Functional Annotation (via sequence analysis and beyond)

  1. A New Progressive-Iterative Algorithm for Multiple Structure Alignment
    Dmitry Lupyan et al
    Bioinformatics 2005, 21: 3255-3263.

  2. Pairwise alignment incorporating dipeptide covariation.
    Gavin E. Crooks et al
    Bioinformatics 2005, 21: 3704-3710.

  3. Improved pairwise alignments of proteins in the Twilight Zone using local structure predictions
    Yao-ming Huang and Christopher Bystroff
    Bioinformatics 2006 22: 413-422.

  4. Determining functional specificity from protein sequences
    Jason E. Donald and Eugene I. Shakhnovich
    Bioinformatics, Jun 2005; 21: 2629 - 2635.

  5. A Long Indel Model For Evolutionary Sequence Alignment
    I. Miklos, G. A. Lunter, and I. Holmes
    Mol. Biol. Evol. 21:529-540, 2004

  6. Application of latent semantic analysis to protein remote homology detection
    Qi-wen Dong, Xiao-long Wang, and Lei Lin
    Bioinformatics, 1 February 2006; 22: 285 - 290.

  7. Semi-supervised protein classification using cluster kernels
    Jason Weston, Christina Leslie, Eugene Ie, Dengyong Zhou, Andre Elisseeff, and William Stafford Noble
    Bioinformatics, Aug 2005; 21: 3241 - 3247.

  8. A comparison of scoring functions for protein sequence profile alignment
    Robert C. Edgar and Kimmen Sjolander
    Bioinformatics 2004

  9. Protein homology detection by HMM-HMM comparison
    Johannes Soding
    Bioinformatics, Apr 2005; 21: 951 - 960.

  10. Clustering proteins from interaction networks for the prediction of cellular functions
    Christine Brun et al
    BMC Bioinformatics 2004

  11. Profile-based direct kernels for remote homology detection and fold recognition
    Rangwala, H. and Karypis, G.
    Bioinformatics, (2005) 21, 4239-4247.

  12. Inferring functional information from domain co-evolution
    Yohan Kim, Mehmet Koyutrk, Umut Topkara, Ananth Grama, and Shankar Subramaniam
    Bioinformatics, 1 January 2006; 22: 40 - 49

    Identification of Structrual Features

  13. SSEP-Domain: Protein Domain Prediction by Alignment of Secondary Structure Elements and Profiles
    Jan E. Gewehr and Ralf Zimmer
    Bioinformatics 2005, 22: 181-187.

  14. Reliable prediction of transcription factor binding sites by phylogenetic verification
    Xiaoman Li et al
    PNAS 102:16945-16950, 2005.

  15. Gapped alignment of protein sequence motifs through Monte Carlo optimization of a hidden Markov model
    Andrew F Neuwald and Jun S Liu
    BMC Bioinformatics, 2004

  16. MicroRNA identification based on sequence and structure alignment
    Xiaowo Wang et al
    Bioinformatics, 2005, 21: 3610-3614.

  17. A simple and fast secondary structure prediction method using hidden neural networks
    Kuang Lin et al
    Bioinformatics, 2005, 21: 152-159.

  18. (JB) Exploiting conserved structure for faster annotation of non-coding RNAs without loss of accuracy
    Zasha Weinberg and Walter L. Ruzzo1
    Bioinformatics, 2004.

  19. Accurate identification of alternatively spliced exons using support vector machine
    Gideon Dror, Rotem Sorek, and Ron Shamir
    Bioinformatics, Apr 2005; 21: 897 - 901.

  20. Discrimination of outer membrane proteins using support vector machines
    Keun-Joon Park, M. Michael Gromiha, Paul Horton, and Makiko Suwa
    Bioinformatics, December 1, 2005; 21: 4223 - 4229

  21. A simple statistical method for discriminating outer membrane proteins with better accuracy
    M. Michael Gromiha and Makiko Suwa
    Bioinformatics, Apr 2005; 21: 961 - 968.

  22. Prediction of splice sites with dependency graphs and their expanded bayesian networks
    Te-Ming Chen, Chung-Chin Lu, and Wen-Hsiung Li
    Bioinformatics, Feb 2005; 21: 471 - 482.

  23. (Tapan) Improved prediction of protein-protein binding sites using a support vector machines approach
    James R. Bradford and David R. Westhead
    Bioinformatics, Apr 2005; 21: 1487 - 1494.

  24. Non-additivity in protein-DNA binding
    R. A. O'Flanagan, G. Paillard, R. Lavery, and A. M. Sengupta
    Bioinformatics, May 2005; 21: 2254 - 2263.

  25. Fold recognition by combining profile-profile alignment and support vector machine
    Sangjo Han, Byung-chul Lee, Seung Taek Yu, Chan-seok Jeong, Soyoung Lee, and Dongsup Kim
    Bioinformatics, Jun 2005; 21: 2667 - 2673.

    Inference of Biological Networks

  26. Combining biological networks to predict genetic interactions
    Sharyl L. Wong et al
    PNAS 101:15682-15687, 2004.

  27. Explore Biological Pathways from Noisy Array Data by Directed Acyclic Boolean Networks
    LEI M. LI and HENRY HORNG-SHING LU
    J. Comp. Bio. 12:170-185, 2005.

  28. Prediction of regulatory networks: genome-wide identification of transcription factor targets from gene expression data
    Jiang Qian et al
    Bioinformatics, 19:1917-1926, 2003.

  29. Uncovering transcriptional regulation of metabolism by using metabolic network topology
    Kiran Raosaheb Patil and Jens Nielsen
    PNAS 102:2685-2689, 2005.

  30. A Statistical Method for Constructing Transcriptional Regulatory Networks Using Gene Expression and Sequence Data
    BIAO XING and MARK J. VAN DER LAAN
    J. Mol. Bio. 12:229-246, 2005.

  31. Selective Integration of Multiple Biological Data for Supervised Network Inference
    Tsuyoshi Kato et al
    Bioinformatics, 2005, 21: 2488-2495.

  32. Highly conserved upstream sequences for transcription factor genes and implications for the regulatory network
    Hisakazu Iwama and Takashi Gojobori
    PNAS, 101:17156-17161, 2004.

  33. Developing optimal non-linear scoring function for protein design
    Changyu Hu, Xiang Li, and Jie Liang
    Bioinformatics, Nov 2004; 20: 3080 - 3098.

  34. Gene selection using support vector machines with non-convex penalty
    Hao Helen Zhang, Jeongyoun Ahn, Xiaodong Lin, and Cheolwoo Park
    Bioinformatics, 1 January 2006; 22: 88 - 95.

  35. A hidden Markov model for analyzing ChIP-chip experiments on genome tiling arrays and its application to p53 binding sequences
    Wei Li, Clifford A. Meyer, and X. Shirley Liu
    Bioinformatics, Jun 2005; 21: i274 - i282.

  36. Generating Boolean networks with a prescribed attractor structure
    Ranadip Pal, Ivan Ivanov, Aniruddha Datta, Michael L. Bittner, and Edward R. Dougherty
    Bioinformatics, Nov 2005; 21: 4021 - 4025.

  37. Prediction of protein-protein interactions using distant conservation of sequence patterns and structure relationships
    Jordi Espadaler, Oriol Romero-Isart, Richard M. Jackson, and Baldo Oliva
    Bioinformatics, Aug 2005; 21: 3360 - 3368.

  38. A stochastic differential equation model for quantifying transcriptional regulatory network in Saccharomyces cerevisiae
    Kuang-Chi Chen, Tse-Yi Wang, Huei-Hun Tseng, Chi-Ying F. Huang, and Cheng-Yan Kao
    Bioinformatics, Jun 2005; 21: 2883 - 2890.

  39. A hypothesis-based approach for identifying the binding specificity of regulatory proteins from chromatin immunoprecipitation data
    Kenzie D. MacIsaac, D. Benjamin Gordon, Lena Nekludova, Duncan T. Odom, Joerg Schreiber, David K. Gifford, Richard A. Young, and Ernest Fraenkel
    Bioinformatics, 15 February 2006; 22: 423 - 429.

  40. Inferring protein-protein interactions through high-throughput interaction data from diverse organisms
    Yin Liu, Nianjun Liu, and Hongyu Zhao
    Bioinformatics, Aug 2005; 21: 3279 - 3285.

  41. Comparison of computational methods for the identification of cell cycle-regulated genes
    Ulrik de Lichtenberg, Lars Juhl Jensen, Anders Fausbøll, Thomas S. Jensen, Peer Bork, and Søren Brunak
    Bioinformatics, Apr 2005; 21: 1164 - 1171.

  42. Iterative Cluster Analysis of Protein Interaction Data
    Vicente Arnau, Sergio Mars, and Ignacio Marn
    Bioinformatics, Feb 2005; 21: 364 - 378.

  43. Application of compression-based distance measures to protein sequence classification: a methodological study
    Andras Kocsor, et al
    Bioinformatics, 22, pp. 407-412, 2006.