Computational Neuroscience, Nonlinear Systems,
Data Mining and Machine Learning
Mathematical and Computational Research
1. Middlemas E* and Knisley J. Soliton Solutions of a Variation of the Nonlinear Schrodinger Equation, to appear in the Springer Proceedings in Mathematics & Statistics series.
2. Zheng S, Chunming Z, and Knisley J. Moments of Matrix Variate Skew Elliptically Contoured Distributions. Advances and Applications in Statistics, to appear, 2013.
3. Zheng S, Chunming Z, and Knisley J. Stochastic Representations of the Matrix Variate Skew Elliptically Contoured Distributions. Advances and Applications in Statistics, vol. 33:2, 83 - 98, 2013.
4. Knisley D, Knisley J, and Herron A*. Graph-Theoretic Models of Mutations in the Nucleotide Binding Domain 1 of the Cystic Fibrosis Transmembrane Conductance Regulator. Computational Biology Journal, vol. 2013, Article ID 938169, 9 pages, 2013. doi:10.1155/2013/938169.
5. Network Properties of nested (t,r)-regular graphs, Knisley D. Brooks J and Knisley J, Journal of Combinatorial Mathematics and Computational Computing, (to appear).
6. Classifying multigraph models of secondary RNA structure using graph-theoretic descriptors, Knisley D, Knisley J, Rockney A* and Ross C*, ISRN Bioinformatics vol 12(2012) Article ID 157137, doi:10.402/2012/157135
7. Knisley, D., Knisley, J., Predicting Protein-Protein Interactions using Graph Invariants and a Neural Network. Computational Biology and Chemistry (2011), doi:10.1016/j.compbiolchem.2011.03.003
8. D. Koessler*, D. Knisley, J. Knisley, and T. Haynes. A predictive model for secondary RNA structure using graph theory and a neural network. BMC Bioinformatics 2010, 11(Suppl 6):S21 (7 Oct 2010)
9. D. Knisley, J. Knisley, and T. Haynes. Using a Neural Network to Identify Secondary RNA Structures Quantified by Graphical Invariants, Communications in Mathematical and Computer Chemistry, match: vol 60 (2) pp. 277-290, 2008
10. D. Knisley, J. Knisley and L. Roberts*, Biomolecular Invariants of Amino Acid Trees, Proceedings of the 2008 International Conference on Bioinformatics, Computational Biology, Genomics and Cheminformatics, Eds. Doble, Loging, Malone, Tseng, ISBN 978-1-60651-002-5, 2008.
11. D. Knisley, J. Knisley, and D. Williams*. Network Properties of (t,r)-regular graphs, Proceedings of the 2008 International Conference on Theoretical and Mathematical Foundations of Computer Science, Eds. Zoran Majkic, Michael Sipser, R. Radha, Daming Wei, ISBN: 978-1-60651-006-3, 2008.
12. L. L. Glenn and J. Knisley, Eigenslope Method for Second-Order Parabolic Partial Differential Equations and the Special Case of Cylindrical Cellular Structures with Spatial Gradients in Membrane Capacitance, Quantitative Medical Data Analysis Using Math Tools and Statistical Techniques, Don Hong and Yu Shyr, Editors, World Scientific, 2007.
13. D. Knisley and J. Knisley, Graph-theoretic Models in Chemistry and Molecular Biology, Handbook of Applied Algorithms: Solving Scientific, Engineering and Practical Problems, Ivan Stojmenovic and Amiya Nayak Eds. John Wiley & Sons. (2007) (by invitation).
14. J. Knisley, L. L. Glenn, K. Joplin, and P. Carey*, “Artificial Neural Networks for Data Mining and Feature Extraction,” Quantitative Medical Data Analysis Using Math Tools and Statistical Techniques, Don Hong and Yu Shyr, Editors, World Scientific, 2007.
15. D. Knisley and J. Knisley, Graphical Invariants and Molecular Descriptors for Secondary RNA Structures, Proceedings of the IASTED International Conference on Computational and Systems Biology, 2006.
16. L. Glenn and J. Knisley, Solutions for Transients in Arbitrarily Branching and Tapering Cables, Modeling in the Neurosciences: From Biological Systems to Neuromimetic Robotics, ed. Lindsay, R., R. Poznanski, G.N.Reeke, J.R. Rosenberg, and O.Sporns, CRC Press, London, 2004.
17. L. Glenn and J. Knisley, Voltage Transients in Multipolar Neurons with Tapering Dendrites, Modeling in the Neurosciences: From Ionic Channels to Neural Networks, ed. R. Poznanski, Harwood Academic Publishers, 1999.
18. L. Glenn and J. Knisley, Use of Eigenslopes to Estimate Fourier Coefficients for Passive Cable Models of the Neuron, Neuroscience Research Communications, 21:4, 1998.
19. J. Knisley and L. Glenn, A linear method for the curve fitting of multiexponentials, Journal of Neuroscience methods, 67 (1996) 166-174.
20. J. Knisley, On the Analytic Model of a Class of Semi-hyponormal Operators, Integral Equations and Operator Theory, 13 (1990) 576-592.
* signifies a student author
Mathematics and Science Education:
1. Jeff Knisley, Istvan Karsai and Thomas Schmickl. Compartmental models of migratory dynamics. Math. Model. Nat Phenom, 2011, 6(6):245-259.
2. J. Knisley and E. Behravesh. Developing student collaborations across disciplines, distances, and institutions. CBE Life Sci Educ. 2010 Fall; 9(3):364-9.
3. Istvan Karsai and Jeff Knisley, The Role of Institutes in Interdisciplinary Research and Education: An Example from Quantitative Biology, Journal of College Science Teaching, 37(3), pp 32 -36, Jan/Feb 2010.
4. Seier, E. , Price, R.M. and Knisley, J. (2005), “The Stat-Cave and a Multi-task Approach to Improve the Teaching of Introductory Statistics”, 2005 Proceedings of the American Statistical Association, Statistical Education Section.
5. J. Knisley, “A 4-Stage Model of Mathematical Learning,” The Mathematics Educator, Volume 12, Number 1, Spring, 2002, 11-16.
6. L. Kerley and J. Knisley, Using Data to Motivate the Models Used in Introductory Mathematics Courses, Primus, XI(2), June 2001, 111-123.
7. J. Knisley, Calculus: A Modern Perspective, The MAA Monthly, 104:8 (October, 1997) 724-727.
8. J. Knisley, Inverse Problems and their Algorithmic Solutions, The Journal of Computing in Small Colleges, 12 (1997) 160-165.
9. L. Kerley and J. Knisley, Illustrating Convolution with PC-MATLAB, Collegiate Microcomputer, 10(2) (1992), 71-81.
10. L. Kerley and J. Knisley, Complex Vectors and Image Identification, College Mathematics Journal, 24(2) (1993) 166-174.