Volume 7 Supplement 1

NIPS workshop on New Problems and Methods in Computational Biology


Edited by Gal Chechik, Christina Leslie, Gunnar Rätsch, Koji Tsuda

NIPS workshop on New Problems and Methods in Computational Biology.

Whistler, Canada

18 December 2004

  1. Proceedings

    Network-based de-noising improves prediction from microarray data

    Prediction of human cell response to anti-cancer drugs (compounds) from microarray data is a challenging problem, due to the noise properties of microarrays as well as the high variance of living cell response...

    Tsuyoshi Kato, Yukio Murata, Koh Miura, Kiyoshi Asai, Paul B Horton, Koji Tsuda and Wataru Fujibuchi

    BMC Bioinformatics 2006 7(Suppl 1):S4

    Published on: 20 March 2006

  2. Proceedings

    A classification-based framework for predicting and analyzing gene regulatory response

    We have recently introduced a predictive framework for studying gene transcriptional regulation in simpler organisms using a novel supervised learning algorithm called GeneClass. GeneClass is motivated by the ...

    Anshul Kundaje, Manuel Middendorf, Mihir Shah, Chris H Wiggins, Yoav Freund and Christina Leslie

    BMC Bioinformatics 2006 7(Suppl 1):S5

    Published on: 20 March 2006

  3. Proceedings

    ARACNE: An Algorithm for the Reconstruction of Gene Regulatory Networks in a Mammalian Cellular Context

    Elucidating gene regulatory networks is crucial for understanding normal cell physiology and complex pathologic phenotypes. Existing computational methods for the genome-wide "reverse engineering" of such netw...

    Adam A Margolin, Ilya Nemenman, Katia Basso, Chris Wiggins, Gustavo Stolovitzky, Riccardo Dalla Favera and Andrea Califano

    BMC Bioinformatics 2006 7(Suppl 1):S7

    Published on: 20 March 2006

  4. Proceedings

    Discrete profile comparison using information bottleneck

    Sequence homologs are an important source of information about proteins. Amino acid profiles, representing the position-specific mutation probabilities found in profiles, are a richer encoding of biological se...

    Sean O'Rourke, Gal Chechik, Robin Friedman and Eleazar Eskin

    BMC Bioinformatics 2006 7(Suppl 1):S8

    Published on: 20 March 2006

  5. Proceedings

    Learning Interpretable SVMs for Biological Sequence Classification

    Support Vector Machines (SVMs) – using a variety of string kernels – have been successfully applied to biological sequence classification problems. While SVMs achieve high classification accuracy they lack int...

    Gunnar Rätsch, Sören Sonnenburg and Christin Schäfer

    BMC Bioinformatics 2006 7(Suppl 1):S9

    Published on: 20 March 2006

  6. Proceedings

    Protein Ranking by Semi-Supervised Network Propagation

    Biologists regularly search DNA or protein databases for sequences that share an evolutionary or functional relationship with a given query sequence. Traditional search methods, such as BLAST and PSI-BLAST, fo...

    Jason Weston, Rui Kuang, Christina Leslie and William Stafford Noble

    BMC Bioinformatics 2006 7(Suppl 1):S10

    Published on: 20 March 2006