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.
Claesen M.,Center for Dynamical Systems |
Gillard P.,University of Leuven and University Hospitals Leuven |
De Smet F.,Catholic University of Leuven |
Callens M.,University of Leuven and University Hospitals Leuven |
And 2 more authors.
Journal of Clinical Endocrinology and Metabolism | Year: 2016
Context: Several observational studies and meta-analyses have reported increased mortality of patients taking sulfonylurea and insulin. The impact of patient profiles and concomitant therapies often remains unclear. Objective: The objective of the study was to quantify survival of patients after starting glucoselowering agents (GLAs) and compare it with control subjects, matched for risk profiles and concomitant therapies. Design: This was a retrospective, controlled, cohort study. Setting: The study is based on health expenditure records of the largest Belgian health mutual insurer, covering more than 4.4 million people. Patients: A total of 115 896 patients starting metformin, sulfonylurea, or insulin (alone or in combination) between January 2003 and December 2007 participated in the study. Control subjects without GLA therapy were matched for age, gender, history of cardiovascular events, and therapy with antihypertensives, statins and blood platelet aggregation inhibitors. Intervention(s): There were no interventions. Main Outcome Measure: Five-year survival after the start of GLA was measured. Results: Profiles of patients using different GLAs varied, with patients on sulfonylurea being oldest and patients on insulin having more frequently a history of cardiovascular disease. Excess mortality differed across GLA therapies compared with matched controls without GLAs, even after adjusting for observable characteristics. Only metformin monotherapy was not associated with an increased 5-year mortality compared with matched controls, whereas individuals on a combination of sulfonylurea and insulin had the highest mortality risks. Age and concomitant use of statins strongly affect survival. Conclusions: Differences exist in 5-year survival of patients on GLA, at least partly driven by the risk profile of the individuals themselves. Metformin use was associated with lowest 5-year mortality risk and statins dramatically lowered 5-year mortality throughout all cohorts.
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.
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.
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.