Description of course project

The course project will allow the student to do some independent study in the area of Bioinformatics. The list of possible project topics, together with up 2-3 starting references is below. Please feel free to add papers to this list with my approval.

The project work will be assessed through:

The project assessment will be based on the following criteria: Unless you are working on a problem already with me, you should pick one area and read 2-3 papers in that area. Your presentation and report should be based on these papers.

Papers

[These are all available electronically].
  1. DNA and machine learning. Robert (Alzheiumer detection), Saad (Cancer detection)
    1. Machine learning and genome annotation: a match meant to be? Kevin Y Yip, Chao Cheng and Mark Gerstein, link
    2. Unsupervised pattern discovery in human chromatin structure through genomic segmentation. Hoffman MM, Buske OJ, Wang J, Weng Z, Bilmes JA, Noble WS. link.
    3. Machine Learning Concepts and Tools for Statistical Genomics, link.
  2. DNA and information retrieval.
    1. A Term Association Approach for Genomics Information Retrieval link
    2. Information Retrieval meets Gene Analysis link.
    3. Omic Data Modelling for Information Retrieval link.
  3. Bayesian networks and its applications in Bioinformatics. Jessica
    1. A Primer on Learning in Bayesian Networks for Computational Biology link
    2. A Bayesian network model for protein fold and remote homologue recognition, link
    3. BioBayesNet: a web server for feature extraction and Bayesian network modeling of biological sequence data link
  4. RNA, DNA and Protein in Computer Science
    1. Quake: quality-aware detection and correction of sequencing errors David R Kelley, Michael C Schatz, Steven L Salzberg link
    2. DECOD: fast and accurate discriminative DNA motif finding, Huggins P, Zhong S, Shiff I, Beckerman R, Laptenko, Prives , Schulz MH, Simon I, Bar-Joseph Z, link.
    3. Probabilistic error correction for RNA sequencing, link.
  5. String Algorithms; string similarities and matching. Rafay
    1. Fast Algorithms for Top-k Approximate String Matching, link.
    2. A Fast Algorithm for Approximate String Matching on Gene Sequences, link.
  6. Others
    1. A beginner's guide to eukaryotic genome annotation, Mark Yandell, Daniel Encelink.
    2. Information retrieval from biological databases, Andreas D. Baxevanis,link.
    3. Bayesian methods in bioinformatics and computational systems biology, link.
  7. Clustering Algorithms: Dong
  8. Repeat detection : Jun Lin
  9. Cancer and Systems Biology: Jonathan