NIPS workshop on New Problems and Methods in Computational Biology
Citation:
BMC Bioinformatics
2007
8(Suppl 10):S1
Volume 8 Supplement 10
Neural Information Processing Systems (NIPS) workshop on New Problems and Methods in Computational Biology
Proceedings
Edited by Gal Chechik, Christina Leslie, William Stafford Noble, Gunnar Rätsch, Quiad Morris and Koji Tsuda
NIPS workshop on New Problems and Methods in Computational Biology. Go to conference site.
Whistler, Canada8 December 2006
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Prediction of tissue-specific cis-regulatory modules using Bayesian networks and regression trees
In vertebrates, a large part of gene transcriptional regulation is operated by cis-regulatory modules. These modules are believed to be regulating much of the tissue-specificity of gene expression.
Citation: BMC Bioinformatics 2007 8(Suppl 10):S2 -
Semi-supervised learning for the identification of syn-expressed genes from fused microarray and in situ image data
Gene expression measurements during the development of the fly Drosophila melanogaster are routinely used to find functional modules of temporally co-expressed genes. Complimentary large data sets of in situ RNA ...
Citation: BMC Bioinformatics 2007 8(Suppl 10):S3 -
Time-series alignment by non-negative multiple generalized canonical correlation analysis
Quantitative analysis of differential protein expressions requires to align temporal elution measurements from liquid chromatography coupled to mass spectrometry (LC/MS). We propose multiple Canonical Correlation...
Citation: BMC Bioinformatics 2007 8(Suppl 10):S4 -
Analyzing in situ gene expression in the mouse brain with image registration, feature extraction and block clustering
Many important high throughput projects use in situ hybridization and may require the analysis of images of spatial cross sections of organisms taken with cellular level resolution. Projects creating gene express...
Citation: BMC Bioinformatics 2007 8(Suppl 10):S5 -
A mixture of feature experts approach for protein-protein interaction prediction
High-throughput methods can directly detect the set of interacting proteins in model species but the results are often incomplete and exhibit high false positive and false negative rates. A number of researche...
Citation: BMC Bioinformatics 2007 8(Suppl 10):S6 -
Accurate splice site prediction using support vector machines
For splice site recognition, one has to solve two classification problems: discriminating true from decoy splice sites for both acceptor and donor sites. Gene finding systems typically rely on Markov Chains to...
Citation: BMC Bioinformatics 2007 8(Suppl 10):S7 -
A new pairwise kernel for biological network inference with support vector machines
Much recent work in bioinformatics has focused on the inference of various types of biological networks, representing gene regulation, metabolic processes, protein-protein interactions, etc. A common setting i...
Citation: BMC Bioinformatics 2007 8(Suppl 10):S8
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Source Normalized Impact per Paper (SNIP): 0.821
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