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Matic V.,Center for Dynamical Systems | Cherian P.J.,Erasmus Medical Center | Koolen N.,Center for Dynamical Systems | Ansari A.H.,Center for Dynamical Systems | And 5 more authors.
Frontiers in Human Neuroscience | Year: 2015

A quantitative and objective assessment of background electroencephalograph (EEG) in sick neonates remains an everyday clinical challenge. We studied whether long range temporal correlations quantified by detrended fluctuation analysis (DFA) could be used in the neonatal EEG to distinguish different grades of abnormality in the background EEG activity. Long-term EEG records of 34 neonates were collected after perinatal asphyxia, and their background was scored in 1 h epochs (8 h in each neonate) as mild, moderate or severe. We applied DFA on 15 min long, non-overlapping EEG epochs (n = 1088) filtered from 3 to 8 Hz. Our formal feasibility study suggested that DFA exponent can be reliably assessed in only part of the EEG epochs, and in only relatively short time scales (10–60 s), while it becomes ambiguous if longer time scales are considered. This prompted further exploration whether paradigm used for quantifying multifractal DFA (MF-DFA) could be applied in a more efficient way, and whether metrics from MF-DFA paradigm could yield useful benchmark with existing clinical EEG gradings. Comparison of MF-DFA metrics showed a significant difference between three visually assessed background EEG grades. MF-DFA parameters were also significantly correlated to interburst intervals quantified with our previously developed automated detector. Finally, we piloted a monitoring application of MF-DFA metrics and showed their evolution during patient recovery from asphyxia. Our exploratory study showed that neonatal EEG can be quantified using multifractal metrics, which might offer a suitable parameter to quantify the grade of EEG background, or to monitor changes in brain state that take place during long-term brain monitoring. © 2015 Matic, Cherian, Koolen, Ansari, Naulaers, Govaert, Van Huffel, De Vos and Vanhatalo.

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.

Varon C.,Center for Dynamical Systems | Caicedo A.,Catholic University of Leuven | Testelmans D.,University Hospitals Leuven | Buyse B.,University Hospitals Leuven | Van Huffel S.,Catholic University of Leuven
IEEE Transactions on Biomedical Engineering | Year: 2015

Goal: This paper presents a methodology for the automatic detection of sleep apnea from single-lead ECG. Methods: It uses two novel features derived from the ECG, and two well-known features in heart rate variability analysis, namely the standard deviation and the serial correlation coefficients of the RR interval time series. The first novel feature uses the principal components of the QRS complexes, and it describes changes in their morphology caused by an increased sympathetic activity during apnea. The second novel feature extracts the information shared between respiration and heart rate using orthogonal subspace projections. Respiratory information is derived from the ECG by means of three state-of-the-art algorithms, which are implemented and compared here. All features are used as input to a least-squares support vector machines classifier, using an RBF kernel. In total, 80 ECG recordings were included in the study. Results: Accuracies of about 85% are achieved on a minute-by-minute basis, for two independent datasets including both hypopneas and apneas together. Separation between apnea and normal recordings is achieved with 100% accuracy. In addition to apnea classification, the proposed methodology determines the contamination level of each ECG minute. Conclusion: The performances achieved are comparable with those reported in the literature for fully automated algorithms. Significance: These results indicate that the use of only ECG sensors can achieve good accuracies in the detection of sleep apnea. Moreover, the contamination level of each ECG segment can be used to automatically detect artefacts, and to highlight segments that require further visual inspection. © 2015 IEEE.

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 (https://galahad.esat.kuleuven.be) 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.

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