Bioinformatics / Computational Biology Publications of Chris Ding



  1. Transitive Closure and Metric Inequality of Weighted Graphs --- Detecting Protein Interaction Modules Using Cliques (PDF).
    Chris Ding, Xiaofeng He, Hui Xiong, Hanchuan Peng, Stephen R. Holbrook
    Int'l J. Data Mining and Bioinformatics, Vol. 1, No. 2, pp. 162 - 177, 2006.

  2. PSoL: A Positive Sample Only Learning Algorithm for Finding Non-coding RNA Genes (PDF file)
    [Since the publication, the 1st, 3rd and 4th predicted RNAs in E. Coli (locations and structures shown in the Supplement) are confirmed to be "very likely" by experimentalists (near 6S RNA, IstR RNA, and Ig containing IRU17)]
    Chunlin Wang, Chris Ding, Richard Meraz, Stephen Holbrook.
    Bioinformatics. vol.22, pp:2590-2596, 2006.

  3. Feature selection based on mutual information: criteria of max-dependency, max-relevance, and min-redundancy (PDF)
    Hanchuan Peng, Fuhui Long, Chris Ding
    IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 27, No. 8, pp.1226-1238, 2005.

  4. Comparative mapping of sequence-based and structure-based protein domains
    Ya Zhang, John-Marc Chandonia, Chris Ding and Stephen Holbrook.
    BMC Bioinformatics. 2005, 6:77. ( paper online )

  5. Minimum redundancy feature selection from microarray gene expression data.
    Chris Ding and Hanchuan Peng.
    Journal of Bioinformatics and Computational Biology, Vol. 3, No. 2, pp.185-205, 2005. ( pdf file )
    (Journal version of our CSB'03 paper with same title)

  6. A Multi-Level Approach to SCOP Fold Recognition. (PDF)
    Keith Marsolo, Srinivasan Parthasarathy, Chris Ding
    IEEE Int'l Symposium of Bioinformatics and Bioengineering (BIBE 2005), pp:57-64

  7. Identification of Functional Modules in Protein Complexes via Hyperclique Pattern Discovery,
    Hui Xiong, Xiaofeng He, Chris Ding, Ya Zhang, Vipin Kumar, Stephen R. Holbrook,
    Proc. of the Pacific Symposium on Biocomputing (PSB 2005), January 2005. ( pdf file)

  8. Positive Sample Only Learning (PSOL) for Predicting RNA Genes in E. coli
    (Predict functional RNAs by identify most likely negative examples from unlabeled set and discriminate against positive examples using SVM)
    Richard Meraz, Xiaofeng He, Chris Ding, Steve Holbrook
    Proc. IEEE Computer Society Bioinformatics Conference, pp.535-538, August 2004. Stanford, CA.
    (pdf file)

  9. Contraction Graphs for Representation and Analysis of RNA Secondary Structure.
    Chris Ding, Richard Meraz, Xiaofeng He, Steve Holbrook
    Proc. IEEE Computer Society Bioinformatics Conference, pp.716-717, August 2004. Stanford, CA.

  10. A Unified Representation for Multi-Protein Complex Data for Modeling Protein Interaction Networks.
    (A bipartite graph is used to represent protein - protein complex relationship, out of which protein-protein and complex-complex interactions arise naturally. MinMaxCut clustering produces meaningful protein modules and supercomplexes.)
    Chris Ding, Xiaofeng He, Richard Meraz, Steve Holbrook
    Proteins: Structure, Function, and Bioinformatics, 57:99-108, June 2004.
    (pdf file)

  11. Minimum Redundancy Feature Selection for Gene Expression Data
    (Select Features that are minimally redundant among themselves while maximally relevant to class prediction)
    Chris Ding and Hanchuan Peng. Proc. IEEE Computer Society Bioinformatics Conference (CSB 2003), pp.523-529, August 2003. Stanford, CA.
    (pdf file)

  12. Unsupervised feature selection via two-way ordering in gene expression analysis
    Chris Ding. Bioinformatics. v.19, pp.1259-1266, 2003.
    (Paper and supplimentary data)

  13. Structure Search and Stability Enhancement of Bayesian Networks
    Hanchuan Peng and Chris Ding
    (LBNL Tech Report 51006, October 2002.) Proc. IEEE Int'l Conf. Data Mining. pp.621-624, Melbourne, Florida, Nov 2003.
    (pdf file)

  14. Analysis of gene expression profiles: class discovery and leaf ordering.
    Chris Ding. Proc. Conf. Research in Comp.Mol.Bio (RECOMB 2002), pp.127-136. April 2002, Washington, DC.
    (PDF file)

  15. Multi-class Protein Fold Recognition Using Support Vector Machines and Neural Networks.
    (Applying SVM on protein 3D structure prediction)
    Chris Ding and Inna Dubchak. Bioinformatics, April 2001, Vol 17, No 4, pp.349-358.
    (Paper and data)

    Proteins and Bio-Molecules Structure Computation

  16. Atomic Level Simulations of on a Million Particles: The Cell Multipole Method for Coulomb and London Interactions
    H.Q. Ding, N. Karasawa and W.A. Goddard, J. of Chemical Physics, v.97, pp:4309-4315 (1992).

  17. The Reduced Cell Multipole Method for Coulomb Interactions in Periodic Systems with Million-Atom Unit Cells
    H.Q. Ding, N.Karasawa, W.A.Goddard, Chemical Physics Letters, v.196, p.6 (1992).

  18. Optimal Spline Cutoffs for Coulomb and van der Waals Interactions
    H.Q. Ding, N. Karasawa, W.A. Goddard, Chemical Physics letters, v.193, pp:197-201 (1992).

  19. Polymer Simulation on the Hypercube
    H.Q. Ding, Proceedings of the 3rd Conf. on Hypercube Concurrent Computers and Applications, Ed. G. Fox, 1988, pp.1044-1050.