A5041984163- Bioinformatics and Genomic Networks
- Gene expression and cancer classification
- Computational Drug Discovery Methods
- Genomic variations and chromosomal abnormalities
- Genomics and Phylogenetic Studies
- Cancer Genomics and Diagnostics
- Genomics and Rare Diseases
- Machine Learning in Bioinformatics
- Biomedical Text Mining and Ontologies
- Prenatal Screening and Diagnostics
- Genomics and Chromatin Dynamics
- Neural Networks and Applications
- RNA and protein synthesis mechanisms
- Genetics, Bioinformatics, and Biomedical Research
- Silicon and Solar Cell Technologies
- Single-cell and spatial transcriptomics
- Advanced Graph Neural Networks
- Photonic and Optical Devices
- Chromosomal and Genetic Variations
- Semiconductor Lasers and Optical Devices
- Gene Regulatory Network Analysis
- RNA Research and Splicing
- Multiple Sclerosis Research Studies
- Epigenetics and DNA Methylation
- Machine Learning in Materials Science
KU Leuven
2016-2025
Dynamic Systems (United States)
2019-2024
VIB-KU Leuven Center for Cancer Biology
2024
Hasselt University
2019-2023
The University of Melbourne
2021-2022
University of Tasmania
2021-2022
Charles University
2021
Hospital Universitari Germans Trias i Pujol
2021
Hospital Universitario Virgen Macarena
2021
Ospedale Garibaldi
2021
Summary:biomaRt is a new Bioconductor package that integrates BioMart data resources with analysis software in Bioconductor. It can annotate wide range of gene or product identifiers (e.g. Entrez-Gene and Affymetrix probe identifiers) information such as symbol, chromosomal coordinates, Gene Ontology OMIM annotation. Furthermore biomaRt enables retrieval genomic sequences single nucleotide polymorphism information, which be used analysis. Fast up-to-date possible the executes direct SQL...
Inferring a Gene Regulatory Network (GRN) from gene expression data is computationally expensive task, exacerbated by increasing sizes due to advances in high-throughput profiling technology, such as single-cell RNA-seq. To equip researchers with toolset infer GRNs large datasets, we propose GRNBoost2 and the Arboreto framework. an efficient algorithm for regulatory network inference using gradient boosting, based on GENIE3 architecture. computational framework that scales up GRN algorithms...
Abstract Motivation: Transcriptome analysis allows detection and clustering of genes that are coexpressed under various biological circumstances. Under the assumption coregulated share cis-acting regulatory elements, it is important to investigate upstream sequences controlling transcription these genes. To improve robustness Gibbs sampling algorithm noisy data sets we propose an extension this for motif finding with a higher-order background model. Results: Simulated real well-described...
Abstract Cardiovascular disease associated with metabolic syndrome has a high prevalence, but the mechanistic basis of cardiomyopathy remains poorly understood. We characterised cardiac transcriptome in murine (MetS) model (LDLR−/−; ob/ob, DKO) relative to healthy, control heart (C57BL/6, WT) and transcriptional changes induced by ACE-inhibition those hearts. RNA-Seq, differential gene expression transcription factor analysis identified 288 genes differentially expressed between DKO WT...
Personalized, precision, P4, or stratified medicine is understood as a medical approach in which patients are based on their disease subtype, risk, prognosis, treatment response using specialized diagnostic tests. The key idea to base decisions individual patient characteristics, including molecular and behavioral biomarkers, rather than population averages. Personalized deeply connected dependent data science, specifically machine learning (often named Artificial Intelligence the mainstream...
Abstract Motivation: Clinical data, such as patient history, laboratory analysis, ultrasound parameters—which are the basis of day-to-day clinical decision support—are often underused to guide management cancer in presence microarray data. We propose a strategy based on Bayesian networks treat and data an equal footing. The main advantage this probabilistic model is that it allows integrate these sources several ways investigate understand structure parameters. Furthermore using concept...
Patients with genetic disease of unknown causes can be rapidly diagnosed by bioinformatic analysis disease-associated DNA sequences and phenotype.
<h3>Background and Objectives</h3> People with multiple sclerosis (MS) are a vulnerable group for severe coronavirus disease 2019 (COVID-19), particularly those taking immunosuppressive disease-modifying therapies (DMTs). We examined the characteristics of COVID-19 severity in an international sample people MS. <h3>Methods</h3> Data from 12 data sources 28 countries were aggregated (sources could include patients 1–12 countries). Demographic (age, sex), clinical (MS phenotype, disability),...
Federated multipartner machine learning has been touted as an appealing and efficient method to increase the effective training data volume thereby predictivity of models, particularly when generation is resource-intensive. In landmark MELLODDY project, indeed, each ten pharmaceutical companies realized aggregated improvements on its own classification or regression models through federated learning. To this end, they leveraged a novel implementation extending multitask across partners,...
Microarray experiments can reveal important information about transcriptional regulation. In our case, we look for potential promoter regulatory elements in the upstream region of coexpressed genes. Here present two modifications original Gibbs sampling algorithm motif finding (Lawrence et al., 1993). First, introduce use a probability distribution to estimate number copies sequence. Second, describe technical aspects incorporation higher-order background model whose application discussed...
Microarray transcript profiling and RNA interference are two new technologies crucial for large-scale gene function studies in multicellular eukaryotes. Both rely on sequence-specific hybridization between complementary nucleic acid strands, inciting us to create a collection of gene-specific sequence tags (GSTs) representing at least 21,500 Arabidopsis genes which compatible with both approaches. The GSTs were carefully selected ensure that each them shared no significant similarity any...
Gibbs sampling has become a method of choice for the discovery noisy patterns, known as motifs, in DNA and protein sequences. Because handling noise microarray data presents similar challenges, we have adapted this strategy to biclustering discretized data.In contrast with standard clustering that reveals genes behave similarly over all conditions, groups only subset conditions which those sharp probability distribution. We opted simple probabilistic model biclusters because it key advantage...
This paper presents a novel optimized kernel k-means algorithm (OKKC) to combine multiple data sources for clustering analysis. The uses an alternating minimization framework optimize the cluster membership and coefficients as nonconvex problem. In proposed algorithm, problem are all based on same Rayleigh quotient objective; therefore converges locally. OKKC has simpler procedure lower complexity than other algorithms in literature. Simulated real-life fusion applications experimentally...
E ndeavour ( http://www.esat.kuleuven.be/endeavourweb ; this web site is free and open to all users there no login requirement) a resource for the prioritization of candidate genes. Using training set genes known be involved in biological process interest, our approach consists (i) inferring several models (based on various genomic data sources), (ii) applying each model rank those candidates against profile (iii) merging rankings into global ranking In present article, we describe latest...
Background Housekeeping genes are needed in every tissue as their expression is required for survival, integrity or duplication of cell. commonly have been used reference to normalize gene data, the underlying assumption being that they expressed cell type at approximately same level. Often, terms “reference genes” and “housekeeping interchangeably. In this paper, we would like distinguish between these terms. Consensus growing housekeeping which traditionally data not good genes. Recently,...
Genomic imbalances are a major cause of constitutional and acquired disorders. Therefore, aneuploidy screening has become the cornerstone preimplantation, prenatal postnatal genetic diagnosis, as well routine aspect diagnostic workup many Recently, array comparative genomic hybridization (array CGH) been introduced rapid high-resolution method for detection both benign disease-causing copy-number variations. Until now, CGH performed using significant quantity DNA derived from pool cells....
Abstract Motivation: Microarray experiments generate a considerable amount of data, which analyzed properly help us gain huge biologically relevant information about the global cellular behaviour. Clustering (grouping genes with similar expression profiles) is one first steps in data analysis high-throughput measurements. A number clustering algorithms have proved useful to make sense such data. These classical algorithms, though useful, suffer from several drawbacks (e.g. they require...