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BMC bioinformatics special issues with contributions from the workshops on "Machine Learning in Computational Biology"

The 2010 Issue

A BMC Bioinformatics special issue has been published based on contributions to the workshops in 2008 and 2009.
The list of accepted papers is as follows.

  • Efficient motif finding algorithms for large-alphabet inputs (Pavel P Kuksa, Vladimir Pavlovic)
  • Graphical models for inferring single molecule dynamics (Jonathan E Bronson, Jake M Hofman, Jingyi Fei, Ruben L Gonzalez Jr., Chris H Wiggins)
  • A machine learning pipeline for quantitative phenotype prediction from genotype data (Giorgio Guzzetta, Giuseppe Jurman, Cesare Furlanello)
  • Classifying and scoring of molecules with the NGN: new datasets, significance tests, and generalization (Eddie YT Ma, Christopher JF Cameron, Stefan C Kremer)
  • Inferring latent task structure for Multitask Learning by Multiple Kernel Learning (Christian Widmer, Nora C Toussaint, Yasemin Altun, Gunnar Rätsch)
  • Semi-supervised prediction of protein subcellular localization using abstraction augmented Markov models (Cornelia Caragea, Doina Caragea, Adrian Silvescu, Vasant Honavar)
  • Exploiting physico-chemical properties in string kernels (Nora C Toussaint, Christian Widmer, Oliver Kohlbacher, Gunnar Rätsch)
  • Infinite mixture-of-experts model for sparse survival regression with application to breast cancer (Sudhir Raman, Thomas J Fuchs, Peter J Wild, Edgar Dahl, Joachim M Buhmann, Volker Roth)

The 2007 Issue

A BMC Bioinformatics special issue has been published based on contributions to the workshops in 2006 and 2007. The list of accepted papers is as follows.

    • Prediction of tissue-specific cis-regulatory modules using Bayesian networks and regression trees (Xiaoyu Chen, Mathieu Blanchette)
    • Semi-supervised learning for the identification of syn-expressed genes from fused microarray and in situ image data (Ivan G. Costa, Roland Krause, Lennart Opitz, Alexander Schliep)
    • Time-Series Alignment by Non-Negative Multiple Generalized Canonical Correlation Analysis (Bernd Fischer, Volker Roth, Joachim M. Buhmann)
    • Analyzing in situ Gene Expression in the Mouse Brain with Image Registration, Feature Extraction and Block Clustering (Manjunatha Jagalur, Chris Pal, Erik Learned-Miller, R. T. Zoeller, David Kulp)
    • A mixture of feature experts approach for protein-protein interaction prediction (Yanjun Qi, Judith Klein-Seetharaman,Ziv Bar-Joseph)
    • Accurate Splice Site Prediction Using Support Vector Machines (Soeren Sonnenburg, Gabriele Schweikert, Petra Philips, Jonas Behr, Gunnar Raetsch)
    • A new pairwise kernel for biological network inference with support vector machines (Jean-Philippe Vert, Jian Qiu, William S Noble)

The 2006 Issue

The following contributions to the 2004 workshop have been published in a special issue of BMC bioinformatics.
The full papers can be accessed online here

  • The Secrets of a Functional Synapse - From a Computational and Experimental Viewpoint (Michal Linial)
  • The Cluster Variation Method of Efficient Linkage Analysis on Extended Pedigrees (C.A. Albers, M.A.R. Leisink and H.J. Kappen)
  • Choosing negative examples for the prediction of protein-protein interactions (A. Ben-Hur and W.S. Noble)
  • PepDist: A New Framework for Protein-Peptide Binding Prediction based on Learning Peptide Distance Functions (T. Hertz and C. Yanover)
  • Network-based de-noising improves prediction from microarray data (T. Kato, Y. Murata, K. Miura, K. Asai, P.B. Horton, K. Tsuda and W. Fujibuchi)
  • A classification-based framework for predicting and analyzing gene regulatory response (A. Kundaje, M. Middendorf, M. Shah, C. Wiggins, Y. Freund and Christina Leslie)
  • Regulatory Networks in a Mammalian Cellular Context (A. Margolin, I. Nemenman, K. Basso, C. Wiggins, G. Stolovitzky, R.D. Favera, A. Califano)
  • Discrete profile comparison using information bottleneck (S. O’Rourke, G. Chechik, R. Friedman and E. Eskin)
  • Learning Interpretable SVMs for Biological Sequence Classification (G. Rätsch, S. Sonnenburg and C. Schäfer)
  • Protein Ranking by Semi-Supervised Network Propagation (J. Weston, R. Kuang, C. Leslie and W.S. Noble)
  • A Regression-based K nearest neighbor algorithm for gene function prediction from heterogeneous data (Z. Yao and W.L. Ruzzo)

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