Sonnenburg, S., Zien, A., Philips, P., Rätsch, G.
Motivation: At the heart of many important bioinformatics problems, such as gene finding and function prediction, is the classification of biological sequences. Frequently the most accurate...
Support vector machines and kernels for computational biology (2008)
Ben-Hur, A., Ong, C.S., Sonnenburg, S., Schölkopf, B., Rätsch, G.
Support Vector Machines and Kernels for Computational Biology (2008)
Ben-Hur, A., Ong, C.S., Sonnenburg, S., Schölkopf, B., Rätsch, G.
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Support Vector Machines and Kernels for Computational Biology (2008)
Ben-Hur, A., Ong, C.S., Sonnenburg, S., Schölkopf, B., Rätsch, G.
Improving the Caenorhabditis elegans genome annotation using machine learning (2007)
Rätsch, G., Sonnenburg, S., Srinivasan, J., Witte, H., ...
For modern biology, precise genome annotations are of prime importance, as they allow the accurate definition of genic regions. We employ state-of-the-art machine learning methods to assay and...
The need for open source software in machine learning (2007)
Sonnenburg, S., Braun, M.L., Ong, C.S., Bengio, S., Bottou, L., Holmes, G., ...
Open source tools have recently reached a level of maturity which makes them suitable for building large-scale real-world systems. At the same time, the field of machine learning has developed a...
Improving the C. elegans genome annotation using machine learning techniques (2007)
Rätsch, Gunnar, Sonnenburg, S., Srinavasan, J., Witte, Han, Müller, H., Sommer, Ralf, ...
The Need for Open Source Software in Machine Learning (2007)
Sonnenburg, S., Braun, M.L., Ong, C.S., Bengio, S., Bottou, L., Holmes, G., ...
Open source tools have recently reached a level of maturity which makes them suitable for building large-scale real-world systems. At the same time, the field of machine learning has developed a...
Accurate Splice site Prediction Using Support Vector Machines (2007)
Sonnenburg, S., Schweikert, G., Philips, P., Behr, J., Rätsch, G.
Background: 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...
Accurate splice site prediction using support vector machines (2007)
Sonnenburg, S., Schweikert, G., Philips, P., Behr, J., Rätsch, G.
Background: 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...
ARTS: Accurate Recognition of Transcription Starts in Human (2006)
Sonnenburg, S., Zien, A., Rätsch, G.
Motivation: One of the most important features of genomic DNA are the protein-coding genes. While it is of great value to identify those genes and the encoded proteins, it is also crucial to...
Large Scale Multiple Kernel Learning (2006)
Sonnenburg, S., Rätsch, G., Schäfer, C., Schölkopf, B.
While classical kernel-based learning algorithms are based on a single kernel, in practice it is often desirable to use multiple kernels. Lanckriet et al. (2004) considered conic combinations of...
Learning interpretable SVMs for biological sequence classification (2006)
Rätsch, G., Sonnenburg, S., Schäfer, C.
Background: Support Vector Machines (SVMs) - using a variety of string kernels - have been successfully applied to biological sequence classification problems. While SVMs achieve high classification...
Large scale multiple kernel learning (2006)
Sonnenburg, S., Rätsch, G., Schäfer, C., Schölkopf, B.
While classical kernel-based learning algorithms are based on a single kernel, in practice it is often desirable to use multiple kernels. Lanckriet et al. (2004) considered conic combinations of...
ARTS: Accurate Recognition of Transcription Starts in Human (2006)
Sonnenburg, S., Zien, A., Rätsch, G.
We develop new methods for finding transcription start sites (TSS) of RNA Polymerase II binding genes in genomic DNA sequences. Employing Support Vector Machines with advanced sequence kernels, we...
Classifying 'drug-likeness' with kernel-based learning methods (2005)
Rätsch, G., Sonnenburg, S., Mika, S., Grimm, M., Heinrich, N.
In this article we report about a successful application of modern machine learning technology, namely Support Vector Machines, to the problem of assessing the 'drug-likeness' of a chemical from a...
RASE: Recognition of alternatively spliced exons in C.elegans (2005)
Rätsch, G., Sonnenburg, S., Schölkopf, B.
Motivation: Eukaryotic pre-mRNAs are spliced to form mature mRNA. Pre-mRNA alternative splicing greatly increases the complexity of gene expression. Estimates show that more than half of the human...
Learning interpretable SVMs for biological sequence classification (2005)
Sonnenburg, S., Rätsch, G., Schäfer, C.
We propose novel algorithms for solving the so-called Support Vector Multiple Kernel Learning problem and show how they can be used to understand the resulting support vector decision function. While...
G. Rätsch, S. Sonnenburg, B. Schölkopf
Vol. 21 Suppl. 1 2005, pages i369–i377 doi:10.1093/bioinformatics/bti1053
Large scale genomic sequence svm classifiers (2005)
S. Sonnenburg, G. Rätsch, B. Schölkopf
Abstract. In genomic sequence analysis tasks like splice site recognition or promoter identification, large amounts of training sequences are available, and indeed needed to achieve sufficiently high...
Learning interpretable SVMs for biological sequence classification (2005)
S. Sonnenburg, G. Rätsch, C. Schäfer
Abstract. We propose novel algorithms for solving the so-called Support Vector Multiple Kernel Learning problem and show how they can be used to understand the resulting support vector decision...
RASE: recognition of alternatively spliced exons in C.elegans (2005)
Rätsch, G., Sonnenburg, S., Schölkopf, B.
Motivation: Eukaryotic pre-mRNAs are spliced to form mature mRNA. Pre-mRNA alternative splicing greatly increases the complexity of gene expression. Estimates show that more than half of the human...
A new discriminative kernel from probabilistic models (2002)
Tsuda, K., Kawanabe, M., Rätsch, G., Sonnenburg, S.
Recently, Jaakkola and Haussler (1999) proposed a method for constructing kernel functions from probabilistic models. Their so-called Fisher kernel has been combined with discriminative classifiers...
New methods for splice site recognition (2002)
Sonnenburg, S., Rätsch, G., Jagota, A.K.
Splice sites are locations in DNA which separate protein-coding regions (exons) from noncoding regions (introns). Accurate splice site detectors thus form important components of computational gene...
New methods for splice site recognition (2002)
S. Sonnenburg, G. Rätsch, A. Jagota
Abstract. Splice sites are locations in DNA which separate protein-coding regions (exons) from noncoding regions (introns). Accurate splice site detectors thus form important components of...
New Methods for Splice Site Recognition (2002)
Sonnenburg Ratsch Jagota, S. Sonnenburg, G. Ratsch, A. Jagota
Splice sites are locations in DNA which separate protein-coding regions (exons) from noncoding regions (introns). Accurate splice site detectors thus form important components of computational gene...