Center for Dynamical Systems

Leuven, Belgium

Center for Dynamical Systems

Leuven, Belgium

Time filter

Source Type

Fanuel M.,Center for Dynamical Systems | Alaiz C.M.,Center for Dynamical Systems | Suykens J.A.K.,Center for Dynamical Systems
Physical Review E - Statistical, Nonlinear, and Soft Matter Physics | Year: 2017

Communities in directed networks have often been characterized as regions with a high density of links, or as sets of nodes with certain patterns of connection. Our approach for community detection combines the optimization of a quality function and a spectral clustering of a deformation of the combinatorial Laplacian, the so-called magnetic Laplacian. The eigenfunctions of the magnetic Laplacian, which we call magnetic eigenmaps, incorporate structural information. Hence, using the magnetic eigenmaps, dense communities including directed cycles can be revealed as well as "role" communities in networks with a running flow, usually discovered thanks to mixture models. Furthermore, in the spirit of the Markov stability method, an approach for studying communities at different energy levels in the network is put forward, based on a quantum mechanical system at finite temperature. © 2017 American Physical Society.

Bahari M.H.,Center for Dynamical Systems | Bertrand A.,Center for Dynamical Systems | Moonen M.,Center for Dynamical Systems
IEEE/ACM Transactions on Audio Speech and Language Processing | Year: 2017

Microphone arrays allow to exploit the spatial coherence between simultaneously recorded microphone signals, e.g., to perform speech enhancement, i.e., to extract a speech signal and reduce background noise. However, in systems where the microphones are not sampled in a synchronous fashion, as it is often the case in wireless acoustic sensor networks, a sampling rate offset (SRO) exists between signals recorded in different nodes, which severely affects the speech enhancement performance. To avoid this performance reduction, the SRO should be estimated and compensated for. In this paper, we propose a new approach to blind SRO estimation for an asynchronous wireless acoustic sensor network, which exploits the phase drift of the coherence between the asynchronous microphones signals. We utilize the fact that the SRO causes a linearly increasing time delay between two signals and hence a linearly increasing phase-shift in the short-time Fourier transform domain. The increasing phase shift, observed as a phase drift of the coherence between the signals, is used in a weighted least-squares framework to estimate the SRO. This method is referred to as least-squares coherence drift (LCD). Experimental results in different real-world recording and simulated scenarios show the effectiveness of LCD compared to different benchmark methods. The LCD is effective even for short signal segments. We finally demonstrate that the use of the LCD within a conventional compensation approach eliminates the performance loss due to SRO in a speech enhancement algorithm based on the multichannel Wiener filter. © 2014 IEEE.

Sifrim A.,Center for Dynamical Systems | Sifrim A.,Future Health | Popovic D.,Center for Dynamical Systems | Popovic D.,Future Health | And 15 more authors.
Nature Methods | Year: 2013

Massively parallel sequencing greatly facilitates the discovery of novel disease genes causing Mendelian and oligogenic disorders. However, many mutations are present in any individual genome, and identifying which ones are disease causing remains a largely open problem. We introduce eXtasy, an approach to prioritize nonsynonymous single-nucleotide variants (nSNVs) that substantially improves prediction of disease-causing variants in exome sequencing data by integrating variant impact prediction, haploinsufficiency prediction and phenotype-specific gene prioritization. © 2013 Nature America, Inc. All rights reserved.

Sorber L.,Catholic University of Leuven | Sorber L.,Center for Dynamical Systems | Sorber L.,Future Health | Van Barel M.,Catholic University of Leuven | And 3 more authors.
IEEE Journal on Selected Topics in Signal Processing | Year: 2015

We present structured data fusion (SDF) as a framework for the rapid prototyping of knowledge discovery in one or more possibly incomplete data sets. In SDF, each data set - stored as a dense, sparse, or incomplete tensor - is factorized with a matrix or tensor decomposition. Factorizations can be coupled, or fused, with each other by indicating which factors should be shared between data sets. At the same time, factors may be imposed to have any type of structure that can be constructed as an explicit function of some underlying variables. With the right choice of decomposition type and factor structure, even well-known matrix factorizations such as the eigenvalue decomposition, singular value decomposition and QR factorization can be computed with SDF. A domain specific language (DSL) for SDF is implemented as part of the software package Tensorlab, with which we offer a library of tensor decompositions and factor structures to choose from. The versatility of the SDF framework is demonstrated by means of four diverse applications, which are all solved entirely within Tensorlab's DSL. © 2015 IEEE.

Winand R.,Center for Dynamical Systems | Winand R.,Future Health | Hens K.,Maastricht University | Dondorp W.,Maastricht University | And 7 more authors.
Human Reproduction | Year: 2014

STUDY QUESTIONWhat are the analytical and clinical validity and the clinical utility of in vitro screening of embryos by whole-genome sequencing?SUMMARY ANSWERAt present there are still many limitations in terms of analytical and clinical validity and utility and many ethical questions remain.WHAT IS KNOWN ALREADYWhole-genome sequencing of IVF/ICSI embryos is technically possible. Many loss-of-function mutations exist in the general population without serious effects on the phenotype of the individual. Moreover, annotations of genes and the reference genome are still not 100% correct.STUDY DESIGN, SIZE, DURATIONWe used publicly available samples from the 1000 Genomes project and Complete Genomics, together with 42 samples from in-house research samples of parents from trios to investigate the presence of loss-of-function mutations in healthy individuals.PARTICIPANTS/MATERIALS, SETTING, METHODSIn the samples, we looked for mutations in genes that are associated with a selection of severe Mendelian disorders with a known molecular basis. We looked for mutations predicted to be damaging by PolyPhen and SIFT and for mutations annotated as disease causing in Human Genome Mutation Database (HGMD).MAIN RESULTS AND THE ROLE OF CHANCEMore than 40% of individuals who can be considered healthy have mutations that are predicted to be damaging in genes associated with severe Mendelian disorders or are annotated as disease causing.LIMITATIONS, REASONS FOR CAUTIONThe analysis relies on current knowledge and databases are continuously updated to reflect our increasing knowledge about the genome. In the process of our analysis several updates were already made.WIDER IMPLICATIONS OF THE FINDINGSAt this moment it is not advisable to use whole-genome sequencing as a tool to set up health profiles to select embryos for transfer. We also raise some ethical questions that have to be addressed before this technology can be used for embryo selection.STUDY FUNDINGThis research was supported by: Research Council KU Leuven (Projects: GOA/10/09 MaNet, KUL PFV/10/016 SymBioSys); Flemish Government: IWT - Agency for Innovation by Science and Technology (Project: O&O ExaScience Life), Hercules Foundation (Project: Hercules III PacBio RS), iMinds Future Health Department (Projects: SBO 2013, Art&D Instance), Flemish tier-1 Supercomputer (Project: VSC Tier 1 Exome sequencing); K.H. was supported by the Centre for Society and Life Sciences (CSG, non-profit organization) (Project number: 70.1.074).COMPETING INTEREST(S)None of the authors has any conflict of interest to declare.TRIAL REGISTRATION NUMBERN/A. © The Author 2014.

Verbeeck N.,Center for Dynamical Systems | Yang J.,Vanderbilt University | De Moor B.,Center for Dynamical Systems | Caprioli R.M.,Vanderbilt University | And 2 more authors.
Analytical Chemistry | Year: 2014

Imaging mass spectrometry (IMS) has become a prime tool for studying the distribution of biomolecules in tissue. Although IMS data sets can become very large, computational methods have made it practically feasible to search these experiments for relevant findings. However, these methods lack access to an important source of information that many human interpretations rely upon: anatomical insight. In this work, we address this need by (1) integrating a curated anatomical data source with an empirically acquired IMS data source, establishing an algorithm-accessible link between them and (2) demonstrating the potential of such an IMS-anatomical atlas link by applying it toward automated anatomical interpretation of ion distributions in tissue. The concept is demonstrated in mouse brain tissue, using the Allen Mouse Brain Atlas as the curated anatomical data source that is linked to MALDI-based IMS experiments. We first develop a method to spatially map the anatomical atlas to the IMS data sets using nonrigid registration techniques. Once a mapping is established, a second computational method, called correlation-based querying, gives an elementary demonstration of the link by delivering basic insight into relationships between ion images and anatomical structures. Finally, a third algorithm moves further beyond both registration and correlation by providing automated anatomical interpretation of ion images. This task is approached as an optimization problem that deconstructs ion distributions as combinations of known anatomical structures. We demonstrate that establishing a link between an IMS experiment and an anatomical atlas enables automated anatomical annotation, which can serve as an important accelerator both for human and machine-guided exploration of IMS experiments. © 2014 American Chemical Society.

Widjaja D.,Center for Dynamical Systems | Caicedo A.,Center for Dynamical Systems | Vlemincx E.,Catholic University of Leuven | Van Diest I.,Catholic University of Leuven | Van Huffel S.,Center for Dynamical Systems
PLoS ONE | Year: 2014

The variability of the heart rate (HRV) is widely studied as it contains information about the activity of the autonomic nervous system (ANS). However, HRV is influenced by breathing, independently of ANS activity. It is therefore important to include respiratory information in HRV analyses in order to correctly interpret the results. In this paper, we propose to record respiratory activity and use this information to separate the tachogram in two components: one which is related to breathing and one which contains all heart rate variations that are unrelated to respiration. Several algorithms to achieve this have been suggested in the literature, but no comparison between the methods has been performed yet. In this paper, we conduct two studies to evaluate the methods' performances to accurately decompose the tachogram in two components and to assess the robustness of the algorithms. The results show that orthogonal subspace projection and an ARMAX model yield the best performances over the two comparison studies. In addition, a real-life example of stress classification is presented to demonstrate that this approach to separate respiratory information in HRV studies can reveal changes in the heart rate variations that are otherwise masked by differing respiratory patterns. © 2014 Widjaja et al.

Sorensen M.,Center for Dynamical Systems | Sorensen M.,Catholic University of Leuven | De Lathauwer L.,Center for Dynamical Systems | De Lathauwer L.,Catholic University of Leuven
IEEE Transactions on Signal Processing | Year: 2016

The Canonical Polyadic Decomposition (CPD) of higher-order tensors has proven to be an important tool for array processing. CPD approaches have so far assumed regular array geometries such as uniform linear arrays. However, in the case of sparse arrays such as nonuniform linear arrays (NLAs), the CPD approach is not suitable anymore. Using the coupled CPD we propose in this paper a multiple invariance ESPRIT method for both one- and multi-dimensional NLA processing. We obtain a multiresolution ESPRIT method for sparse arrays with multiple baselines. The coupled CPD framework also yields a new uniqueness condition that is relaxed compared with the CPD approach. It also leads to an eigenvalue decomposition based algorithm that is guaranteed to reduce the multi-source NLA problem into decoupled single-source NLA problems in the noiseless case. Finally, we present a new polynomial rooting procedure for the latter problem, which again is guaranteed to find the solution in the noiseless case. In the presence of noise, the algebraic algorithm provides an inexpensive initialization for optimization-based methods. © 2016 IEEE.

Laenen G.,Center for Dynamical Systems | Ardeshirdavani A.,Center for Dynamical Systems | Moreau Y.,Center for Dynamical Systems | Thorrez L.,Catholic University of Leuven
Nucleic acids research | Year: 2015

Galahad ( is a web-based application for analysis of drug effects. It provides an intuitive interface to be used by anybody interested in leveraging microarray data to gain insights into the pharmacological effects of a drug, mainly identification of candidate targets, elucidation of mode of action and understanding of off-target effects. The core of Galahad is a network-based analysis method of gene expression. As an input, Galahad takes raw Affymetrix human microarray data from treatment versus control experiments and provides quality control and data exploration tools, as well as computation of differential expression. Alternatively, differential expression values can be uploaded directly. Using these differential expression values, drug target prioritization and both pathway and disease enrichment can be calculated and visualized. Drug target prioritization is based on the integration of the gene expression data with a functional protein association network. The web site is free and open to all and there is no login requirement. © The Author(s) 2015. Published by Oxford University Press on behalf of Nucleic Acids Research.

Zakeri P.,Center for Dynamical Systems | Zakeri P.,Catholic University of Leuven | Jeuris B.,Catholic University of Leuven | Vandebril R.,Catholic University of Leuven | And 2 more authors.
Bioinformatics | Year: 2014

Motivation: Various approaches based on features extracted from protein sequences and often machine learning methods have been used in the prediction of protein folds. Finding an efficient technique for integrating these different protein features has received increasing attention. In particular, kernel methods are an interesting class of techniques for integrating heterogeneous data. Various methods have been proposed to fuse multiple kernels. Most techniques for multiple kernel learning focus on learning a convex linear combination of base kernels. In addition to the limitation of linear combinations, working with such approaches could cause a loss of potentially useful information. Results: We design several techniques to combine kernel matrices by taking more involved, geometry inspired means of these matrices instead of convex linear combinations. We consider various sequence-based protein features including information extracted directly from position-specific scoring matrices and local sequence alignment. We evaluate our methods for classification on the SCOP PDB-40D benchmark dataset for protein fold recognition. The best overall accuracy on the protein fold recognition test set obtained by our methods is ∼86.7%. This is an improvement over the results of the best existing approach. Moreover, our computational model has been developed by incorporating the functional domain composition of proteins through a hybridization model. It is observed that by using our proposed hybridization model, the protein fold recognition accuracy is further improved to 89.30%. Furthermore, we investigate the performance of our approach on the protein remote homology detection problem by fusing multiple string kernels. © The Author 2014.

Loading Center for Dynamical Systems collaborators
Loading Center for Dynamical Systems collaborators