Alexander Zien

Research Scientist

Alumni

Empirical Inference

Publications

49 results (View BibTeX file of all listed publications)

2011

Support Vector Machines as Probabilistic Models

Franc, V., Zien, A., Schölkopf, B.

In Proceedings of the 28th International Conference on Machine Learning, pages: 665-672, (Editors: L Getoor and T Scheffer), International Machine Learning Society, Madison, WI, USA, ICML, July 2011 (inproceedings)

Abstract

We show how the SVM can be viewed as a maximum likelihood estimate of a class of probabilistic models. This model class can be viewed as a reparametrization of the SVM in a similar vein to the v-SVM reparametrizing the classical (C-)SVM. It is not discriminative, but has a non-uniform marginal. We illustrate the benefits of this new view by rederiving and re-investigating two established SVM-related algorithms.

PDF Web [BibTex]

2011

Franc, V., Zien, A., Schölkopf, B. Support Vector Machines as Probabilistic Models In Proceedings of the 28th International Conference on Machine Learning, pages: 665-672, (Editors: L Getoor and T Scheffer), International Machine Learning Society, Madison, WI, USA, ICML, July 2011 (inproceedings)

PDF Web [BibTex]

2009

mGene: accurate SVM-based gene finding with an application to nematode genomes

Schweikert, G., Zien, A., Zeller, G., Behr, J., Dieterich, C., Ong, C., Philips, P., De Bona, F., Hartmann, L., Bohlen, A., Krüger, N., Sonnenburg, S., Rätsch, G.

Genome Research, 19(11):2133-43, 2009 (article)

Abstract

We present a highly accurate gene-prediction system for eukaryotic genomes, called mGene. It combines in an unprecedented manner the flexibility of generalized hidden Markov models (gHMMs) with the predictive power of modern machine learning methods, such as Support Vector Machines (SVMs). Its excellent performance was proved in an objective competition based on the genome of the nematode Caenorhabditis elegans. Considering the average of sensitivity and specificity, the developmental version of mGene exhibited the best prediction performance on nucleotide, exon, and transcript level for ab initio and multiple-genome gene-prediction tasks. The fully developed version shows superior performance in 10 out of 12 evaluation criteria compared with the other participating gene finders, including Fgenesh++ and Augustus. An in-depth analysis of mGene's genome-wide predictions revealed that approximately 2200 predicted genes were not contained in the current genome annotation. Testing a subset of 57 of these genes by RT-PCR and sequencing, we confirmed expression for 24 (42%) of them. mGene missed 300 annotated genes, out of which 205 were unconfirmed. RT-PCR testing of 24 of these genes resulted in a success rate of merely 8%. These findings suggest that even the gene catalog of a well-studied organism such as C. elegans can be substantially improved by mGene's predictions. We also provide gene predictions for the four nematodes C. briggsae, C. brenneri, C. japonica, and C. remanei. Comparing the resulting proteomes among these organisms and to the known protein universe, we identified many species-specific gene inventions. In a quality assessment of several available annotations for these genomes, we find that mGene's predictions are most accurate.

DOI Project Page [BibTex]

2009

Schweikert, G., Zien, A., Zeller, G., Behr, J., Dieterich, C., Ong, C., Philips, P., De Bona, F., Hartmann, L., Bohlen, A., Krüger, N., Sonnenburg, S., Rätsch, G. mGene: accurate SVM-based gene finding with an application to nematode genomes Genome Research, 19(11):2133-43, 2009 (article)

DOI Project Page [BibTex]

mGene.web: a web service for accurate computational gene finding

Schweikert, G., Behr, J., Zien, A., Zeller, G., Ong, C., Sonnenburg, S., Rätsch, G.

Nucleic Acids Research, 37, pages: W312-6, 2009 (article)

Abstract

We describe mGene.web, a web service for the genome-wide prediction of protein coding genes from eukaryotic DNA sequences. It offers pre-trained models for the recognition of gene structures including untranslated regions in an increasing number of organisms. With mGene.web, users have the additional possibility to train the system with their own data for other organisms on the push of a button, a functionality that will greatly accelerate the annotation of newly sequenced genomes. The system is built in a highly modular way, such that individual components of the framework, like the promoter prediction tool or the splice site predictor, can be used autonomously. The underlying gene finding system mGene is based on discriminative machine learning techniques and its high accuracy has been demonstrated in an international competition on nematode genomes. mGene.web is available at http://www.mgene.org/web, it is free of charge and can be used for eukaryotic genomes of small to moderate size (several hundred Mbp).

DOI Project Page [BibTex]

Schweikert, G., Behr, J., Zien, A., Zeller, G., Ong, C., Sonnenburg, S., Rätsch, G. mGene.web: a web service for accurate computational gene finding Nucleic Acids Research, 37, pages: W312-6, 2009 (article)

DOI Project Page [BibTex]

2008

An Automated Combination of Kernels for Predicting Protein Subcellular Localization

Ong, C., Zien, A.

In WABI 2008, pages: 186-197, (Editors: Crandall, K. A., J. Lagergren), Springer, Berlin, Germany, 8th Workshop on Algorithms in Bioinformatics, September 2008 (inproceedings)

Abstract

Protein subcellular localization is a crucial ingredient to many important inferences about cellular processes, including prediction of protein function and protein interactions. While many predictive computational tools have been proposed, they tend to have complicated architectures and require many design decisions from the developer. Here we utilize the multiclass support vector machine (m-SVM) method to directly solve protein subcellular localization without resorting to the common approach of splitting the problem into several binary classification problems. We further propose a general class of protein sequence kernels which considers all motifs, including motifs with gaps. Instead of heuristically selecting one or a few kernels from this family, we utilize a recent extension of SVMs that optimizes over multiple kernels simultaneously. This way, we automatically search over families of possible amino acid motifs. We compare our automated approach to three other predictors on four different datasets, and show that we perform better than the current state of the art. Further, our method provides some insights as to which sequence motifs are most useful for determining subcellular ocalization, which are in agreement with biological reasoning.

PDF PDF Web DOI [BibTex]

2008

Ong, C., Zien, A. An Automated Combination of Kernels for Predicting Protein Subcellular Localization In WABI 2008, pages: 186-197, (Editors: Crandall, K. A., J. Lagergren), Springer, Berlin, Germany, 8th Workshop on Algorithms in Bioinformatics, September 2008 (inproceedings)

PDF PDF Web DOI [BibTex]

mGene: A Novel Discriminative Gene Finder

Schweikert, G., Zeller, G., Zien, A., Behr, J., Sonnenburg, S., Philips, P., Ong, C., Rätsch, G.

Worm Genomics and Systems Biology meeting, July 2008 (talk)

[BibTex]

Schweikert, G., Zeller, G., Zien, A., Behr, J., Sonnenburg, S., Philips, P., Ong, C., Rätsch, G. mGene: A Novel Discriminative Gene Finder Worm Genomics and Systems Biology meeting, July 2008 (talk)

[BibTex]

2007

Positional Oligomer Importance Matrices

Sonnenburg, S., Zien, A., Philips, P., Rätsch, G.

NIPS Workshop on Machine Learning in Computational Biology, December 2007 (talk)

Abstract

At the heart of many important bioinformatics problems, such as gene finding and function prediction, is the classification of biological sequences, above all of DNA and proteins. In many cases, the most accurate classifiers are obtained by training SVMs with complex sequence kernels, for instance for transcription starts or splice sites. However, an often criticized downside of SVMs with complex kernels is that it is very hard for humans to understand the learned decision rules and to derive biological insights from them. To close this gap, we introduce the concept of positional oligomer importance matrices (POIMs) and develop an efficient algorithm for their computation. We demonstrate how they overcome the limitations of sequence logos, and how they can be used to find relevant motifs for different biological phenomena in a straight-forward way. Note that the concept of POIMs is not limited to interpreting SVMs, but is applicable to general k−mer based scoring systems.

Alexander Zien

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