*, Flurchick KM, Bidash M, KC DB
    Parallel-SymD: A parallel approach to detect internal symmetry in protein domains
    BioMed Research International, 2016, 4628592.
    [Article]

  14. Jiang Y, …., Chapman CH*, KC DB,…., Predrag Radivojac, An expanded evaluation of protein function prediction methods show an improvement in accuracy, Genome Biology 17:184, 2016.[Article]

  15. Amini H*, Shahbazi A, Bikdash M, KC DB, Yuan W, Hashemisohi A*, Wang L, Numerical and experimental investigation of hydrodynamics and light transfer in open raceway ponds at various algal cell concentrations and medium depths, Chemical Engineering Science 156, 11-23, 2016.

  16. Khan IK, Wei Q, Chapman S*, KC DB, Kihara D
    PFP and ESG protein function prediction methods in 2014: Effects of database updates and ensemble approaches
    Giga Science , 4:43, 2015.

  17. Yu Y*, Megri AC, Flurchick KM, KC DB
    The improvement of Computational Perfromance of the zonal model POMA using parallel techniques
    American Journal of Engineering and Applied Sciences , 7(1), 185-193, 2014.

  18. Tai CH, Paul R, KC DB, Schilling J, Lee BK
    SymD webserver: a platform for detecting internally symmetric protein structures
    Nucleic Acids Res , 42(W1):W296-W300, 2014.

  19. KC DB
    Structure-based methods for computational protein functional site prediction
    Computational Structural Biotechnology Journal, 8:e201308005, 2013. PMID: 24688745.

  20. KC DB, Livesay DR, Topology improves phylogenetic motif functional site predictionsIEEE/ACM Transactions on Computational Biology and Bioinformatics , 8:226-233, 2011.

  21. KC DB, Improving Consensus Structure by Eliminating Averaging Artifacts, BMC Structural Biology, BMC Structural Biology, 9:12, 2009.

  22. KC DB, Livesay DR, Improving position specific predictions of protein functional sites using phylogenetic motifs, Bioinformatics, 24:2308-2316, 2008.

  23. Brown JB, KC DB, Tomita E., Akutsu T
    Multiple methods for protein side chain packing using maximum weight cliques
    Genome Informatics, 17-1, 3-12, 2006.

  24. Akutsu T, Hayashida M, KC DB, Tomita E, Suzuki J, Horimoto K, Protein Threading by Dynamic Programming and Clique Based Approaches, IEICE Transactions on Fundamentals of Electronics, Communication and Computer Science, E89-A, 1215-1222, 2006.

  25. KC DB, Tomita E, Suzuki J, Horimoto K, Akutsu T., Protein Threading With Profiles and Distance Constraints Using Clique Based Algorithms, Journal of Bioinformatics and Computational Biology , 4:19-42, 2006.

  26. KC DB, Moesa HA, Akutsu T
    Efficient Determination of Cluster Boundaries for Analysis of Gene Expression Profile Data Using Hierarchical Clustering and Wavelet Transform
    Genome Informatics ,16(1): 132-141, 2005.

  27. KC DB, Tomita E, Suzuki J. Akutsu T
    Protein side-chain packing problem: A maximum common edge-weight clique algorithmic approach,
    Journal of Bioinformatics and Computational Biology , 3(1): 103-126, 2005.

  28. KC DB, Akutsu T, Tomita E, Seki T., Fujiyama A.
    Point Matching Under Non-uniform Distortions and Protein Side Chain Packing Based on an Efficient Clique Algorithm
    Genome Informatics, 13: 143-152, 2002.

Conference Papers

  1. Thapa N*, Chaudhari M*, McManus S, Roy K, Newman RH, Saigo H, KC, DB: DeepSuccinylSite: a deep learning based approach for protein succinylation site prediction, Joint GIW/ABACBS-2019, Sydney, Dec 9-11, 2019.
  2. Albarakati H*, Saigo H, Newman RH, KC, DB: SVM-GlutarySite: A support vector machine-based prediction of Glutarylation sites from protein sequences, MCBIOS, Birmingham, Alabama, March28-30, 2019.
  3. Ocansey DT*, Aidoo M, Bikdash MU, Ismail H*, White, C*, Newman, RH, KC DB
    (2018). Performance of Canonical Correlation Forest in Phosphorylation Site Predictions, (pp. 8). SoutheastCon 2018.
  4. White C*, Ismail H,* Saigo H, KC DB: CNN-BLPred: A Convolutional Neural Network based predictor for Beta-Lactamases (BL) and their classes , International Conference on Bioinformatics (InCoB2017) , Shenzhen, China, September 20-22, 2017.
  5. Hudson T*, Harrison S, KC DB, Prins J, Muganda PM, Differential expression of human
    cytomegalovirus microRNA in triple-negative breast cancer tumors In Proceedings of the
    AACR 107th Annual Meeting, April 16-20, 2016; New Orleans, LA, AACR; DOI:
    10.1158/1538-7445, Published 15 July 2016.
  6. Chapman SD*, Adami C, Wilke CO, KC DB
    The evolution of logic circuits for the purpose of protein contact map prediction
    25th International Joint Conference on Artificial Intelligence IJCAI-16 BOOM Workshop 07/09/2016 July 9, 2016, New York, USA. [Article]
  7. Ismail H*, Jones A*, Kim J, Newman RH, KC DB: RF-Phos: Random Forest Based
    prediction fo phosphorylation sites, 2015 IEEE International Conference on
    Bioinformatics and Biomedicine (BIBM 2015), Nov 9-12, 2015, Washington DC,
    Accepted (18% acceptance rate).
  8. Ismail H*, Saigo H,  KC DB, RF-NR: Random forest based approach for improved classification of Nuclear Receptors , International Conference on Genome Informatics & International Conference on Bioinformatics (GIW/InCoB2015), Tokyo, Japan. (9 2015).
  9. Ismail H*, Jones A*, Kim J, Newman RH, KC DB: Phosphorylation site prediction using
    random forest, 5th IEEE International Conference on Computational Advances in Bio and
    Medical Sciences (ICCABS), October 15-17, Fl, USA, Accepted (50% acceptance rate).
  10. Swanson B*, Harrison S, KC DB, Muganda P
    Comparative analysis of bioinformatic tools for the detection of viral DNA sequences in tumor cells
    Proceedings of the Sixth AACR Conference: The Science of Cancer Health Disparities; Dec 6–9, 2013; Atlanta, GA. Philadelphia (PA): AACR Cancer Epidemiol Biomarkers Prev 2014;23(11 Suppl):Abstract nr C04. doi:10.1158/1538-7755.DISP13-C04, [Article]
  11. Ismail H*, Jones A*, Kim J, Newman R, KC DB
    RF-Phos: Random Forest Based prediction for phosphorylation sites
    2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2015), Nov 9-12, 2015, Washington DC. [Article]
  12. Kordmahalleh MK*, Homaifar A, KC DB
    Hierarchical Multi-label Gene Function Prediction using Adaptive Mutation in Crowding Niching
    13th IEEE International Conference on Bioinformatics and Bioengineering (BIBE 2013), 1-6, Chania, Greece, 10-13 November, 2013. [Article]
  13. Kordmahalleh MM*, Sefidmazgi MG*, Homaifar A, KC DB, Guiseppi-Elie A
    Time-series forecasting with evolvable partially connected artificial neural network
    Proceeding of Genetic and Evolution Computation Conference (GECCO14) , Pages 79-80, 2014. [Article]
  14. Chapman S*, Harrison S, Bikdash M, KC DB
    The bustle of bioinformatics: Cloudy with a Chance for Big Data
    3rd ASE Cyber Security Conferences, CA, USA, May 27-31, 2014, Pages 1-7. [Articl3]
  15. KC DB, Newman R, Comparison of Structure prediction Algorithms for multi-domain
    proteins, short paper, ISBRA 2013, Charlotte.
  16. Brown JB*, KC DB, Tomita E and Akutsu T, Multiple methods for protein side chain
    packing using maximum weight cliques, The 6th Int. Workshop on Bioinformatics and
    Systems Biology, 2006, Boston, USA
  17. KC DB, Etsuji Tomita, Jun’ichi Suzuki, K. Horimoto and Tatsuya Akutsu, Clique Based
    algorithms for protein threading with profiles and constraints, Proc. 3rd Asia-Pacific
    Bioinformatics Conference (APBC 2005), 51-64, 2005, Singapore.
  18. Moesa HA, KC DB and Akutsu T, Efficient Determination of Cluster Boundaries for
    Analysis of the Gene Expression Profile Data Using Hierarchical Clustering and Wavelet
    Transform, 5th International Workshop on Bioinformatics and Systems Biology (IBSB)
    2005, Berlin, Germany.
  19. KC DB, Brown JB, Tomita E, Suzuki J and Akutsu T, Large scale protein side-chain
    packing based on maximum edge-weight clique finding algorithm, Proc. 2005
    International Joint Conference of InCoB, AASBi and KSBI (BIOINFO2005), 228-233,
    2005, Pusang, Korea.
  20. KC DB, Akutsu T, Tomita E, Seki T, Protein Side-chain Packing: A maximum edge-
    weight Clique Algorithmic Approach, Proc. 2nd Asia-Pacific Bioinformatics Conference
    (APBC 2004), 191-200, Dunedin, New Zealand.
  21. KC DB, Akutsu T, Protein Side-chain Packing Using an Efficient Clique Algorithm, The
    2nd International Workshop on Bioinformatics and Systems Biology, 2003, Dresden,
    Germany.
  22. KC DB, Akutsu T, Tomita E, Seki T, Fujiyama A, Point Matching Under Non-uniform
    Distortions and Protein Side-chain Packing based on an efficient clique algorithm, GIW
    2002, Tokyo, Japan.
Awards and Honors

2018 Japan Society for Promotion of Science, Invitational Short term Fellowship
2015 NCA&T Release Time Proposal Development Opportunity Award
2012-2013 CAS Innovation Fund Award, NCA&T
2006 Japan Society for Promotion of Science, Postdoctoral Fellow (Declined)
2004-2005 International Communication Foundation Fellow
2003-2004 Mitsubishi Yamamuro Memorial Scholarship
2001-2003 Japanese Government Scholarship for Graduate Students
1996-2001 Japanese Government Scholarship for Undergraduate Students

Grants

Funded Research Grants

Wichita State University, Multidisciplinary Research Project (MURPA), 2019, $7500 (Wang, PI, KC, co-PI), Machine learning directed search for novel non-linear optical materials.

IIS-2003019, NSF (KC,PI), 11/12/2019-07/31/2023 Total: $111,600 III:Medium: Collaborative Research: Multi-level computational approaches to protein function prediction.

73833-CS-RIP ARO, DOA, DOD(KC, co-PI), 04/01/2019-03/01/2020, Total: $200K
A biometric test-bed to support NCAT’s biometric research and education

EIR-1901793, NSF (KC, Former PI), 06/17/2019-06/30/2022 Total: $489,021, Excellence in Research: Deep Learning based approaches for protein post-translational modification site prediction.

5R25GM119987, National Institutes of Health, NIGMS (KC, Multi-PI), 09/10/2018-07/31/2023 Total: $1,356,813
Bridges to Doctorate: Bioinformatics Bridges between NCA&T and UNC-CH

HRD-1818679, NSF, (Monty, PI, KC, Co-PI), 08/01/2018-08/31/2021, Total: $349,998
Broadening Participation Research Project: STEP into STEM, successful transitions and
effective pathways into STEM

REU-SITE 1852319, NSF (Newman, PI, KC, Co-PI), 03/01/2019-02/28/2022, Total $230,158, A multi-site REU in synthetic biology.

DBI-1564604 , NSF, (KC, PI), 08/01/2016-07/31/2020 Total: $144,602
Collaborative Research: ABI Development: Integrated platforms for protein structure and
function predictions.

EAGER-1647994, NSF, (KC, PI), 09/01/2016-08/31/2018 Total:$149,999 EAGER: A novel approach to improve template-based multi-domain protein structure prediction

DBI-0939454.NSF (Beacon Program) (KC, PI) , 08/01/2017-07/31/2018, $42,336.Computational studies to elucidate evolutionary conservation of phosphorylation sites.

Amazon Web Services Research Grant (KC, PI) 11/2014-11/2016 $15,000.Pathogen sequence identification using Cloud Computing.

DBI-0939454.NSF (Beacon Program) (Newman, PI, KC, Co-PI) 08/15-07/2016, $131,525.Directed Evolution of Novel Protein Functions using expanded genetic codes.

DBI-0939454 NSF (Beacon Program) (KC, PI) 08/2015-07/2016, $80,63, Improving Contact Map prediction combining correlated mutation information using Evolutionary computation.

UNC-CH North Carolina Translational and Clinical Science (NC Tracs) Translational
Research Pilot Grant, (Muganda, PI; KC, Co-PI) 5/1/2014-4/30/2015, $50,000Role of viral factors in Triple-negative breast cancer pathogenesis

NVidia Research Grant, (KC, PI) 04/2013-02/2014, $3000.Protein Structure comparison in protein symmetry detection using GPU.