Reading material for EE150a, Genomic Signal and Information Processing.

    Lectures:

  1. Introduction and Overview of the Seminar Topics

    Suggested reading:

    Lecture slides:

  2. On Genomic Signal Processing

    Suggested reading:

    Lecture slides:

    You might want to check the extensive list of references at the end of the lecture slides.

  3. Introduction to Microarray Technologies: Models and Estimation Techniques


    Lecture slides:


    Student presentations:

  4. Sequence Alignment and Gene Finding

    Background reading:


    Papers for presentation:

    1. Jun Liu and T. Logvinenko, "Bayesian Methods in Biological Sequence Analysis," in Handbook of Statistical Genetics, 2nd Ed., D.J. Balding, M. Bishop and C. Cannings (eds), J. Wiley & Sons, 2003.

    Additional reading:


  5. Microarray Technologies I: Design Issues

    Background reading:


    Papers for presentation:

    1. A. Ben-Dor, R. Karp, B. Schwikowski, and Z. Yakhini, "Universal DNA tag systems: a combinatorial design scheme", Proceedings of the fourth annual international conference on Computational Molecular Biology, Tokyo, 2000. [Also in: Journal of Computational Biology, August 2000, Vol. 7, No. 3-4, Pages 503-519.]

    Additional reading:


  6. Microarray Technologies II: Intepreting the Data

    Background reading:


    Papers for presentation*:

    1. A. Tanay et. al., ``Discovering statistically significant biclusters in gene expression data,'' Bioinformatics, 18, suppl. 1, pp. S136-S144, 2002.
    2. Y. Cheng and G. M. Church, ``Biclustering of Expression Data,'' ISMB 2000: 93-103.
    3. X. Zhou et. al., ``Gene clustering based on clusterwide mutual information,'' J. on Computational Biology, 11(1), pp. 147-161, 2004.

    *Should papers 2. or 3. be chosen for presentation, a brief overview of the background reading paper by M. B. Eisen et. al. listed above should also be given.

  7. Transcriptional Regulation and Co-Regulated Genes

    Papers for presentation:

    1. Y. Moreau et. al., "Functional Bioinformatics of Microarray Data: From Expression to Regulation," Proceedings of the IEEE, 90(11), November 2002, pp: 1722-1743.
      Supplementary material: J. Liu, "The collapsed Gibbs sampler with applications to a gene regulation problem," in J. Amer. Statist. Assoc., 89 958-966, 1994.

    2. E. Segal and R. Sharan, "A Discriminative Model for Identifying Spatialcis-Regulatory Modules," in Proc. 8th Inter. Conf. on Research in Computational Molecular Biology (RECOMB), San-Diego, CA, April 2004.

  8. Genetic Regulatory Networks

    Background reading:


    Papers for presentation:

    1. H. De Jong, ``Modeling and Simulation of Genetic Regulatory Systems: A Literature Review,'' J. of Comp. Biology, 9(1), 2002, pp. 67-103.
    2. P. Smolen, D. A. Baxter, and J. H. Byrne, ``Modeling Transcriptional Control in Gene Networks -- Methods, Recent Results, and Future Directions,'' Bull. of Mathematical Biology, 62, 2000, pp. 247-292.
    3. X. Zhou et. al., ``Construction of genomic networks using mutual-information clustering and reversible jump Markov-chain Monte Carlo predictor design,'' Signal Processing, 83, pp. 745-761, 2003.
    4. I. Shmulevich et. al., "Probabilistic Boolean Networks: a rule-based uncertainty model for gene regulatory networks," Bioinformatics, 18(2), 2002, pp. 261-74.

  9. Joint Learning from Multiple Types of Genomic Data

    Papers for presentation:

    1. G. R. G. Lanckriet, M. Deng, N. Cristianini, M. I. Jordan, and W. S. Noble, "A statistical framework for genomic data fusion," Bioinformatics, 2004.

      Supplementary material:
      G. R. G. Lanckriet, N. Cristianini, M. I. Jordan, and W. S. Noble, "Kernel-based Integration of Genomic Data using Semidefinite Programming," in B. Schoelkopf, K. Tsuda and J.-P. Vert (Eds.), Kernel Methods in Computational Biology MIT Press, 2003.

  10. Protein Folding



Other interesting papers:
Useful repositories: For absolute beginners:

Maintained by Haris Vikalo.