Publications

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Journal Articles

  1. J. Eickholt, D. Kelly, J. Bryan, S. Miehls, D. Zielinski. (2020) Advancements Towards Selective Barrier Passage by Automatic Species Identification: Applications of Deep Convolutional Neural Networks on Images of Dewatered Fish. ICES Journal of Marine Science. [Accepted]
  2. J. Eickholt, M.R. Johnson, P. Seeling. (2020) Practical Active Learning Stations to Transform Existing Learning Environments into Flexible, Active Learning Classrooms. IEEE Transactions on Education.
  3. N. Bravata, D. Kelly, J. Eickholt, J. Bryan, S. Miehls, D. Zielinski. (2020) Applications of Deep Convolutional Neural Networks to Predict Length, Circumference and Weight from Mostly Dewatered Images of Fish. Ecology and Evolution. 10.1002/ece3.6618.
  4. J. Smith, M. Conover, N. Stephenson, J. Eickholt, D. Si, M. Sun and R. Cao. (2020) TopQA: A Topological Representation for Single-Model Protein Quality Assessment with Machine Learning. International Journal of Computational Biology and Drug Design, https://doi.org/10.1504/IJCBDD.2020.105095.
  5. C. Phillips and J. Eickholt. (2020) LAGradebook: A Tool for Course-Level Comparative Learning Analytics. The Journal of Computing Sciences in Colleges, 35, 6.
  6. M. Johnson, Q. Cole, J. Eickholt. (2020) Exploring Differences in Students’ Perceptions of Traditional and Economy Active Learning Classrooms in an Undergraduate Computer Science Course. Journal on Excellence in College Teaching. [Accepted].
  7. J. Eickholt, V. Jogiparthi, P. Seeling, Q. Hinton, M. Johnson. (2019) Supporting Project-based Learning through Economical and Flexible Learning Spaces. Education Sciences. 9(3), 212.
  8. R. Joshi, J. Eickholt, L. Li, M. Fornari, V. Barone, J. Peralta. (2019) Machine Learning the Voltage of Electrode Materials in Metal-Ion Batteries. ACS Applied Material Interfaces. 11, 20 https://doi.org/10.1021/acsami.9b04933.
  9. E. McCann, L. Li, K. Pangle, N. Johnson and J. Eickholt. (2018) An Underwater Observation Dataset for Fish Classification and Fishery Assessment. Scientific Data 5,180190.
  10. T. Liu, Y. Wang, J. Eickholt and Z. Wang. (2016) Benchmarking Deep Networks for Predicting Residue-Specific Quality of Individual Protein Models in CASP11. Scientific Reports. 6,19301. 10.1038/srep19301.
  11. X. Deng, J. Gumm, S. Karki, J. Eickholt and J. Cheng. (2015) An Overview of Practical Applications of Protein Disorder Prediction and Drive for Faster, More Accurate Predictions. International Journal of Molecular Sciences, 16(7). 10.3390/ijms160715384.
  12. R. Kendra, S. Karki, J. Eickholt and L. Gandy. (2015) Characterizing the Discussion of Antibiotics in the Twittersphere: What is the Bigger Picture. Journal of Medical Internet Reserach, 17(6). 10.2196/jmir.4220.
  13. T. Jo, J. Hie, J. Eickholt and J. Cheng. (2015) Improving Protein Fold Recognition by Deep Learning Networks. Scientific Reports. 5,17573. 10.1038/srep17573.
  14. J. Eickholt and Z. Wang. (2014) PCP-ML: Protein Characterization Package for Machine Learning. BMC Research Notes, 7:810. 10.1186/1756-0500-7-810.
  15. M. Spencer, J. Eickholt and J. Cheng. (2014) A Deep Learning Network Approach to ab initio Protein Secondary Structure Prediction. IEEE/ACM Transactions on Computational Biology and Bioinformatics. 10.1109/TCBB.2014.2343960.
  16. J. Eickholt and J. Cheng. (2013) A Study and Benchmark of DNcon: a method for protein residue-residue contact prediction using deep networks. BMC Bioinformatics, 14(Suppl 14):S12.
  17. J. Eickholt and J. Cheng. (2013) DNdisorder: Predicting Protein Disorder Using Boosting and Deep Networks. BMC Bioinformatics, 14(1):88
  18. J. Li, X. Deng, J. Eickholt, J. Cheng. (2013) Designing and Benchmarking the MULTICOM Protein Structure Prediction System. BMC Structural Biology. 13:2.
  19. J. Eickholt and J. Cheng. (2012) Predicting Protein Residue-Residue Contacts using Deep Networks and Boosting. Bioinformatics, DOI:10.1093/bioinformatics/bts598.
  20. J. Cheng, J. Li, Z. Wang, J. Eickholt, X. Deng. (2012) The MULTICOM Toolbox for Protein Structure Prediction. BMC Bioinformatics, 13:65.
  21. J. Cheng, J. Eickholt, Z. Wang, and X. Deng. (2012) Recursive Protein Modeling: a Divide and Conquer Strategy for Protein Structure Prediction and its Case Study in CASP9. Journal of Bioinformatics and Computational Biology. DOI: 10.1142/S0219720012420036
  22. J. Eickholt, Z. Wang, J. Cheng. (2011) A Conformation Ensemble Approach to Protein Contact Map Prediction. BMC Structural Biology, 11, 38.
  23. X. Deng, J. Eickholt, J. Cheng. (2011) A Comprehensive Overview of Computational Protein Disorder Prediction Methods. Molecular BioSystems, DOI:10.1039/C1MB05207A.
  24. Z. Wang, J. Eickholt, J. Cheng. (2011) APOLLO: A Quality Assessment Service for Single and Multiple Protein Models. Bioinformatics.
  25. J. Eickholt, X. Deng, and J. Cheng. (2011) DoBo: Protein Domain Boundary Prediction by Integrating Evolutionary Signals and Machine Learning. BMC Bioinformatics, 12, 43.
  26. Z. Wang, J. Eickholt, and J. Cheng. (2010) MULTICOM: A Multi-Level Combination Approach to Protein Structure Prediction and its Assessments in CASP8. Bioinformatics, 26, 882-888.
  27. X. Deng, J. Eickholt, and J. Cheng. (2009) PreDisorder: Ab Initio Sequence-Based Prediction of Protein Disordered Regions. BMC Bioinformatics, 10, 436.
  28. A.N. Tegge, Z. Wang, J. Eickholt, and J. Cheng. (2009) NNcon: Improved Protein Contact Map Prediction Using 2D-Recursive Neural Networks. Nucleic Acids Research, 37(Web Server issue), W515-8.
  29. J. Cheng, Z. Wang, A.N. Tegge, and J. Eickholt. (2009) Prediction of Global and Local Quality of CASP8 Models by MULTICOM Series. Proteins, 77(Suppl 9), 181-184.

Conference Proceedings

  1. J. Eickholt. (2020) Supporting Instructor Reflection on Employed Teaching Techniques via Multimodal Instructor Analytics. IEEE Frontiers in Education. [Accepted]
  2. P. Seeling, J. Eickholt, Q. Hinton, M. Johnson. (2019) Low-Cost Active Learning Benefits for Introductory Computer Science Courses. IEEE Frontiers in Education, Cincinnati, Ohio.
  3. J. Eickholt, L. Gandy, P. Seeling, M. Johnson. (2019) Advancing Adoption of Active Learning Pedagogy via New Avenues of Research and Training. IEEE Frontiers in Education, Cincinnati, Ohio.
  4. L. Li, T. Danner, J. Eickholt, E. McCann, K. Pangle, N:wq. Johnson. (2017) A Distributed Pipeline for DIDSON Data Processing. BigPMA 2017, Boston, Massachusetts.
  5. Q. Cole, M. Johnson, J. Eickholt. (2017) Creating Economy Active Learning Classrooms for IT Students. Annual Conference on Information Technology Education (SIGITE 2017), Rochester, New York.
  6. J. Eickholt, J. Roush, P. Seeling, T. Vedantham, and M. Johnson. (2017) Supporting Active Learning though Commodity and Open Source Solutions. IEEE Frontiers in Education, Indianapolis, Indiana.
  7. P. Seeling and J. Eickholt. (2017) Levels of Active Learning in Programming Skill Acquisition: From Lecture to Active Learning Rooms. IEEE Fronteirs in Education, Indianapolis, Indiana.
  8. J. Eickholt and S. Shrestha. (2017) Teaching Big Data and Cloud Computing with a Physical Cluster. The Special Interest Group on Computer Science Education Technical Symposium (SIGCSE 2017), Seattle, Washington.
  9. J. Eickholt and E. Mirielli. (2016) A Bioinformatics Approach for Exploring Text-processing: Calculating Protein Weights. CCSC Central Plains Conference, St. Joseph, Missouri.
  10. J. Litchfield, S. Ledsworth, J. Eickholt. (2015) Incorporating Economy Hardware into a Computer Design and Architecture Course . CCSC Midwest Conference, Evansville, Indiana.
  11. J. Eickholt. (2015) Adding Computing Theory to a Course on Computer Design and Architecture. CCSC Central Plains Conference, Point Lookout, Missouri.
  12. J. Eickholt and S. Karki. (2014) Adopting the MapReduce Framework to Pre-train 1-D and 2-D Protein Structure Predictors with Large Protein Datasets. IEEE International Conference on Bioinformatics and Biomedicine Workshops, Belfast, N. Ireland.
  13. J. Cheng, J. Eickholt, Z. Wang, and X. Deng. (2011) Recursive Protein Modeling: a Divide and Conquer Strategy for Protein Structure Prediction and its Case Study in CASP9. Computational Structural Bioinformatics Workshop, Atlanta, Georgia.

Conference Presentations

  1. A. Charmelo, J. Eickholt, J. Golden, D. Quesada, E. Strzalkowski, R. Francis. (2020) Motivating Effective Use of Gamification in Higher Education Michigan Academy of Science, Arts, & Letters 2020 Conference, Southfield, MI. [Accepted].
  2. M. Johnson, J. Eickholt, P. Seeling, Q. Hinton. (2019) Evaluating Students Experiences in Traditional and Economy Active Learning STEM Classrooms. Original Lilly Conference on College Teaching, Oxford, Ohio.

Conference Abstracts

  1. J. Eickholt and P. Seeling. (2020) BoF: Pedagogy and Classroom: How Can I Do This and That Space or Does it Even Matter?The Special Interest Group on Computer Science Education Technical Symposium (SIGCSE 2020), Portland, Oregon. [Handout]

Book Chapters

  1. J. Li, D. Bhattacharya, R. Cao, B. Adhikari, X. Deng, J. Eickholt, J. Cheng. The MULTICOM Protein Tertiary Structure Prediction System, in Protein Structure Prediction, 2014.

Tutorials and Workshops

  1. J. Eickholt, P. Seeling, L. Gandy, Q. Cole and M. Johnson. (2018) Confronting Barriers to Active Learning in Computer Science Through Technology, Community and Culture. CCSC Rocky Mountain Conference, Soccoro, New Mexico.
  2. J. Eickholt, P. Seeling, L. Gandy, Q. Cole and M. Johnson. (2018) Creating a Culture and Environment for Active Learning Success. CCSC Eastern Conference, Arlington, Virginia.
  3. J. Eickholt, P. Seeling and M. Johnson. (2017) Supporting Active Learning in Computer Science Through Technology and Community. CCSC Midwest Conference, Grand Rapids, Michigan.

Datasets and Software

  1. S. Miehls, J. Bryan, J. Eickholt. (2020). FishL Low Resolution Images of Fish from the Great Lakes Region. Open Science Framework.
  2. N Bravata, D. Kelly, J. Eickholt. (2020). Tools to Apply Deep Convolutional Neural Networks to Predict Length, Circumfrence and Weight from Mostly Dewatered Images of Fish. Open Science Framework.
  3. T. Danner, L. Li and J. Eickholt. (2018). SequenceViewer for Viewing DIDSON SequenceFiles. Open Science Framework.
  4. E. McCann, L. Li, K. Pangle, N. Johnson and J. Eickholt. (2018) An Underwater Observation Dataset of Fish. Open Science Framework.

Preprints

  1. J. Eickholt. (2018) Barriers to Active Learning for Computer Science Faculty. arXiv.org.

Poster Presentations

  1. J. Eickholt and C. Phillips. (2020) Towards Instructor-based Predictive Learning Analytics. The 10th International Conference on Learning Analytics and Knowledge (LAK20), Frankfurt am Main, Germany, 2020. [Poster].
  2. J. Eickholt and J. Cheng. (2012) Predicting Residue-Residue Contacts and Disordered Regions with Deep Networks. The Tenth Critical Assessment of Techniques for Protein Structure Prediction (CASP10), Geata, Italy, December, 2012.
  3. J. Eickholt and J. Cheng. (2012) Predicting Residue-Residue Contacts and Disordered Regions with Deep Networks and Boosting. 3Dsig at Intelligent Systems for Molecular Biology Conference, Long Beach, CA, USA, July 13-14, 2012.
  4. J. Eickholt and J. Cheng. (2012) Residue-Residue Contact and Disorder Prediction with Deep Networks & Gene-Gene Proximity Study. National Library of Medicine Informatics Training Conference, Madison, WA, USA, June 26-27, 2012.
  5. J. Eickholt, X. Deng and J. Cheng. (2011) DoBo: Domain Boundary Prediction by Integrating Evolutionary Signals and Machine Learning. 3Dsig and Intelligent Systems for Molecular Biology Conference, Austria, Vienna, July 15-19, 2011.
  6. J. Eickholt, Z. Wang and J. Cheng. (2010) A Conformation Ensemble Approach to Protein Contact Map Prediction. The Ninth Critical Assessment of Techniques for Protein Structure Prediction (CASP9), Pacific Grove, California, USA, December 5-9, 2010.
  7. J. Eickholt, T. Danilova, J. Birchler, J. Cheng. (2009) ChromoProbe: A Pipeline to Automatically Identify Unique Segments for Chromosome Probe Development. The Interdisciplinary Plant Group (IPG) Symposium, Columbia, MO, USA, May 29-31, 2009.