Reading material

1. Some biology background

• Lecture 1 of CS5238 ps pdf
• Chapter 1 of "Introduction to Computational Molecular Biology" by Setubal/Meidanis

2. How to do experiment and feature generation

• Section 1 of Chapter 3 in The Practical Bioinformatician
• John A. Swets, "Measuring the accuracy of diagnostic systems", Science 240:1285--1293, June 1988.
• Trevor Hastie, Robert Tibshirani, Jerome Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Predicition, Springer, 2001. Chapter 1, 7
• Lance D. Miller et al., "Optimal gene expression analysis by microarrays", Cancer Cell, 2:353--361, November 2002.

3. Data mining techniques

• Sections 2 and 3 of Chapter 3 in The Practical Bioinformatician
• R. Agrawal, R. Srikant, Fast Algorithms for Mining Association Rules, Proc. of the 20th Int'l Conference on Very Large Databases, Santiago, Chile, Sept. 1994.

4. DNA Feature Recognition

• Chapters 4 and 7 in The Practical Bioinformatician
• A. G. Pedersen, H. Nielsen, "Neural network prediction of translation initiation sites in eukaryotes", ISMB 5:226--233, 1997
• L. Wong et al., " Using feature generation and feature selection for accurate prediction of translation initiation sites", GIW 13:192--200, 2002.
• A. Zien et al., "Engineering support vector machine kernels that recognize translation initiation sites", Bioinformatics 16:799--807, 2000.
• A. G. Hatzigeorgiou, "Translation initiation start prediction in human cDNAs with high accuracy", Bioinformatics 18:343--350, 2002
• V.B.Bajic et al., "Computer model for recognition of functional transcription start sites in RNA polymerase II promoters of vertebrates", J. Mol. Graph. & Mod., 2003, in press.
• J.W.Fickett, A.G.Hatzigeorgiou, "Eukaryotic promoter recognition", Gen. Res. 7:861--878, 1997.
• A.G.Pedersen et al., "The biology of eukaryotic promoter prediction---a review", Computer & Chemistry 23:191--207, 1999.
• M.Scherf et al., "Highly specific localisation of promoter regions in large genome sequences by PromoterInspector", JMB 297:599--606, 2000.
• M. A. Hall, "Correlation-based feature selection machine learning", PhD thesis, Dept of Comp. Sci., Univ. of Waikato, New Zealand, 1998.
• U. M. Fayyad, K. B. Irani, "Multi-interval discretization of continuous-valued attributes", IJCAI 13:1022-1027, 1993.
• H. Liu, R. Sentiono, "Chi2: Feature selection and discretization of numeric attributes", IEEE Intl. Conf. Tools with Artificial Intelligence 7:338--391, 1995.
• C. P. Joshi et al., "Context sequences of translation initiation codon in plants", PMB 35:993--1001, 1997.
• D. J. States, W. Gish, "Combined use of sequence similarity and codon bias for coding region identification", JCB 1:39--50, 1994.
• G. D. Stormo et al., "Use of Perceptron algorithm to distinguish translational initiation sites in E. coli", NAR 10:2997--3011, 1982.
• J. E. Tabaska, M. Q. Zhang, "Detection of polyadenylation signals in human DNA sequences", Gene 231:77--86, 1999.

5. Sequence Homology Interpretation

• Chapters 10 and 19 in The Practical Bioinformatician

  Function Assignment

• S.E.Brenner. "Errors in genome annotation", TIG, 15:132--133, 1999.
• T.F.Smith & X.Zhang. The challenges of genome sequence annotation or `The devil is in the details'", Nature Biotech, 15:1222--1223, 1997.
• D. Devos & A.Valencia. "Intrinsic errors in genome annotation", TIG, 17:429--431, 2001.
• K.L.Lim et al. "Interconversion of kinetic identities of the tandem catalytic domains of receptor-like protein tyrosine phosphatase PTP-alpha by two point mutations is synergist and substrate dependent", JBC, 273:28986--28993, 1998.

  Alignment Applications

• J. Park et al. "Sequence comparisons using multiple sequences detect three times as many remote homologs as pairwise methods", JMB, 284(4):1201-1210, 1998
• J. Park et al. "Intermediate sequences increase the detection of homology between sequences", JMB, 273:349--354, 1997
• Z. Zhang et al. "Protein sequence similarity searches using patterns as seeds", NAR, 26(17):3986--3990, 1996
• M.S.Gelfand et al. "Gene recognition via spliced sequence alignment", PNAS, 93:9061--9066, 1996
• S.F.Altschul et al. "Gapped BLAST and PSI-BLAST: A new generation of protein database search programs", NAR, 25(17):3389--3402, 1997.

  Phylogeny Analysis

• B. Sykes. "The seven daughters of Eve", Gorgi Books, 2002

6. Microarray Analysis

• Chapters 14 in The Practical Bioinformatician
• E.-J. Yeoh et al., "Classification, subtype discovery, and prediction of outcome in pediatric acute lymphoblastic leukemia by gene expression profiling", Cancer Cell, 1:133--143, 2002
• E.F. Petricoin et al., "Use of proteomic patterns in serum to identify ovarian cancer", Lancet, 359:572--577, 2002
• U.Alon et al., "Broad patterns of gene expression revealed by clustering analysis of tumor colon tissues probed by oligonucleotide arrays", PNAS 96:6745--6750, 1999
• J.Li, L. Wong, "Geography of differences between two classes of data", Proc. 6th European Conf. on Principles of Data Mining and Knowledge Discovery, pp. 325--337, 2002
• J.Li, L. Wong, "Identifying good diagnostic genes or gene groups from gene expression data by using the concept of emerging patterns", Bioinformatics, 18:725--734, 2002
• J.Li et al., "A comparative study on feature selection and classification methods using a large set of gene expression profiles", GIW, 13:51--60, 2002
• M. A. Hall, "Correlation-based feature selection machine learning", PhD thesis, Dept of Comp. Sci., Univ. of Waikato, New Zealand, 1998
• U. M. Fayyad, K. B. Irani, "Multi-interval discretization of continuous-valued attributes", IJCAI 13:1022-1027, 1993
• H. Liu, R. Sentiono, "Chi2: Feature selection and discretization of numeric attributes", IEEE Intl. Conf. Tools with Artificial Intelligence 7:338--391, 1995
• L.D. Miller et al., "Optimal gene expression analysis by microarrays", Cancer Cell 2:353--361, 2002

7. Machine Learning

• ISMB99 Tutorial Material --- Making the most of your hidden Marakov models
• HMM Project on Gene Finding in Human DNA
• Graphical Models --- This is an introduction to Bayesian Network

8. Microarray and Proteomic data analysis

• General FAQ 25: Handling missing or incomplete data

9. Sequence Similarity

• Lecture 2 of CS5238

10. Suffix Tree and its applications

• Lecture 4 of CS5238
• Suffix Trees in Computational Biology

11. Physical Mapping and Genome Sequencing

• Chapters 4 and 5 of "Introduction to Computational Molecular Biology" (by Setubal and Meidanis)
• Gene Myers, Whole-Genome DNA Sequencing, IEEE Computational Engineering and Science 3(1):33-43, 1999.
• Sequencing the Human Genome
• Ting Chen and S. Skiena, Trie-Based Data Structures for Sequence Assembly (Extended Abstract) CPM, 1997.

12. Structure Prediction and Comparison

• Please read the chapters "RNA secondary structure prediction" and "RNA secondary structure comparison" on CS5238
• Jeong-Mi Yoon, Yash Gad, Zhijun Wu, Mathematic Modeling of Protein Structure Using Distance Geometry
• Tao Jiang, Guo-Hui Lin, Bin Ma, and Kaizhong Zhang, The Longest Common Subsequence Problem for Arc-Annotated Sequences. CPM, 2000.

13. Phylogeny Tree

• Please read the three chapters related to phylogeny tree on CS5238