A5054476841- Bioinformatics and Genomic Networks
- Single-cell and spatial transcriptomics
- Biomedical Text Mining and Ontologies
- Gene Regulatory Network Analysis
- Topic Modeling
- Complex Network Analysis Techniques
- Gene expression and cancer classification
- Computational Drug Discovery Methods
- Natural Language Processing Techniques
- Data Visualization and Analytics
- Microbial Metabolic Engineering and Bioproduction
- Genomics and Phylogenetic Studies
- Nutrition, Genetics, and Disease
- Advanced Proteomics Techniques and Applications
- Tryptophan and brain disorders
- Web visibility and informetrics
- Image Retrieval and Classification Techniques
- Ethics in medical practice
- Advanced Radiotherapy Techniques
- 3D Surveying and Cultural Heritage
- Cell Image Analysis Techniques
- Image Processing and 3D Reconstruction
- Histone Deacetylase Inhibitors Research
- Genetics, Bioinformatics, and Biomedical Research
- Epigenetics and DNA Methylation
International Commission on Radiological Protection
2025
Newcastle upon Tyne Hospitals NHS Foundation Trust
2025
Novo Nordisk Foundation
2021-2024
University of Copenhagen
2021-2024
Foundation Center
2024
Alexander Fleming Biomedical Sciences Research Center
2020-2023
Much of the complexity within cells arises from functional and regulatory interactions among proteins. The core these is increasingly known, but novel continue to be discovered, information remains scattered across different database resources, experimental modalities levels mechanistic detail. STRING (https://string-db.org/) systematically collects integrates protein-protein interactions-both physical as well associations. data originate a number sources: automated text mining scientific...
Abstract Proteins cooperate, regulate and bind each other to achieve their functions. Understanding the complex network of interactions is essential for a systems-level description cellular processes. The STRING database compiles, scores integrates protein–protein association information drawn from experimental assays, computational predictions prior knowledge. Its goal create comprehensive objective global networks that encompass both physical functional interactions. Additionally, provides...
Protein networks are commonly used for understanding how proteins interact. However, they typically biased by data availability, favoring well-studied with more interactions. To uncover functions of understudied proteins, we must use that not affected this literature bias, such as single-cell RNA-seq and proteomics. Due to sparseness redundancy, functional association analysis becomes complex.
Neurodegenerative and neuropsychiatric diseases impose a significant societal public health burden. However, our understanding of the molecular mechanisms underlying these highly complex conditions remains limited. To gain deeper insights into etiology different brain diseases, we used specimens from 1,494 unique donors to generate population-scale single-cell transcriptomic atlas human dorsolateral prefrontal cortex (DLPFC), comprising over 6.3 million individual nuclei. The cohort includes...
Abstract Motivation Despite lifestyle factors (LSFs) being increasingly acknowledged in shaping individual health trajectories, particularly chronic diseases, they have still not been systematically described the biomedical literature. This is part because no named entity recognition (NER) system exists, which can comprehensively detect all types of LSFs text. The task challenging due to their inherent diversity, lack a comprehensive LSF classification for dictionary-based NER, and corpus...
Dictionary-based named entity recognition (NER) allows terms to be detected in a corpus and normalized biomedical databases ontologies. However, adaptation different types requires new high-quality dictionaries associated lists of blocked names for each type. The latter are so far created by identifying cases that cause many false positives through manual inspection individual names, process scales poorly.
Abstract Motivation Despite lifestyle factors (LSFs) being increasingly acknowledged in shaping individual health trajectories, particularly chronic diseases, they have still not been systematically described the biomedical literature. This is part because no named entity recognition (NER) system exists, which can comprehensively detect all types of LSFs text. The task challenging due to their inherent diversity, lack a comprehensive LSF classification for dictionary-based NER, and corpus...
Abstract Spatial transcriptomics enables analysis of gene expression that is spatially resolved within a tissue section, making it possible to elucidate the relationship between individual cells context tissue. This transformative technology researchers better understand function health tissue, developmental processes, and disease. In this study, we present an innovative spatial data using high-resolution DNA chip with total capture region size 6.5 x mm containing 2 µm features for barcoding...
The Network Makeup Artist (NORMA) is a web tool for interactive network annotation visualization and topological analysis, able to handle multiple networks annotations simultaneously. Precalculated (e.g., Gene Ontology, Pathway enrichment, community detection, or clustering results) can be uploaded visualized in network, either as colored pie-chart nodes color-filled areas 2D/3D Venn-diagram-like style. In the case where no exists, algorithms automated detection are offered. Users adjust...
Abstract NORMA is a web tool for interactive network annotation visualization and topological analysis, able to handle multiple networks annotations simultaneously. Precalculated (e.g. Gene Ontology/Pathway enrichment or clustering results) can be uploaded visualized in either as colored pie-chart nodes color-filled convex hulls Venn-diagram-like style. In the case where no exists, algorithms automated community detection are offered. Users adjust views using standard layout allow slightly...
Abstract Protein networks are commonly used for understanding how proteins interact. However, they typically biased by data availability, favoring well-studied with more interactions. To uncover functions of understudied proteins, we must use that not affected this literature bias, such as single-cell RNA-seq and proteomics. Due to sparseness redundancy, co-expression analysis becomes complex. address this, have developed FAVA (Functional Associations using Variational Autoencoders), which...
Network biology is a dominant player in today's multi-omics era. Therefore, the need for visualization tools which can efficiently cope with intra-network heterogeneity emerges.NORMA-2.0 web application uses efficient layouts to group together areas of interest network. In this version, NORMA-2.0 utilizes three different strategies make such groupings as distinct possible while it preserves all properties from its first version where one handle multiple networks and annotation files...
Abstract Data visualization is essential to discover patterns and anomalies in large high‐dimensional datasets. New dimensionality reduction techniques have thus been developed for visualizing omics data, particular from single‐cell studies. However, jointly showing several types of example, expression gene networks, remains a challenge. Here, we present ‘U‐CIE, method that encodes arbitrary data as colors using combination the CIELAB color space retain original structure extent possible....
In this article we present the Network Analysis Profiler (NAP v2.0), a web tool to directly compare topological features of multiple networks simultaneously. NAP is written in R and Shiny currently offers both 2D 3D network visualisation, as well simultaneous visual comparisons node- edge-based bar charts or scatterplot matrix. fully interactive, users can easily export visualise intersection between any pair using Venn diagrams multi-layer graph-based visualisation. supports weighted,...
Abstract Motivation Despite significant progress in biomedical information extraction, there is a lack of resources for Named Entity Recognition (NER) and Normalization (NEN) protein-containing complexes. Current inadequately address the recognition complex names across different organisms, underscoring crucial need dedicated corpus. Results We introduce Complex Corpus (CoNECo), an annotated corpus NER NEN CoNECo comprises 1,621 documents with 2,052 entities, 1,976 which are normalized to...
Despite significant progress in biomedical information extraction, there is a lack of resources for Named Entity Recognition (NER) and Normalization (NEN) protein-containing complexes. Current inadequately address the recognition complex names across different organisms, underscoring crucial need dedicated corpus.
Abstract Linkage-specific ubiquitin chains dictate the functional outcome of numerous critical ubiquitin-dependent signaling processes. However, functions and targets several poly-ubiquitin topologies remain poorly defined due to a paucity tools for their specific detection manipulation. To remedy this knowledge gap, we applied cell-based replacement strategy enabling targeted conditional abrogation each seven lysine-based chain types in human cells profile system-wide impacts disabling...
Abstract Motivation Dictionary-based named entity recognition (NER) allows terms to be detected in a corpus and normalized biomedical databases ontologies. However, adaptation different types requires new high-quality dictionaries associated lists of blocked names for each type. The latter are so far created by identifying cases that cause many false positives through manual inspection individual names, process scales poorly. Results In this work we aim improve block automatically block,...