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Bart De Moor: [Publications] [Author Rank by year] [Co-authors] [Prefers] [Cites] [Cited by]

Publications of Author

  1. Peter Antal, Geert Fannes, Bart De Moor, Joos Vandewalle, Yves Moreau, Dirk Timmerman
    Extended Bayesian Regression Models: A Symbiotic Application of Belief Networks and Multilayer Perceptrons for the Classification of Ovarian Tumors. [Citation Graph (0, 0)][DBLP]
    AIME, 2001, pp:177-187 [Conf]
  2. Stein Aerts, Peter Antal, Dirk Timmerman, Bart De Moor, Yves Moreau
    Web-based Data Collection for Uterine Adnexal Tumors: A Case Study. [Citation Graph (0, 0)][DBLP]
    CBMS, 2002, pp:282-287 [Conf]
  3. Peter Antal, Bart De Moor, Tamás Mészáros, Tadeusz P. Dobrowiecki
    Annotated Bayesian Networks: A Tool to Integrate Textual and Probabilistic Medical Knowledge. [Citation Graph (0, 0)][DBLP]
    CBMS, 2001, pp:177-182 [Conf]
  4. Peter Antal, Herman Verrelst, Dirk Timmerman, Sabine Van Huffel, Bart De Moor, Ignace Vergote
    Bayesian Networks in Ovarian Cancer Diagnosis: Potentials and Limitations. [Citation Graph (0, 0)][DBLP]
    CBMS, 2000, pp:103-108 [Conf]
  5. Nathalie Pochet, Frizo A. L. Janssens, Frank De Smet, Kathleen Marchal, Ignace Vergote, Johan A. K. Suykens, Bart De Moor
    M@CBETH: Optimizing Clinical Microarray Classification. [Citation Graph (0, 0)][DBLP]
    CSB Workshops, 2005, pp:89-90 [Conf]
  6. Stein Aerts, Peter Van Loo, Gert Thijs, Yves Moreau, Bart De Moor
    Computational detection of cis-regulatory modules. [Citation Graph (0, 0)][DBLP]
    ECCB, 2003, pp:5-14 [Conf]
  7. Qizheng Sheng, Yves Moreau, Bart De Moor
    Biclustering microarray data by Gibbs sampling. [Citation Graph (0, 0)][DBLP]
    ECCB, 2003, pp:196-205 [Conf]
  8. Tony Van Gestel, Johan A. K. Suykens, Bart De Moor, Joos Vandewalle
    Automatic relevance determination for Least Squares Support Vector Machines classifiers. [Citation Graph (0, 0)][DBLP]
    ESANN, 2001, pp:13-18 [Conf]
  9. Luc Hoegaerts, Johan A. K. Suykens, Joos Vandewalle, Bart De Moor
    Kernel PLS variants for regression. [Citation Graph (0, 0)][DBLP]
    ESANN, 2003, pp:200-208 [Conf]
  10. Kristiaan Pelckmans, Johan A. K. Suykens, Bart De Moor
    Sparse LS-SVMs using additive regularization with a penalized validation criterion. [Citation Graph (0, 0)][DBLP]
    ESANN, 2004, pp:435-440 [Conf]
  11. Bart De Schutter, Bart De Moor
    The Extended Linear Complementary Problem and the Modeling and Analysis of Hybrid Systems. [Citation Graph (0, 0)][DBLP]
    Hybrid Systems, 1997, pp:70-85 [Conf]
  12. Bart De Schutter, Bart De Moor
    Generalized Linear Complementary Problems and the Analysis of Continuously Variable Systems and Discrete Event Systems. [Citation Graph (0, 0)][DBLP]
    HART, 1997, pp:409-414 [Conf]
  13. Tony Van Gestel, Johan A. K. Suykens, Jos De Brabanter, Bart De Moor, Joos Vandewalle
    Kernel Canonical Correlation Analysis and Least Squares Support Vector Machines. [Citation Graph (0, 0)][DBLP]
    ICANN, 2001, pp:384-389 [Conf]
  14. Bart Hamers, Johan A. K. Suykens, Bart De Moor
    Compactly Supported RBF Kernels for Sparsifying the Gram Matrix in LS-SVM Regression Models. [Citation Graph (0, 0)][DBLP]
    ICANN, 2002, pp:720-726 [Conf]
  15. Kristiaan Pelckmans, Johan A. K. Suykens, Bart De Moor
    Componentwise Support Vector Machines for Structure Detection. [Citation Graph (0, 0)][DBLP]
    ICANN (2), 2005, pp:643-648 [Conf]
  16. Luc Hoegaerts, Johan A. K. Suykens, Joos Vandewalle, Bart De Moor
    A Comparison of Pruning Algorithms for Sparse Least Squares Support Vector Machines. [Citation Graph (0, 0)][DBLP]
    ICONIP, 2004, pp:1247-1253 [Conf]
  17. Kristiaan Pelckmans, Johan A. K. Suykens, Bart De Moor
    Morozov, Ivanov and Tikhonov Regularization Based LS-SVMs. [Citation Graph (0, 0)][DBLP]
    ICONIP, 2004, pp:1216-1222 [Conf]
  18. B. Vandermeulen, J. Duflou, Bart De Moor
    The Role of User Profiles in Vector-Based Information Retrieval. [Citation Graph (0, 0)][DBLP]
    IKE, 2003, pp:668-669 [Conf]
  19. Olivier Gevaert, Frank De Smet, Dirk Timmerman, Yves Moreau, Bart De Moor
    Predicting the prognosis of breast cancer by integrating clinical and microarray data with Bayesian networks. [Citation Graph (0, 0)][DBLP]
    ISMB (Supplement of Bioinformatics), 2006, pp:184-190 [Conf]
  20. Shi Yu, Steven Van Vooren, Bert Coessens, Bart De Moor
    Interpreting Gene Profiles from Biomedical Literature Mining with Self Organizing Maps. [Citation Graph (0, 0)][DBLP]
    ISNN (2), 2006, pp:635-641 [Conf]
  21. Marcelo Espinoza, Johan A. K. Suykens, Bart De Moor
    Load Forecasting Using Fixed-Size Least Squares Support Vector Machines. [Citation Graph (0, 0)][DBLP]
    IWANN, 2005, pp:1018-1026 [Conf]
  22. Bert Coessens, Stijn Christiaens, Ruben Verlinden, Yves Moreau, Robert Meersman, Bart De Moor
    Ontology Guided Data Integration for Computational Prioritization of Disease Genes. [Citation Graph (0, 0)][DBLP]
    OTM Workshops (1), 2006, pp:689-698 [Conf]
  23. Maja Hadzic, Bart De Moor, Yves Moreau, Arek Kasprzyk
    KSinBIT 2006 PC Co-chairs' Message. [Citation Graph (0, 0)][DBLP]
    OTM Workshops (1), 2006, pp:647- [Conf]
  24. Stijn Viaene, Bart Baesens, Tony Van Gestel, Johan A. K. Suykens, Dirk Van den Poel, Jan Vanthienen, Bart De Moor, Guido Dedene
    Knowledge Discovery Using Least Squares Support Vector Machine Classifiers: A Direct Marketing Case. [Citation Graph (0, 0)][DBLP]
    PKDD, 2000, pp:657-664 [Conf]
  25. Patrick Glenisson, Peter Antal, Janick Mathys, Yves Moreau, Bart De Moor
    Evaluation of the Vector Space Representation in Text-Based Gene Clustering. [Citation Graph (0, 0)][DBLP]
    Pacific Symposium on Biocomputing, 2003, pp:391-402 [Conf]
  26. Tijl De Bie, Patrick Monsieurs, Kristof Engelen, Bart De Moor, Nello Cristianini, Kathleen Marchal
    Discovering Transcriptional Modules from Motif, Chip-Chip and Microarray Data. [Citation Graph (0, 0)][DBLP]
    Pacific Symposium on Biocomputing, 2005, pp:- [Conf]
  27. Gert Thijs, Kathleen Marchal, Magali Lescot, Stephane Rombauts, Bart De Moor, Pierre Rouzé, Yves Moreau
    A Gibbs sampling method to detect over-represented motifs in the upstream regions of co-expressed genes. [Citation Graph (0, 0)][DBLP]
    RECOMB, 2001, pp:305-312 [Conf]
  28. Patrick Glenisson, Bert Coessens, Steven Van Vooren, Yves Moreau, Bart De Moor
    Text-Based Gene Profiling with Domain-Specific Views. [Citation Graph (0, 0)][DBLP]
    SWDB, 2003, pp:15-31 [Conf]
  29. Tijl De Bie, Johan A. K. Suykens, Bart De Moor
    Learning from General Label Constraints. [Citation Graph (0, 0)][DBLP]
    SSPR/SPR, 2004, pp:671-679 [Conf]
  30. Peter Antal, Geert Fannes, Dirk Timmerman, Yves Moreau, Bart De Moor
    Bayesian applications of belief networks and multilayer perceptrons for ovarian tumor classification with rejection. [Citation Graph (0, 0)][DBLP]
    Artificial Intelligence in Medicine, 2003, v:29, n:1-2, pp:39-60 [Journal]
  31. Peter Antal, Geert Fannes, Dirk Timmerman, Yves Moreau, Bart De Moor
    Using literature and data to learn Bayesian networks as clinical models of ovarian tumors. [Citation Graph (0, 0)][DBLP]
    Artificial Intelligence in Medicine, 2004, v:30, n:3, pp:257-281 [Journal]
  32. Stein Aerts, Peter Van Loo, Yves Moreau, Bart De Moor
    A genetic algorithm for the detection of new cis-regulatory modules in sets of coregulated genes. [Citation Graph (0, 0)][DBLP]
    Bioinformatics, 2004, v:20, n:12, pp:1974-1976 [Journal]
  33. Steffen Durinck, Joke Allemeersch, Vincent Carey, Yves Moreau, Bart De Moor
    Importing MAGE-ML format microarray data into BioConductor. [Citation Graph (0, 0)][DBLP]
    Bioinformatics, 2004, v:20, n:18, pp:3641-3642 [Journal]
  34. Steffen Durinck, Yves Moreau, Arek Kasprzyk, Sean Davis, Bart De Moor, Alvis Brazma, Wolfgang Huber
    BioMart and Bioconductor: a powerful link between biological databases and microarray data analysis. [Citation Graph (0, 0)][DBLP]
    Bioinformatics, 2005, v:21, n:16, pp:3439-3440 [Journal]
  35. Kristof Engelen, Bert Coessens, Kathleen Marchal, Bart De Moor
    MARAN: Normalizing Micro-array Data. [Citation Graph (0, 0)][DBLP]
    Bioinformatics, 2003, v:19, n:7, pp:893-894 [Journal]
  36. Kristof Engelen, Bart Naudts, Bart De Moor, Kathleen Marchal
    A calibration method for estimating absolute expression levels from microarray data. [Citation Graph (0, 0)][DBLP]
    Bioinformatics, 2006, v:22, n:10, pp:1251-1258 [Journal]
  37. Nathalie Pochet, Frizo A. L. Janssens, Frank De Smet, Kathleen Marchal, Johan A. K. Suykens, Bart De Moor
    M@CBETH: a microarray classification benchmarking tool. [Citation Graph (0, 0)][DBLP]
    Bioinformatics, 2005, v:21, n:14, pp:3185-3186 [Journal]
  38. Nathalie Pochet, Frank De Smet, Johan A. K. Suykens, Bart De Moor
    Systematic benchmarking of microarray data classification: assessing the role of non-linearity and dimensionality reduction. [Citation Graph (0, 0)][DBLP]
    Bioinformatics, 2004, v:20, n:17, pp:3185-3195 [Journal]
  39. Frank De Smet, Janick Mathys, Kathleen Marchal, Gert Thijs, Bart De Moor, Yves Moreau
    Adaptive quality-based clustering of gene expression profiles. [Citation Graph (0, 0)][DBLP]
    Bioinformatics, 2002, v:18, n:5, pp:735-746 [Journal]
  40. Gert Thijs, Magali Lescot, Kathleen Marchal, Stephane Rombauts, Bart De Moor, Pierre Rouzé, Yves Moreau
    A higher-order background model improves the detection of promoter regulatory elements by Gibbs sampling. [Citation Graph (0, 0)][DBLP]
    Bioinformatics, 2001, v:17, n:12, pp:1113-1122 [Journal]
  41. Gert Thijs, Yves Moreau, Frank De Smet, Janick Mathys, Magali Lescot, Stephane Rombauts, Pierre Rouzé, Bart De Moor, Kathleen Marchal
    INCLUSive: INtegrated Clustering, Upstream sequence retrieval and motif Sampling. [Citation Graph (0, 0)][DBLP]
    Bioinformatics, 2002, v:18, n:2, pp:331-332 [Journal]
  42. Tim Van den Bulcke, Koen Van Leemput, Bart Naudts, Piet van Remortel, Hongwu Ma, Alain Verschoren, Bart De Moor, Kathleen Marchal
    SynTReN: a generator of synthetic gene expression data for design and analysis of structure learning algorithms. [Citation Graph (0, 0)][DBLP]
    BMC Bioinformatics, 2006, v:7, n:, pp:43- [Journal]
  43. Björn Menten, Filip Pattyn, Katleen De Preter, Piet Robbrecht, Evi Michels, Karen Buysse, Geert Mortier, Anne De Paepe, Steven Van Vooren, Joris Vermeesch, Yves Moreau, Bart De Moor, Stefan Vermeulen, Frank Speleman, Jo Vandesompele
    arrayCGHbase: an analysis platform for comparative genomic hybridization microarrays. [Citation Graph (0, 0)][DBLP]
    BMC Bioinformatics, 2005, v:6, n:, pp:124- [Journal]
  44. Pieter Monsieurs, Gert Thijs, Abeer A. Fadda, Sigrid C. J. De Keersmaecker, Jozef Vanderleyden, Bart De Moor, Kathleen Marchal
    More robust detection of motifs in coexpressed genes by using phylogenetic information. [Citation Graph (0, 0)][DBLP]
    BMC Bioinformatics, 2006, v:7, n:, pp:160- [Journal]
  45. Johan A. K. Suykens, Joos Vandewalle, Bart De Moor
    Intelligence and Cooperative Search by Coupled Local Minimizers [Citation Graph (0, 0)][DBLP]
    CoRR, 2002, v:0, n:, pp:- [Journal]
  46. Stijn Viaene, Bart Baesens, Tony Van Gestel, Johan A. K. Suykens, Dirk Van den Poel, Jan Vanthienen, Bart De Moor, Guido Dedene
    Knowledge discovery in a direct marketing case using least squares support vector machines. [Citation Graph (0, 0)][DBLP]
    Int. J. Intell. Syst., 2001, v:16, n:9, pp:1023-1036 [Journal]
  47. Michel Duhoux, Johan A. K. Suykens, Bart De Moor, Joos Vandewalle
    Improved Long-Term Temperature Prediction by Chaining of Neural Networks. [Citation Graph (0, 0)][DBLP]
    Int. J. Neural Syst., 2001, v:11, n:1, pp:1-10 [Journal]
  48. Luc Hoegaerts, Johan A. K. Suykens, Joos Vandewalle, Bart De Moor
    Subset based least squares subspace regression in RKHS. [Citation Graph (0, 0)][DBLP]
    Neurocomputing, 2005, v:63, n:, pp:293-323 [Journal]
  49. Kristiaan Pelckmans, Jos De Brabanter, Johan A. K. Suykens, Bart De Moor
    The differogram: Non-parametric noise variance estimation and its use for model selection. [Citation Graph (0, 0)][DBLP]
    Neurocomputing, 2005, v:69, n:1-3, pp:100-122 [Journal]
  50. Kristiaan Pelckmans, Johan A. K. Suykens, Bart De Moor
    Building sparse representations and structure determination on LS-SVM substrates. [Citation Graph (0, 0)][DBLP]
    Neurocomputing, 2005, v:64, n:, pp:137-159 [Journal]
  51. Patrick Glenisson, Wolfgang Glänzel, Frizo A. L. Janssens, Bart De Moor
    Combining full text and bibliometric information in mapping scientific disciplines. [Citation Graph (0, 0)][DBLP]
    Inf. Process. Manage., 2005, v:41, n:6, pp:1548-1572 [Journal]
  52. Frizo A. L. Janssens, Jacqueline Leta, Wolfgang Glänzel, Bart De Moor
    Towards mapping library and information science. [Citation Graph (0, 0)][DBLP]
    Inf. Process. Manage., 2006, v:42, n:6, pp:1614-1642 [Journal]
  53. Gert Thijs, Kathleen Marchal, Magali Lescot, Stephane Rombauts, Bart De Moor, Pierre Rouzé, Yves Moreau
    A Gibbs Sampling Method to Detect Overrepresented Motifs in the Upstream Regions of Coexpressed Genes. [Citation Graph (0, 0)][DBLP]
    Journal of Computational Biology, 2002, v:9, n:2, pp:447-464 [Journal]
  54. Tony Van Gestel, Johan A. K. Suykens, Bart Baesens, Stijn Viaene, Jan Vanthienen, Guido Dedene, Bart De Moor, Joos Vandewalle
    Benchmarking Least Squares Support Vector Machine Classifiers. [Citation Graph (0, 0)][DBLP]
    Machine Learning, 2004, v:54, n:1, pp:5-32 [Journal]
  55. Kristiaan Pelckmans, Johan A. K. Suykens, Bart De Moor
    Additive Regularization Trade-Off: Fusion of Training and Validation Levels in Kernel Methods. [Citation Graph (0, 0)][DBLP]
    Machine Learning, 2006, v:62, n:3, pp:217-252 [Journal]
  56. Bart De Moor, Lieven Vandenberghe, Joos Vandewalle
    The generalized linear complementarity problem and an algorithm to find all its solutions. [Citation Graph (0, 0)][DBLP]
    Math. Program., 1992, v:57, n:, pp:415-426 [Journal]
  57. Bart De Schutter, Bart De Moor
    The extended linear complementarity problem. [Citation Graph (0, 0)][DBLP]
    Math. Program., 1995, v:71, n:, pp:289-325 [Journal]
  58. Bert Coessens, Gert Thijs, Stein Aerts, Kathleen Marchal, Frank De Smet, Kristof Engelen, Patrick Glenisson, Yves Moreau, Janick Mathys, Bart De Moor
    INCLUSive: a web portal and service registry for microarray and regulatory sequence analysis. [Citation Graph (0, 0)][DBLP]
    Nucleic Acids Research, 2003, v:31, n:13, pp:3468-3470 [Journal]
  59. Stein Aerts, Peter Van Loo, Gert Thijs, Herbert Mayer, Rainer de Martin, Yves Moreau, Bart De Moor
    TOUCAN 2: the all-inclusive open source workbench for regulatory sequence analysis. [Citation Graph (0, 0)][DBLP]
    Nucleic Acids Research, 2005, v:33, n:Web-Server-Issue, pp:393-396 [Journal]
  60. Tony Van Gestel, Johan A. K. Suykens, Gert R. G. Lanckriet, Annemie Lambrechts, Bart De Moor, Joos Vandewalle
    Bayesian Framework for Least-Squares Support Vector Machine Classifiers, Gaussian Processes, and Kernel Fisher Discriminant Analysis. [Citation Graph (0, 0)][DBLP]
    Neural Computation, 2002, v:14, n:5, pp:1115-1147 [Journal]
  61. Kristiaan Pelckmans, Jos De Brabanter, Johan A. K. Suykens, Bart De Moor
    Handling missing values in support vector machine classifiers. [Citation Graph (0, 0)][DBLP]
    Neural Networks, 2005, v:18, n:5-6, pp:684-692 [Journal]
  62. Johan A. K. Suykens, Bart De Moor, Joos Vandewalle
    Static and dynamic stabilizing neural controllers, applicable to transition between equilibrium points. [Citation Graph (0, 0)][DBLP]
    Neural Networks, 1994, v:7, n:5, pp:819-831 [Journal]
  63. Johan A. K. Suykens, Bart De Moor, Joos Vandewalle
    NLq Theory: A Neural Control Framework with Global Asymptotic Stability Criteria. [Citation Graph (0, 0)][DBLP]
    Neural Networks, 1997, v:10, n:4, pp:615-637 [Journal]
  64. Johan A. K. Suykens, Joos Vandewalle, Bart De Moor
    Optimal control by least squares support vector machines. [Citation Graph (0, 0)][DBLP]
    Neural Networks, 2001, v:14, n:1, pp:23-35 [Journal]
  65. Luc Hoegaerts, Lieven De Lathauwer, Ivan Goethals, Johan A. K. Suykens, Joos Vandewalle, Bart De Moor
    Efficiently updating and tracking the dominant kernel principal components. [Citation Graph (0, 0)][DBLP]
    Neural Networks, 2007, v:20, n:2, pp:220-229 [Journal]
  66. Tony Van Gestel, Johan A. K. Suykens, Gert R. G. Lanckriet, Annemie Lambrechts, Bart De Moor, Joos Vandewalle
    Multiclass LS SVMs Moderated Outputs and Coding Decoding Schemes. [Citation Graph (0, 0)][DBLP]
    Neural Processing Letters, 2002, v:15, n:1, pp:45-58 [Journal]
  67. Kristiaan Pelckmans, Marcelo Espinoza, Jos De Brabanter, Johan A. K. Suykens, Bart De Moor
    Primal-Dual Monotone Kernel Regression. [Citation Graph (0, 0)][DBLP]
    Neural Processing Letters, 2005, v:22, n:2, pp:171-182 [Journal]
  68. Johan A. K. Suykens, Philippe Lemmerling, W. Favoreel, Bart De Moor, M. Crepel, P. Briol
    Modelling the Belgian Gas Consumption Using Neural Networks. [Citation Graph (0, 0)][DBLP]
    Neural Processing Letters, 1996, v:4, n:3, pp:157-166 [Journal]
  69. Patrick Glenisson, Janick Mathys, Bart De Moor
    Meta-clustering of gene expression data and literature-based information. [Citation Graph (0, 0)][DBLP]
    SIGKDD Explorations, 2003, v:5, n:2, pp:101-112 [Journal]
  70. Philippe Lemmerling, Leentje Vanhamme, Sabine Van Huffel, Bart De Moor
    IQML-like algorithms for solving structured total least squares problems: a unified view. [Citation Graph (0, 0)][DBLP]
    Signal Processing, 2001, v:81, n:9, pp:1935-1945 [Journal]
  71. Katrien De Cock, Bernard Hanzon, Bart De Moor
    On a cepstral norm for an ARMA model and the polar plot of the logarithm of its transfer function. [Citation Graph (0, 0)][DBLP]
    Signal Processing, 2003, v:83, n:2, pp:439-443 [Journal]
  72. Geert Ysebaert, Katleen Van Acker, Marc Moonen, Bart De Moor
    Constraints in channel shortening equalizer design for DMT-based systems. [Citation Graph (0, 0)][DBLP]
    Signal Processing, 2003, v:83, n:3, pp:641-648 [Journal]
  73. Arie Yeredor, Bart De Moor
    On homogeneous least-squares problems and the inconsistency introduced by mis-constraining. [Citation Graph (0, 0)][DBLP]
    Computational Statistics & Data Analysis, 2004, v:47, n:3, pp:455-465 [Journal]
  74. Kristof Op De Beeck, Irene Y. H. Gu, Liyuan Li, Mats Viberg, Bart De Moor
    Region-Based Statistical Background Modeling for Foreground Object Segmentation. [Citation Graph (0, 0)][DBLP]
    ICIP, 2006, pp:3317-3320 [Conf]
  75. Frizo A. L. Janssens, Wolfgang Glänzel, Bart De Moor
    Dynamic hybrid clustering of bioinformatics by incorporating text mining and citation analysis. [Citation Graph (0, 0)][DBLP]
    KDD, 2007, pp:360-369 [Conf]
  76. Kristiaan Pelckmans, Jos De Brabanter, Johan A. K. Suykens, Bart De Moor
    Support and Quantile Tubes [Citation Graph (0, 0)][DBLP]
    CoRR, 2007, v:0, n:, pp:- [Journal]
  77. Diederik Aerts, Marek Czachor, Bart De Moor
    On Geometric Algebra representation of Binary Spatter Codes [Citation Graph (0, 0)][DBLP]
    CoRR, 2006, v:0, n:, pp:- [Journal]
  78. Kristiaan Pelckmans, Ivan Goethals, Jos De Brabanter, Johan A. K. Suykens, Bart De Moor
    Componentwise Least Squares Support Vector Machines [Citation Graph (0, 0)][DBLP]
    CoRR, 2005, v:0, n:, pp:- [Journal]
  79. Diederik Aerts, Marek Czachor, Bart De Moor
    Geometric Analogue of Holographic Reduced Representation [Citation Graph (0, 0)][DBLP]
    CoRR, 2007, v:0, n:, pp:- [Journal]

  80. Comparison of vocabularies, representations and ranking algorithms for gene prioritization by text mining. [Citation Graph (, )][DBLP]


  81. Convex optimization for the design of learning machines. [Citation Graph (, )][DBLP]


  82. Robustness of Kernel Based Regression: A Comparison of Iterative Weighting Schemes. [Citation Graph (, )][DBLP]


  83. Identifying Customer Profiles in Power Load Time Series Using Spectral Clustering. [Citation Graph (, )][DBLP]


  84. Hybrid Clustering by Integrating Text and Citation Based Graphs in Journal Database Analysis. [Citation Graph (, )][DBLP]


  85. Variable selection by rank-one updates for least squares support vector machines. [Citation Graph (, )][DBLP]


  86. Hybrid Clustering of Multiple Information Sources via HOSVD. [Citation Graph (, )][DBLP]


  87. An empirical assessment of kernel type performance for least squares support vector machine classifiers. [Citation Graph (, )][DBLP]


  88. Classification of Sporadic and BRCA1 Ovarian Cancer Based on a Genome-Wide Study of Copy Number Variations. [Citation Graph (, )][DBLP]


  89. A Risk Minimization Principle for a Class of Parzen Estimators. [Citation Graph (, )][DBLP]


  90. Prospective Exploration of Biochemical Tissue Composition via Imaging Mass Spectrometry Guided by Principal Component Analysis. [Citation Graph (, )][DBLP]


  91. Integrating Microarray and Proteomics Data to Predict the Response of Cetuximab in Patients with Rectal Cancer. [Citation Graph (, )][DBLP]


  92. Integration of Microarray and Textual Data Improves the Prognosis Prediction of Breast, Lung, and Ovarian Cancer Patients. [Citation Graph (, )][DBLP]


  93. Discrete wavelet transform-based multivariate exploration of tissue via imaging mass spectrometry. [Citation Graph (, )][DBLP]


  94. Hybrid Clustering of Text Mining and Bibliometrics Applied to Journal Sets. [Citation Graph (, )][DBLP]


  95. Semi-supervised Learning of Sparse Linear Models in Mass Spectral Imaging. [Citation Graph (, )][DBLP]


  96. A nonlinear least squares estimation procedure without initial parameter guesses. [Citation Graph (, )][DBLP]


  97. Algorithm for reducing the number of constraints of POD-based predictive controllers. [Citation Graph (, )][DBLP]


  98. A structure exploiting interior-point method for moving horizon estimation. [Citation Graph (, )][DBLP]


  99. Robustness analysis for Least Squares kernel based regression: an optimization approach. [Citation Graph (, )][DBLP]


  100. A convex approximation for parameter estimation involving parameter-affine dynamic models. [Citation Graph (, )][DBLP]


  101. Exploring the Operational Characteristics of Inference Algorithms for Transcriptional Networks by Means of Synthetic Data. [Citation Graph (, )][DBLP]


  102. CALIB: a Bioconductor package for estimating absolute expression levels from two-color microarray data. [Citation Graph (, )][DBLP]


  103. Query-driven module discovery in microarray data. [Citation Graph (, )][DBLP]


  104. ViTraM: visualization of transcriptional modules. [Citation Graph (, )][DBLP]


  105. ModuleDigger: an itemset mining framework for the detection of cis-regulatory modules. [Citation Graph (, )][DBLP]


  106. An experimental loop design for the detection of constitutional chromosomal aberrations by array CGH. [Citation Graph (, )][DBLP]


  107. Gene prioritization and clustering by multi-view text mining. [Citation Graph (, )][DBLP]


  108. L2-norm multiple kernel learning and its application to biomedical data fusion. [Citation Graph (, )][DBLP]


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