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bobychan library [21 articles]

最近 bobychan さんのライブラリ .
  • Learning MHC I--peptide binding
    Bioinformatics, Vol. 22, No. 14. (15 July 2006), pp. e227-235.
    by Nebojsa Jojic, Manuel Reyes-Gomez, David Heckerman, Carl Kadie, Ora Schueler-Furman
    posted to immunogenicity by bobychan on 2008-05-22 22:16:09 as read along with 1 person operon
  • Predicting Proteolytic Sites in Extracellular Proteins: Only Halfway There.
    Bioinformatics (4 March 2008)
    by Yossef Kliger, Eyal Gofer, Assaf Wool, Amir Toporik, Avihay Apatoff, Moshe Olshansky
    posted to hormones by bobychan on 2008-05-12 23:31:39 as ** along with 1 person grahamc
  • The metric space of proteins-comparative study of clustering algorithms.
    Bioinformatics (Oxford, England), Vol. 18 Suppl 1 (2002)
    by O Sasson, N Linial, M Linial
    posted to sequence_clustering by bobychan on 2008-05-07 21:28:32 as **
  • Compression-based classification of biological sequences and structures via the Universal Similarity Metric: experimental assessment
    BMC Bioinformatics, Vol. 8 (13 July 2007), 252.
    by Paolo Ferragina, Raffaele Giancarlo, Valentina Greco, Giovanni Manzini, Gabriel Valiente
  • CLUSS: Clustering of protein sequences based on a new similarity measure
    BMC Bioinformatics, Vol. 8 (04 August 2007), 286.
    by Abdellali Kelil, Shengrui Wang, Ryszard Brzezinski, Alain Fleury
  • SEQOPTICS: a protein sequence clustering system
    BMC Bioinformatics, Vol. 7, No. Suppl 4. (2006)
    by Yonghui Chen, Kevin Reilly, Alan Sprague, Zhijie Guan
    posted to sequence_clustering by bobychan on 2008-05-07 19:13:24 as **
  • Methods for evaluating clustering algorithms for gene expression data using a reference set of functional classes
    BMC Bioinformatics, Vol. 7 (31 August 2006), 397.
    by Susmita Datta, Somnath Datta
  • Spectral clustering of protein sequences.
    Nucleic Acids Res, Vol. 34, No. 5. (2006), pp. 1571-1580.
    by A Paccanaro, JA Casbon, MA Saqi
    posted to sequence_clustering by bobychan on 2008-05-07 19:12:12 as ** along with 1 person knaseanka
  • Large scale clustering of protein sequences with FORCE -- A layout based heuristic for weighted cluster editing
    BMC Bioinformatics, Vol. 8, No. 1. (2007)
    by Tobias Wittkop, Jan Baumbach, Francisco Lobo, Sven Rahmann
    posted to sequence_clustering by bobychan on 2008-05-07 19:11:47 as ** along with 1 person nklee
  • Analysis of an optimal hidden Markov model for secondary structure prediction
    BMC Structural Biology, Vol. 6 (13 December 2006), 25.
    by Juliette Martin, Jean-Francois Gibrat, Francois Rodolphe
    posted to secondary_structure by bobychan on 2008-04-04 00:21:18 as read
  • Protein secondary structure prediction for a single-sequence using hidden semi-Markov models
    BMC Bioinformatics, Vol. 7, No. 1. (2006)
    by Zafer Aydin, Yucel Altunbasak, Mark Borodovsky
    posted to secondary_structure by bobychan on 2008-04-04 00:19:39 as read along with 1 person isakemalp
  • Secondary structure assignment that accurately reflects physical and evolutionary characteristics.
    BMC Bioinformatics, Vol. 6 Suppl 4 (1 December 2005)
    by MV Cubellis, F Cailliez, SC Lovell
  • Protein secondary structure assignment revisited: a detailed analysis of different assignment methods
    BMC Structural Biology, Vol. 5, No. 1. (2005)
    by Juliette Martin, Guillaume Letellier, Antoine Marin, Jean F Taly, Alexandre de Brevern, Jean F Gibrat
  • Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
    (08 June 2005)
    by Ian H Witten, Eibe Frank
  • Recognition of analogous and homologous protein folds: analysis of sequence and structure conservation.
    J Mol Biol, Vol. 269, No. 3. (13 June 1997), pp. 423-439.
    by RB Russell, MA Saqi, RA Sayle, PA Bates, MJ Sternberg
  • Understanding the relationship between the primary structure of proteins and its propensity to be soluble on overexpression in Escherichia coli.
    Protein Sci, Vol. 14, No. 3. (March 2005), pp. 582-592.
    posted to machine_learning protein_solubility by bobychan on 2008-03-10 16:42:05 as read
  • A support vector machine-based method for predicting the propensity of a protein to be soluble or to form inclusion body on overexpression in Escherichia coli.
    Bioinformatics, Vol. 22, No. 3. (1 February 2006), pp. 278-284.
    posted to machine_learning protein_solubility by bobychan on 2008-03-10 16:41:51 as read
  • Correlation between the structural stability and aggregation propensity of proteins.
    In Silico Biol, Vol. 7, No. 2. (2007), pp. 225-237.
    posted to machine_learning protein_solubility by bobychan on 2008-03-10 16:41:39 as read
  • Mining the structural genomics pipeline: identification of protein properties that affect high-throughput experimental analysis.
    J Mol Biol, Vol. 336, No. 1. (6 February 2004), pp. 115-130.
    by CS Goh, N Lan, SM Douglas, B Wu, N Echols, A Smith, D Milburn, GT Montelione, H Zhao, M Gerstein
    posted to machine_learning protein_solubility by bobychan on 2008-03-10 16:41:13 as read
  • Predicting experimental properties of proteins from sequence by machine learning techniques.
    Curr Protein Pept Sci, Vol. 8, No. 2. (April 2007), pp. 121-133.
    posted to machine_learning protein_solubility by bobychan on 2008-03-10 16:40:40 as read
  • Protein solubility: sequence based prediction and experimental verification.
    Bioinformatics (6 December 2006)
    by Pawel Smialowski, Antonio J J Martin-Galiano, Aleksandra Mikolajka, Tobias Girschick, Tad A A Holak, Dmitrij Frishman
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