Publications
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eDNA-based detection as an early warning tool for detecting established and emerging invasive amphipods Research Institute for Nature and Forest
Giardia detection and codetection with other enteric pathogens in young children in the Vaccine Impact on Diarrhea in Africa (VIDA) Case-Control Study 2015-2018 Institute of Tropical Medicine
Background: Giardia has been associated with reduced risk of diarrhea in children in low-resource settings, but the mechanism underlying this association is unknown. To assess whether Giardia may shape colonization or infection with other enteric pathogens and impact associations with diarrhea, we examined Giardia and enteric pathogen codetection among children <5 years old in Kenya, The Gambia, and Mali as part of the Vaccine Impact on ...
Analysis of Miniaturization Effects and Channel Selection Strategies for EEG Sensor Networks With Application to Auditory Attention Detection KU Leuven
OBJECTIVE: Concealable, miniaturized electroencephalography (mini-EEG) recording devices are crucial enablers toward long-term ambulatory EEG monitoring. However, the resulting miniaturization limits the inter-electrode distance and the scalp area that can be covered by a single device. The concept of wireless EEG sensor networks (WESNs) attempts to overcome this limitation by placing a multitude of these mini-EEG devices at various scalp ...
Selection of universal peptide biomarkers for the detection of the allergen hazelnut in food trough a comprehensive, high resolution mass spectrometric (HRMS) based approach Research Institute for Agriculture, Fisheries and Food Ghent University Flanders Institute for Biotechnology
The interest of using LC-MS/MS as a method for detection of allergens in food is growing. In such methods, peptides are used as biomarkers for the detection and quantification of the allergens. The selection of good biomarker peptides is of high importance to develop a specific, universal and sensitive method. Biomarkers should, for example, be robust to food processing. To evaluate robustness, test material incurred with hazelnut having ...
Feature Selection Algorithm based on Random Forest applied to Sleep Apnea Detection KU Leuven
This paper presents a new feature selection method based on the changes in out-of-bag (OOB) Cohen kappa values of a random forest (RF) classifier, which was tested on the automatic detection of sleep apnea based on the oxygen saturation signal (SpO2). The feature selection method is based on the RF predictor importance defined as the increase in error when features are permuted. This method is improved by changing the classification error into ...
Ultra-Sensitive TP53 Sequencing for Cancer Detection Reveals Progressive Clonal Selection in Normal Tissue over a Century of Human Lifespan KU Leuven
High-accuracy next-generation DNA sequencing promises a paradigm shift in early cancer detection by enabling the identification of mutant cancer molecules in minimally invasive body fluid samples. We demonstrate 80% sensitivity for ovarian cancer detection using ultra-accurate Duplex Sequencing to identify TP53 mutations in uterine lavage. However, in addition to tumor DNA, we also detect low-frequency TP53 mutations in nearly all lavages from ...
Detecting long-lived autodependency changes in a multivariate system via change point detection and regime switching models. KU Leuven
Long-lived simultaneous changes in the autodependency of dynamic system variables characterize crucial events as epileptic seizures and volcanic eruptions and are expected to precede psychiatric conditions. To understand and predict such phenomena, methods are needed that detect such changes in multivariate time series. We put forward two methods: First, we propose KCP-AR, a novel adaptation of the general-purpose KCP (Kernel Change Point) ...
Detecting correlation changes in multivariate time series: A comparison of four non-parametric change point detection methods KU Leuven
Change point detection in multivariate time series is a complex task since next to the mean, the correlation structure of the monitored variables may also alter when change occurs. DeCon was recently developed to detect such changes in mean and\or correlation by combining a moving windows approach and robust PCA. However, in the literature, several other methods have been proposed that employ other non-parametric tools: E-divisive, Multirank, ...