Header logo is ei


2009


no image
Enumeration of condition-dependent dense modules in protein interaction networks

Georgii, E., Dietmann, S., Uno, T., Pagel, P., Tsuda, K.

Bioinformatics, 25(7):933-940, February 2009 (article)

Abstract
Motivation: Modern systems biology aims at understanding how the different molecular components of a biological cell interact. Often, cellular functions are performed by complexes consisting of many different proteins. The composition of these complexes may change according to the cellular environment, and one protein may be involved in several different processes. The automatic discovery of functional complexes from protein interaction data is challenging. While previous approaches use approximations to extract dense modules, our approach exactly solves the problem of dense module enumeration. Furthermore, constraints from additional information sources such as gene expression and phenotype data can be integrated, so we can systematically mine for dense modules with interesting profiles. Results: Given a weighted protein interaction network, our method discovers all protein sets that satisfy a user-defined minimum density threshold. We employ a reverse search strategy, which allows us to exploit the density criterion in an efficient way. Our experiments show that the novel approach is feasible and produces biologically meaningful results. In comparative validation studies using yeast data, the method achieved the best overall prediction performance with respect to confirmed complexes. Moreover, by enhancing the yeast network with phenotypic and phylogenetic profiles and the human network with tissue-specific expression data, we identified condition-dependent complex variants.

Web DOI [BibTex]

2009

Web DOI [BibTex]


no image
Prototype Classification: Insights from Machine Learning

Graf, A., Bousquet, O., Rätsch, G., Schölkopf, B.

Neural Computation, 21(1):272-300, January 2009 (article)

Abstract
We shed light on the discrimination between patterns belonging to two different classes by casting this decoding problem into a generalized prototype framework. The discrimination process is then separated into two stages: a projection stage that reduces the dimensionality of the data by projecting it on a line and a threshold stage where the distributions of the projected patterns of both classes are separated. For this, we extend the popular mean-of-class prototype classification using algorithms from machine learning that satisfy a set of invariance properties. We report a simple yet general approach to express different types of linear classification algorithms in an identical and easy-to-visualize formal framework using generalized prototypes where these prototypes are used to express the normal vector and offset of the hyperplane. We investigate nonmargin classifiers such as the classical prototype classifier, the Fisher classifier, and the relevance vector machine. We then study hard and soft margin cl assifiers such as the support vector machine and a boosted version of the prototype classifier. Subsequently, we relate mean-of-class prototype classification to other classification algorithms by showing that the prototype classifier is a limit of any soft margin classifier and that boosting a prototype classifier yields the support vector machine. While giving novel insights into classification per se by presenting a common and unified formalism, our generalized prototype framework also provides an efficient visualization and a principled comparison of machine learning classification.

PDF Web DOI [BibTex]

PDF Web DOI [BibTex]


no image
Automatic classification of brain resting states using fMRI temporal signals

Soldati, N., Robinson, S., Persello, C., Jovicich, J., Bruzzone, L.

Electronics Letters, 45(1):19-21, January 2009 (article)

Abstract
A novel technique is presented for the automatic discrimination between networks of dasiaresting statesdasia of the human brain and physiological fluctuations in functional magnetic resonance imaging (fMRI). The method is based on features identified via a statistical approach to group independent component analysis time courses, which may be extracted from fMRI data. This technique is entirely automatic and, unlike other approaches, uses temporal rather than spatial information. The method achieves 83% accuracy in the identification of resting state networks.

Web DOI [BibTex]

Web DOI [BibTex]


no image
The DICS repository: module-assisted analysis of disease-related gene lists

Dietmann, S., Georgii, E., Antonov, A., Tsuda, K., Mewes, H.

Bioinformatics, 25(6):830-831, January 2009 (article)

Abstract
The DICS database is a dynamic web repository of computationally predicted functional modules from the human protein–protein interaction network. It provides references to the CORUM, DrugBank, KEGG and Reactome pathway databases. DICS can be accessed for retrieving sets of overlapping modules and protein complexes that are significantly enriched in a gene list, thereby providing valuable information about the functional context.

Web DOI [BibTex]

Web DOI [BibTex]


no image
Large Margin Methods for Part of Speech Tagging

Altun, Y.

In Automatic Speech and Speaker Recognition: Large Margin and Kernel Methods, pages: 141-160, (Editors: Keshet, J. and Bengio, S.), Wiley, Hoboken, NJ, USA, January 2009 (inbook)

Web [BibTex]

Web [BibTex]


no image
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 [BibTex]

DOI [BibTex]


no image
Structure and activity of the N-terminal substrate recognition domains in proteasomal ATPases

Djuranovic, S., Hartmann, MD., Habeck, M., Ursinus, A., Zwickl, P., Martin, J., Lupas, AN., Zeth, K.

Molecular Cell, 34(5):580-590, 2009 (article)

Abstract
The proteasome forms the core of the protein quality control system in archaea and eukaryotes and also occurs in one bacterial lineage, the Actinobacteria. Access to its proteolytic compartment is controlled by AAA ATPases, whose N-terminal domains (N domains) are thought to mediate substrate recognition. The N domains of an archaeal proteasomal ATPase, Archaeoglobus fulgidus PAN, and of its actinobacterial homolog, Rhodococcus erythropolis ARC, form hexameric rings, whose subunits consist of an N-terminal coiled coil and a C-terminal OB domain. In ARC-N, the OB domains are duplicated and form separate rings. PAN-N and ARC-N can act as chaperones, preventing the aggregation of heterologous proteins in vitro, and this activity is preserved in various chimeras, even when these include coiled coils and OB domains from unrelated proteins. The structures suggest a molecular mechanism for substrate processing based on concerted radial motions of the coiled coils relative to the OB rings.

DOI [BibTex]

DOI [BibTex]


no image
Discussion of: Brownian Distance Covariance

Gretton, A., Fukumizu, K., Sriperumbudur, B.

The Annals of Applied Statistics, 3(4):1285-1294, 2009 (article)

[BibTex]

[BibTex]


no image
Covariate shift and local learning by distribution matching

Gretton, A., Smola, A., Huang, J., Schmittfull, M., Borgwardt, K., Schölkopf, B.

In Dataset Shift in Machine Learning, pages: 131-160, (Editors: Quiñonero-Candela, J., Sugiyama, M., Schwaighofer, A. and Lawrence, N. D.), MIT Press, Cambridge, MA, USA, 2009 (inbook)

Abstract
Given sets of observations of training and test data, we consider the problem of re-weighting the training data such that its distribution more closely matches that of the test data. We achieve this goal by matching covariate distributions between training and test sets in a high dimensional feature space (specifically, a reproducing kernel Hilbert space). This approach does not require distribution estimation. Instead, the sample weights are obtained by a simple quadratic programming procedure. We provide a uniform convergence bound on the distance between the reweighted training feature mean and the test feature mean, a transductive bound on the expected loss of an algorithm trained on the reweighted data, and a connection to single class SVMs. While our method is designed to deal with the case of simple covariate shift (in the sense of Chapter ??), we have also found benefits for sample selection bias on the labels. Our correction procedure yields its greatest and most consistent advantages when the learning algorithm returns a classifier/regressor that is simpler" than the data might suggest.

PDF Web [BibTex]

PDF Web [BibTex]


no image
Efficient factor GARCH models and factor-DCC models

Zhang, K., Chan, L.

Quantitative Finance, 9(1):71-91, 2009 (article)

Abstract
We report that, in the estimation of univariate GARCH or multivariate generalized orthogonal GARCH (GO-GARCH) models, maximizing the likelihood is equivalent to making the standardized residuals as independent as possible. Based on this, we propose three factor GARCH models in the framework of GO-GARCH: independent-factor GARCH exploits factors that are statistically as independent as possible; factors in best-factor GARCH have the largest autocorrelation in their squared values such that their volatilities could be forecast well by univariate GARCH; and factors in conditional-decorrelation GARCH are conditionally as uncorrelated as possible. A convenient two-step method for estimating these models is introduced. Since the extracted factors may still have weak conditional correlations, we further propose factor-DCC models as an extension to the above factor GARCH models with dynamic conditional correlation (DCC) modelling the remaining conditional correlations between factors. Experimental results for the Hong Kong stock market show that conditional-decorrelation GARCH and independent-factor GARCH have better generalization performance than the original GO-GARCH, and that conditional-decorrelation GARCH (among factor GARCH models) and its extension with DCC embedded (among factor-DCC models) behave best.

PDF Web DOI [BibTex]

PDF Web DOI [BibTex]


no image
Non-linear System Identification: Visual Saliency Inferred from Eye-Movement Data

Wichmann, F., Kienzle, W., Schölkopf, B., Franz, M.

Journal of Vision, 9(8):article 32, 2009 (article)

Abstract
For simple visual patterns under the experimenter's control we impose which information, or features, an observer can use to solve a given perceptual task. For natural vision tasks, however, there are typically a multitude of potential features in a given visual scene which the visual system may be exploiting when analyzing it: edges, corners, contours, etc. Here we describe a novel non-linear system identification technique based on modern machine learning methods that allows the critical features an observer uses to be inferred directly from the observer's data. The method neither requires stimuli to be embedded in noise nor is it limited to linear perceptive fields (classification images). We demonstrate our technique by deriving the critical image features observers fixate in natural scenes (bottom-up visual saliency). Unlike previous studies where the relevant structure is determined manually—e.g. by selecting Gabors as visual filters—we do not make any assumptions in this regard, but numerically infer number and properties them from the eye-movement data. We show that center-surround patterns emerge as the optimal solution for predicting saccade targets from local image structure. The resulting model, a one-layer feed-forward network with contrast gain-control, is surprisingly simple compared to previously suggested saliency models. Nevertheless, our model is equally predictive. Furthermore, our findings are consistent with neurophysiological hardware in the superior colliculus. Bottom-up visual saliency may thus not be computed cortically as has been thought previously.

Web DOI [BibTex]


no image
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 [BibTex]

DOI [BibTex]


no image
An introduction to Kernel Learning Algorithms

Gehler, P., Schölkopf, B.

In Kernel Methods for Remote Sensing Data Analysis, pages: 25-48, 2, (Editors: Gustavo Camps-Valls and Lorenzo Bruzzone), Wiley, New York, NY, USA, 2009 (inbook)

Abstract
Kernel learning algorithms are currently becoming a standard tool in the area of machine learning and pattern recognition. In this chapter we review the fundamental theory of kernel learning. As the basic building block we introduce the kernel function, which provides an elegant and general way to compare possibly very complex objects. We then review the concept of a reproducing kernel Hilbert space and state the representer theorem. Finally we give an overview of the most prominent algorithms, which are support vector classification and regression, Gaussian Processes and kernel principal analysis. With multiple kernel learning and structured output prediction we also introduce some more recent advancements in the field.

link (url) DOI [BibTex]

link (url) DOI [BibTex]

2003


no image
Molecular phenotyping of human chondrocyte cell lines T/C-28a2, T/C-28a4, and C-28/I2

Finger, F., Schorle, C., Zien, A., Gebhard, P., Goldring, M., Aigner, T.

Arthritis & Rheumatism, 48(12):3395-3403, December 2003 (article)

[BibTex]

2003

[BibTex]


no image
A Study on Rainfall - Runoff Models for Improving Ensemble Streamflow Prediction: 1. Rainfallrunoff Models Using Artificial Neural Networks

Jeong, D., Kim, Y., Cho, S., Shin, H.

Journal of the Korean Society of Civil Engineers, 23(6B):521-530, December 2003 (article)

Abstract
The previous ESP (Ensemble Streamflow Prediction) studies conducted in Korea reported that the modeling error is a major source of the ESP forecast error in winter and spring (i.e. dry seasons), and thus suggested that improving the rainfall-runoff model would be critical to obtain more accurate probabilistic forecasts with ESP. This study used two types of Artificial Neural Networks (ANN), such as a Single Neural Network (SNN) and an Ensemble Neural Networks (ENN), to improve the simulation capability of the rainfall-runoff model of the ESP forecasting system for the monthly inflow to the Daecheong dam. Applied for the first time to Korean hydrology, ENN combines the outputs of member models so that it can control the generalization error better than SNN. Because the dry and the flood season in Korea shows considerably different streamflow characteristics, this study calibrated the rainfall-runoff model separately for each season. Therefore, four rainfall-runoff models were developed according to the ANN types and the seasons. This study compared the ANN models with a conceptual rainfall-runoff model called TANK and verified that the ANN models were superior to TANK. Among the ANN models, ENN was more accurate than SNN. The ANN model performance was improved when the model was calibrated separately for the dry and the flood season. The best ANN model developed in this article will be incorporated into the ESP system to increase the forecast capability of ESP for the monthly inflow to the Daecheong dam.

[BibTex]

[BibTex]


no image
Quantitative Cerebral Blood Flow Measurements in the Rat Using a Beta-Probe and H215O

Weber, B., Spaeth, N., Wyss, M., Wild, D., Burger, C., Stanley, R., Buck, A.

Journal of Cerebral Blood Flow and Metabolism, 23(12):1455-1460, December 2003 (article)

Abstract
Beta-probes are a relatively new tool for tracer kinetic studies in animals. They are highly suited to evaluate new positron emission tomography tracers or measure physiologic parameters at rest and after some kind of stimulation or intervention. In many of these experiments, the knowledge of CBF is highly important. Thus, the purpose of this study was to evaluate the method of CBF measurements using a beta-probe and H215O. CBF was measured in the barrel cortex of eight rats at baseline and after acetazolamide challenge. Trigeminal nerve stimulation was additionally performed in five animals. In each category, three injections of 250 to 300 MBq H215O were performed at 10-minute intervals. Data were analyzed using a standard one-tissue compartment model (K1 = CBF, k2 = CBF/p, where p is the partition coefficient). Values for K1 were 0.35 plusminus 0.09, 0.58 plusminus 0.16, and 0.49 plusminus 0.03 mL dot min-1 dot mL-1 at rest, after acetazolamide challenge, and during trigeminal nerve stimulation, respectively. The corresponding values for k2 were 0.55 plusminus 0.12, 0.94 plusminus 0.16, and 0.85 plusminus 0.12 min-7, and for p were 0.64 plusminus 0.05, 0.61 plusminus 0.07, and 0.59 plusminus 0.06.The standard deviation of the difference between two successive experiments, a measure for the reproducibility of the method, was 10.1%, 13.0%, and 5.7% for K1, k2, and p, respectively. In summary, beta-probes in conjunction with H215O allow the reproducible quantitative measurement of CBF, although some systematic underestimation seems to occur, probably because of partial volume effects.

PDF DOI [BibTex]

PDF DOI [BibTex]


no image
Blind separation of post-nonlinear mixtures using linearizing transformations and temporal decorrelation

Ziehe, A., Kawanabe, M., Harmeling, S., Müller, K.

Journal of Machine Learning Research, 4(7-8):1319-1338, November 2003 (article)

Abstract
We propose two methods that reduce the post-nonlinear blind source separation problem (PNL-BSS) to a linear BSS problem. The first method is based on the concept of maximal correlation: we apply the alternating conditional expectation (ACE) algorithm--a powerful technique from non-parametric statistics--to approximately invert the componentwise nonlinear functions. The second method is a Gaussianizing transformation, which is motivated by the fact that linearly mixed signals before nonlinear transformation are approximately Gaussian distributed. This heuristic, but simple and efficient procedure works as good as the ACE method. Using the framework provided by ACE, convergence can be proven. The optimal transformations obtained by ACE coincide with the sought-after inverse functions of the nonlinearities. After equalizing the nonlinearities, temporal decorrelation separation (TDSEP) allows us to recover the source signals. Numerical simulations testing "ACE-TD" and "Gauss-TD" on realistic examples are performed with excellent results.

PDF PDF DOI [BibTex]

PDF PDF DOI [BibTex]


no image
Correlated stage- and subfield-associated hippocampal gene expression patterns in experimental and human temporal lobe epilepsy

Becker, A., Chen, J., Zien, A., Sochivko, D., Normann, S., Schramm, J., Elger, C., Wiestler, O., Blumcke, I.

European Journal of Neuroscience, 18(10):2792-2802, November 2003 (article)

Abstract
Epileptic activity evokes profound alterations of hippocampal organization and function. Genomic responses may reflect immediate consequences of excitatory stimulation as well as sustained molecular processes related to neuronal plasticity and structural remodeling. Using oligonucleotide microarrays with 8799 sequences, we determined subregional gene expression profiles in rats subjected to pilocarpine-induced epilepsy (U34A arrays, Affymetrix, Santa Clara, CA, USA; P < 0.05, twofold change, n = 3 per stage). Patterns of gene expression corresponded to distinct stages of epilepsy development. The highest number of differentially expressed genes (dentate gyrus, approx. 400 genes and CA1, approx. 700 genes) was observed 3 days after status epilepticus. The majority of up-regulated genes was associated with mechanisms of cellular stress and injury - 14 days after status epilepticus, numerous transcription factors and genes linked to cytoskeletal and synaptic reorganization were differentially expressed and, in the stage of chronic spontaneous seizures, distinct changes were observed in the transcription of genes involved in various neurotransmission pathways and between animals with low vs. high seizure frequency. A number of genes (n = 18) differentially expressed during the chronic epileptic stage showed corresponding expression patterns in hippocampal subfields of patients with pharmacoresistant temporal lobe epilepsy (n = 5 temporal lobe epilepsy patients; U133A microarrays, Affymetrix; covering 22284 human sequences). These data provide novel insights into the molecular mechanisms of epileptogenesis and seizure-associated cellular and structural remodeling of the hippocampus.

[BibTex]

[BibTex]


no image
Concentration Inequalities for Sub-Additive Functions Using the Entropy Method

Bousquet, O.

Stochastic Inequalities and Applications, 56, pages: 213-247, Progress in Probability, (Editors: Giné, E., C. Houdré and D. Nualart), November 2003 (article)

Abstract
We obtain exponential concentration inequalities for sub-additive functions of independent random variables under weak conditions on the increments of those functions, like the existence of exponential moments for these increments. As a consequence of these general inequalities, we obtain refinements of Talagrand's inequality for empirical processes and new bounds for randomized empirical processes. These results are obtained by further developing the entropy method introduced by Ledoux.

PostScript [BibTex]

PostScript [BibTex]


no image
YKL-39 (chitinase 3-like protein 2), but not YKL-40 (chitinase 3-like protein 1), is up regulated in osteoarthritic chondrocytes

Knorr, T., Obermayr, F., Bartnik, E., Zien, A., Aigner, T.

Annals of the Rheumatic Diseases, 62(10):995-998, October 2003 (article)

Abstract
OBJECTIVE: To investigate quantitatively the mRNA expression levels of YKL-40, an established marker of rheumatoid and osteoarthritic cartilage degeneration in synovial fluid and serum, and a closely related molecule YKL-39, in articular chondrocytes. METHODS: cDNA array and online quantitative polymerase chain reaction (PCR) were used to measure mRNA expression levels of YKL-39 and YKL-40 in chondrocytes in normal, early degenerative, and late stage osteoarthritic cartilage samples. RESULTS: Expression analysis showed high levels of both proteins in normal articular chondrocytes, with lower levels of YKL-39 than YKL-40. Whereas YKL-40 was significantly down regulated in late stage osteoarthritic chondrocytes, YKL-39 was significantly up regulated. In vitro both YKLs were down regulated by interleukin 1beta. CONCLUSIONS: The up regulation of YKL-39 in osteoarthritic cartilage suggests that YKL-39 may be a more accurate marker of chondrocyte activation than YKL-40, although it has yet to be established as a suitable marker in synovial fluid and serum. The decreased expression of YKL-40 by osteoarthritic chondrocytes is surprising as increased levels have been reported in rheumatoid and osteoarthritic synovial fluid, where it may derive from activated synovial cells or osteophytic tissue or by increased matrix destruction in the osteoarthritic joint. YKL-39 and YKL-40 are potentially interesting marker molecules for arthritic joint disease because they are abundantly expressed by both normal and osteoarthritic chondrocytes.

[BibTex]

[BibTex]


no image
Statistical Learning Theory, Capacity and Complexity

Schölkopf, B.

Complexity, 8(4):87-94, July 2003 (article)

Abstract
We give an exposition of the ideas of statistical learning theory, followed by a discussion of how a reinterpretation of the insights of learning theory could potentially also benefit our understanding of a certain notion of complexity.

Web DOI [BibTex]


no image
Dealing with large Diagonals in Kernel Matrices

Weston, J., Schölkopf, B., Eskin, E., Leslie, C., Noble, W.

Annals of the Institute of Statistical Mathematics, 55(2):391-408, June 2003 (article)

Abstract
In kernel methods, all the information about the training data is contained in the Gram matrix. If this matrix has large diagonal values, which arises for many types of kernels, then kernel methods do not perform well: We propose and test several methods for dealing with this problem by reducing the dynamic range of the matrix while preserving the positive definiteness of the Hessian of the quadratic programming problem that one has to solve when training a Support Vector Machine, which is a common kernel approach for pattern recognition.

PDF DOI [BibTex]

PDF DOI [BibTex]


no image
The em Algorithm for Kernel Matrix Completion with Auxiliary Data

Tsuda, K., Akaho, S., Asai, K.

Journal of Machine Learning Research, 4, pages: 67-81, May 2003 (article)

PDF [BibTex]

PDF [BibTex]


no image
Constructing Descriptive and Discriminative Non-linear Features: Rayleigh Coefficients in Kernel Feature Spaces

Mika, S., Rätsch, G., Weston, J., Schölkopf, B., Smola, A., Müller, K.

IEEE Transactions on Pattern Analysis and Machine Intelligence, 25(5):623-628, May 2003 (article)

Abstract
We incorporate prior knowledge to construct nonlinear algorithms for invariant feature extraction and discrimination. Employing a unified framework in terms of a nonlinearized variant of the Rayleigh coefficient, we propose nonlinear generalizations of Fisher‘s discriminant and oriented PCA using support vector kernel functions. Extensive simulations show the utility of our approach.

DOI [BibTex]

DOI [BibTex]


no image
Kernel-based nonlinear blind source separation

Harmeling, S., Ziehe, A., Kawanabe, M., Müller, K.

Neural Computation, 15(5):1089-1124, May 2003 (article)

Abstract
We propose kTDSEP, a kernel-based algorithm for nonlinear blind source separation (BSS). It combines complementary research fields: kernel feature spaces and BSS using temporal information. This yields an efficient algorithm for nonlinear BSS with invertible nonlinearity. Key assumptions are that the kernel feature space is chosen rich enough to approximate the nonlinearity and that signals of interest contain temporal information. Both assumptions are fulfilled for a wide set of real-world applications. The algorithm works as follows: First, the data are (implicitly) mapped to a high (possibly infinite)—dimensional kernel feature space. In practice, however, the data form a smaller submanifold in feature space—even smaller than the number of training data points—a fact that has already been used by, for example, reduced set techniques for support vector machines. We propose to adapt to this effective dimension as a preprocessing step and to construct an orthonormal basis of this submanifold. The latter dimension-reduction step is essential for making the subsequent application of BSS methods computationally and numerically tractable. In the reduced space, we use a BSS algorithm that is based on second-order temporal decorrelation. Finally, we propose a selection procedure to obtain the original sources from the extracted nonlinear components automatically. Experiments demonstrate the excellent performance and efficiency of our kTDSEP algorithm for several problems of nonlinear BSS and for more than two sources.

PDF Web DOI [BibTex]

PDF Web DOI [BibTex]


no image
Tractable Inference for Probabilistic Data Models

Csato, L., Opper, M., Winther, O.

Complexity, 8(4):64-68, April 2003 (article)

Abstract
We present an approximation technique for probabilistic data models with a large number of hidden variables, based on ideas from statistical physics. We give examples for two nontrivial applications. © 2003 Wiley Periodicals, Inc.

PDF GZIP Web [BibTex]

PDF GZIP Web [BibTex]


no image
Feature selection and transduction for prediction of molecular bioactivity for drug design

Weston, J., Perez-Cruz, F., Bousquet, O., Chapelle, O., Elisseeff, A., Schölkopf, B.

Bioinformatics, 19(6):764-771, April 2003 (article)

Abstract
Motivation: In drug discovery a key task is to identify characteristics that separate active (binding) compounds from inactive (non-binding) ones. An automated prediction system can help reduce resources necessary to carry out this task. Results: Two methods for prediction of molecular bioactivity for drug design are introduced and shown to perform well in a data set previously studied as part of the KDD (Knowledge Discovery and Data Mining) Cup 2001. The data is characterized by very few positive examples, a very large number of features (describing three-dimensional properties of the molecules) and rather different distributions between training and test data. Two techniques are introduced specifically to tackle these problems: a feature selection method for unbalanced data and a classifier which adapts to the distribution of the the unlabeled test data (a so-called transductive method). We show both techniques improve identification performance and in conjunction provide an improvement over using only one of the techniques. Our results suggest the importance of taking into account the characteristics in this data which may also be relevant in other problems of a similar type.

Web [BibTex]


no image
Use of the Zero-Norm with Linear Models and Kernel Methods

Weston, J., Elisseeff, A., Schölkopf, B., Tipping, M.

Journal of Machine Learning Research, 3, pages: 1439-1461, March 2003 (article)

Abstract
We explore the use of the so-called zero-norm of the parameters of linear models in learning. Minimization of such a quantity has many uses in a machine learning context: for variable or feature selection, minimizing training error and ensuring sparsity in solutions. We derive a simple but practical method for achieving these goals and discuss its relationship to existing techniques of minimizing the zero-norm. The method boils down to implementing a simple modification of vanilla SVM, namely via an iterative multiplicative rescaling of the training data. Applications we investigate which aid our discussion include variable and feature selection on biological microarray data, and multicategory classification.

PDF PostScript PDF [BibTex]

PDF PostScript PDF [BibTex]


no image
An Introduction to Variable and Feature Selection.

Guyon, I., Elisseeff, A.

Journal of Machine Learning, 3, pages: 1157-1182, 2003 (article)

[BibTex]

[BibTex]


no image
Dynamics of a rigid body in a Stokes fluid

Gonzalez, O., Graf, ABA., Maddocks, JH.

Journal of Fluid Mechanics, 2003 (article) Accepted

[BibTex]

[BibTex]


no image
A novel transient heater-foil technique for liquid crystal experiments on film cooled surfaces

Vogel, G., Graf, ABA., von Wolfersdorf, J., Weigand, B.

ASME Journal of Turbomachinery, 125, pages: 529-537, 2003 (article)

PDF [BibTex]

PDF [BibTex]


no image
Support Vector Machines

Schölkopf, B., Smola, A.

In Handbook of Brain Theory and Neural Networks (2nd edition), pages: 1119-1125, (Editors: MA Arbib), MIT Press, Cambridge, MA, USA, 2003 (inbook)

[BibTex]

[BibTex]


no image
Extension of the nu-SVM range for classification

Perez-Cruz, F., Weston, J., Herrmann, D., Schölkopf, B.

In Advances in Learning Theory: Methods, Models and Applications, NATO Science Series III: Computer and Systems Sciences, Vol. 190, 190, pages: 179-196, NATO Science Series III: Computer and Systems Sciences, (Editors: J Suykens and G Horvath and S Basu and C Micchelli and J Vandewalle), IOS Press, Amsterdam, 2003 (inbook)

[BibTex]

[BibTex]


no image
Microarrays: How Many Do You Need?

Zien, A., Fluck, J., Zimmer, R., Lengauer, T.

Journal of Computational Biology, 10(3-4):653-667, 2003 (article)

Abstract
We estimate the number of microarrays that is required in order to gain reliable results from a common type of study: the pairwise comparison of different classes of samples. We show that current knowledge allows for the construction of models that look realistic with respect to searches for individual differentially expressed genes and derive prototypical parameters from real data sets. Such models allow investigation of the dependence of the required number of samples on the relevant parameters: the biological variability of the samples within each class, the fold changes in expression that are desired to be detected, the detection sensitivity of the microarrays, and the acceptable error rates of the results. We supply experimentalists with general conclusions as well as a freely accessible Java applet at www.scai.fhg.de/special/bio/howmanyarrays/ for fine tuning simulations to their particular settings.

Web [BibTex]

Web [BibTex]


no image
New Approaches to Statistical Learning Theory

Bousquet, O.

Annals of the Institute of Statistical Mathematics, 55(2):371-389, 2003 (article)

Abstract
We present new tools from probability theory that can be applied to the analysis of learning algorithms. These tools allow to derive new bounds on the generalization performance of learning algorithms and to propose alternative measures of the complexity of the learning task, which in turn can be used to derive new learning algorithms.

PostScript [BibTex]

PostScript [BibTex]


no image
An Introduction to Support Vector Machines

Schölkopf, B.

In Recent Advances and Trends in Nonparametric Statistics , pages: 3-17, (Editors: MG Akritas and DN Politis), Elsevier, Amsterdam, The Netherlands, 2003 (inbook)

Web DOI [BibTex]

Web DOI [BibTex]


no image
Statistical Learning and Kernel Methods in Bioinformatics

Schölkopf, B., Guyon, I., Weston, J.

In Artificial Intelligence and Heuristic Methods in Bioinformatics, 183, pages: 1-21, 3, (Editors: P Frasconi und R Shamir), IOS Press, Amsterdam, The Netherlands, 2003 (inbook)

[BibTex]

[BibTex]


no image
Statistical Learning and Kernel Methods

Navia-Vázquez, A., Schölkopf, B.

In Adaptivity and Learning—An Interdisciplinary Debate, pages: 161-186, (Editors: R.Kühn and R Menzel and W Menzel and U Ratsch and MM Richter and I-O Stamatescu), Springer, Berlin, Heidelberg, Germany, 2003 (inbook)

[BibTex]

[BibTex]


no image
A Short Introduction to Learning with Kernels

Schölkopf, B., Smola, A.

In Proceedings of the Machine Learning Summer School, Lecture Notes in Artificial Intelligence, Vol. 2600, pages: 41-64, LNAI 2600, (Editors: S Mendelson and AJ Smola), Springer, Berlin, Heidelberg, Germany, 2003 (inbook)

[BibTex]

[BibTex]


no image
Bayesian Kernel Methods

Smola, A., Schölkopf, B.

In Advanced Lectures on Machine Learning, Machine Learning Summer School 2002, Lecture Notes in Computer Science, Vol. 2600, LNAI 2600, pages: 65-117, 0, (Editors: S Mendelson and AJ Smola), Springer, Berlin, Germany, 2003 (inbook)

DOI [BibTex]

DOI [BibTex]


no image
Gene expression in chondrocytes assessed with use of microarrays

Aigner, T., Zien, A., Hanisch, D., Zimmer, R.

Journal of Bone and Joint Surgery, 85(Suppl 2):117-123, 2003 (article)

[BibTex]

[BibTex]


no image
Stability of ensembles of kernel machines

Elisseeff, A., Pontil, M.

In 190, pages: 111-124, NATO Science Series III: Computer and Systems Science, (Editors: Suykens, J., G. Horvath, S. Basu, C. Micchelli and J. Vandewalle), IOS press, Netherlands, 2003 (inbook)

[BibTex]

[BibTex]

2002


no image
Optimized Support Vector Machines for Nonstationary Signal Classification

Davy, M., Gretton, A., Doucet, A., Rayner, P.

IEEE Signal Processing Letters, 9(12):442-445, December 2002 (article)

Abstract
This letter describes an efficient method to perform nonstationary signal classification. A support vector machine (SVM) algorithm is introduced and its parameters optimised in a principled way. Simulations demonstrate that our low complexity method outperforms state-of-the-art nonstationary signal classification techniques.

PostScript Web DOI [BibTex]

2002

PostScript Web DOI [BibTex]


no image
A New Discriminative Kernel from Probabilistic Models

Tsuda, K., Kawanabe, M., Rätsch, G., Sonnenburg, S., Müller, K.

Neural Computation, 14(10):2397-2414, October 2002 (article)

PDF [BibTex]

PDF [BibTex]


no image
Functional Genomics of Osteoarthritis

Aigner, T., Bartnik, E., Zien, A., Zimmer, R.

Pharmacogenomics, 3(5):635-650, September 2002 (article)

Web [BibTex]

Web [BibTex]


no image
Constructing Boosting algorithms from SVMs: an application to one-class classification.

Rätsch, G., Mika, S., Schölkopf, B., Müller, K.

IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(9):1184-1199, September 2002 (article)

Abstract
We show via an equivalence of mathematical programs that a support vector (SV) algorithm can be translated into an equivalent boosting-like algorithm and vice versa. We exemplify this translation procedure for a new algorithm—one-class leveraging—starting from the one-class support vector machine (1-SVM). This is a first step toward unsupervised learning in a boosting framework. Building on so-called barrier methods known from the theory of constrained optimization, it returns a function, written as a convex combination of base hypotheses, that characterizes whether a given test point is likely to have been generated from the distribution underlying the training data. Simulations on one-class classification problems demonstrate the usefulness of our approach.

DOI [BibTex]

DOI [BibTex]


no image
Co-Clustering of Biological Networks and Gene Expression Data

Hanisch, D., Zien, A., Zimmer, R., Lengauer, T.

Bioinformatics, (Suppl 1):145S-154S, 18, July 2002 (article)

Abstract
Motivation: Large scale gene expression data are often analysed by clustering genes based on gene expression data alone, though a priori knowledge in the form of biological networks is available. The use of this additional information promises to improve exploratory analysis considerably. Results: We propose constructing a distance function which combines information from expression data and biological networks. Based on this function, we compute a joint clustering of genes and vertices of the network. This general approach is elaborated for metabolic networks. We define a graph distance function on such networks and combine it with a correlation-based distance function for gene expression measurements. A hierarchical clustering and an associated statistical measure is computed to arrive at a reasonable number of clusters. Our method is validated using expression data of the yeast diauxic shift. The resulting clusters are easily interpretable in terms of the biochemical network and the gene expression data and suggest that our method is able to automatically identify processes that are relevant under the measured conditions.

Web [BibTex]

Web [BibTex]


no image
Confidence measures for protein fold recognition

Sommer, I., Zien, A., von Ohsen, N., Zimmer, R., Lengauer, T.

Bioinformatics, 18(6):802-812, June 2002 (article)

[BibTex]

[BibTex]


no image
The contributions of color to recognition memory for natural scenes

Wichmann, F., Sharpe, L., Gegenfurtner, K.

Journal of Experimental Psychology: Learning, Memory and Cognition, 28(3):509-520, May 2002 (article)

Abstract
The authors used a recognition memory paradigm to assess the influence of color information on visual memory for images of natural scenes. Subjects performed 5-10% better for colored than for black-and-white images independent of exposure duration. Experiment 2 indicated little influence of contrast once the images were suprathreshold, and Experiment 3 revealed that performance worsened when images were presented in color and tested in black and white, or vice versa, leading to the conclusion that the surface property color is part of the memory representation. Experiments 4 and 5 exclude the possibility that the superior recognition memory for colored images results solely from attentional factors or saliency. Finally, the recognition memory advantage disappears for falsely colored images of natural scenes: The improvement in recognition memory depends on the color congruence of presented images with learned knowledge about the color gamut found within natural scenes. The results can be accounted for within a multiple memory systems framework.

PDF Web DOI [BibTex]

PDF Web DOI [BibTex]


no image
Training invariant support vector machines

DeCoste, D., Schölkopf, B.

Machine Learning, 46(1-3):161-190, January 2002 (article)

Abstract
Practical experience has shown that in order to obtain the best possible performance, prior knowledge about invariances of a classification problem at hand ought to be incorporated into the training procedure. We describe and review all known methods for doing so in support vector machines, provide experimental results, and discuss their respective merits. One of the significant new results reported in this work is our recent achievement of the lowest reported test error on the well-known MNIST digit recognition benchmark task, with SVM training times that are also significantly faster than previous SVM methods.

PDF DOI [BibTex]

PDF DOI [BibTex]