Results 1-20 of 139.
((uid:50003033))
![]() Vega Moreno, Carlos Gonzalo ![]() ![]() Poster (2022, September 20) Longitudinal clinical research studies require conducting various assessments over long periods of time. Such assessments comprise numerous stages, requiring different resources defined by ... [more ▼] Longitudinal clinical research studies require conducting various assessments over long periods of time. Such assessments comprise numerous stages, requiring different resources defined by multidisciplinary research staff and aligned with available infrastructure and equipment, altogether constrained by time. While it is possible to manage the allocation of resources manually, it is complex and error-prone. Efficient multi-appointment scheduling is essential to assist clinical teams, ensuring high participant retention and producing successful clinical studies, directly impacting patient throughput and satisfaction. We present Smart Scheduling (SMASCH) system [1], a web application for multi-appointment scheduling management aiming to reduce times, optimise resources and secure personal identifiable information. SMASCH facilitates clinical research and integrated care programs in Luxembourg, providing features to better manage multi-appointment scheduling problems (MASPs) characteristic of longitudinal clinical research studies and speed up management tasks. It is present in multiple clinical research and integrated care programs in Luxembourg since 2017, including Dementia Prevention Program, the study for Mild Cognitive Impairment and gut microbiome, and the National Centre of Excellence in Research on Parkinson’s disease [2] which encompasses the study for REM sleep behaviour disorder and the Luxembourg Parkinson’s Study. SMASCH is a free and open-source solution available both as a Linux package and Docker image. [less ▲] Detailed reference viewed: 54 (2 UL)![]() Vega Moreno, Carlos Gonzalo ![]() ![]() ![]() in Bioinformatics and Biomedical Engineering (2022) The ever increasing use of artificial intelligence (AI) methods in biomedical sciences calls for closer inter-disciplinary collaborations that transfer the domain knowledge from life scientists to ... [more ▼] The ever increasing use of artificial intelligence (AI) methods in biomedical sciences calls for closer inter-disciplinary collaborations that transfer the domain knowledge from life scientists to computer science researchers and vice-versa. We highlight two general areas where the use of AI-based solutions designed for clinical and laboratory settings has proven problematic. These are used to demonstrate common sources of translational challenges that often stem from the differences in data interpretation between the clinical and research view, and the unmatched expectations and requirements on the result quality metrics. We outline how explicit interpretable inference reporting might be used as a guide to overcome such translational challenges. We conclude with several recommendations for safer translation of machine learning solutions into real-world settings. [less ▲] Detailed reference viewed: 50 (5 UL)![]() Vega Moreno, Carlos Gonzalo ![]() ![]() ![]() in JAMIA open (2022), 5(2), 038 OBJECTIVE: Facilitate the multi-appointment scheduling problems (MASPs) characteristic of longitudinal clinical research studies. Additional goals include: reducing management time, optimizing clinical ... [more ▼] OBJECTIVE: Facilitate the multi-appointment scheduling problems (MASPs) characteristic of longitudinal clinical research studies. Additional goals include: reducing management time, optimizing clinical resources, and securing personally identifiable information. MATERIALS AND METHODS: Following a model view controller architecture, we developed a web-based tool written in Python 3. RESULTS: Smart Scheduling (SMASCH) system facilitates clinical research and integrated care programs in Luxembourg, providing features to better manage MASPs and speed up management tasks. It is available both as a Linux package and Docker image (https://smasch.pages.uni.lu). DISCUSSION: The long-term requirements of longitudinal clinical research studies justify the employment of flexible and well-maintained frameworks and libraries through an iterative software life-cycle suited to respond to rapidly changing scenarios. CONCLUSIONS: SMASCH is a free and open-source scheduling system for clinical studies able to satisfy recent data regulations providing features for better data accountability. Better scheduling systems can help optimize several metrics that ultimately affect the success of clinical studies. [less ▲] Detailed reference viewed: 48 (2 UL)![]() Kratochvil, Miroslav ![]() ![]() in Bioinformatics (2021) COBREXA.jl is a Julia package for scalable, high-performance constraint-based reconstruction and analysis of very large-scale biological models. Its primary purpose is to facilitate the integration of ... [more ▼] COBREXA.jl is a Julia package for scalable, high-performance constraint-based reconstruction and analysis of very large-scale biological models. Its primary purpose is to facilitate the integration of modern high performance computing environments with the processing and analysis of large-scale metabolic models of challenging complexity. We report the architecture of the package, and demonstrate how the design promotes analysis scalability on several use-cases with multi-organism community models.https://doi.org/10.17881/ZKCR-BT30.Supplementary data are available at Bioinformatics online. [less ▲] Detailed reference viewed: 73 (8 UL)![]() ; ; et al in Cardiovascular Research (2021) The coronavirus disease 2019 (COVID-19) pandemic has been as unprecedented as unexpected, affecting more than 105 million people worldwide as of 8 February 2020 and causing more than 2.3 million deaths ... [more ▼] The coronavirus disease 2019 (COVID-19) pandemic has been as unprecedented as unexpected, affecting more than 105 million people worldwide as of 8 February 2020 and causing more than 2.3 million deaths according to the World Health Organization (WHO). Not only affecting the lungs but also provoking acute respiratory distress, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is able to infect multiple cell types including cardiac and vascular cells. Hence a significant proportion of infected patients develop cardiac events, such as arrhythmias and heart failure. Patients with cardiovascular comorbidities are at highest risk of cardiac death. To face the pandemic and limit its burden, health authorities have launched several fast-track calls for research projects aiming to develop rapid strategies to combat the disease, as well as longer-term projects to prepare for the future. Biomarkers have the possibility to aid in clinical decision-making and tailoring healthcare in order to improve patient quality of life. The biomarker potential of circulating RNAs has been recognized in several disease conditions, including cardiovascular disease. RNA biomarkers may be useful in the current COVID-19 situation. The discovery, validation, and marketing of novel biomarkers, including RNA biomarkers, require multi-centre studies by large and interdisciplinary collaborative networks, involving both the academia and the industry. Here, members of the EU-CardioRNA COST Action CA17129 summarize the current knowledge about the strain that COVID-19 places on the cardiovascular system and discuss how RNA biomarkers can aid to limit this burden. They present the benefits and challenges of the discovery of novel RNA biomarkers, the need for networking efforts, and the added value of artificial intelligence to achieve reliable advances. [less ▲] Detailed reference viewed: 111 (0 UL)![]() ; ; et al in Nucleic Acids Research (2021) Since 1992 PredictProtein (https://predictprotein.org) is a one-stop online resource for protein sequence analysis with its main site hosted at the Luxembourg Centre for Systems Biomedicine (LCSB) and ... [more ▼] Since 1992 PredictProtein (https://predictprotein.org) is a one-stop online resource for protein sequence analysis with its main site hosted at the Luxembourg Centre for Systems Biomedicine (LCSB) and queried monthly by over 3,000 users in 2020. PredictProtein was the first Internet server for protein predictions. It pioneered combining evolutionary information and machine learning. Given a protein sequence as input, the server outputs multiple sequence alignments, predictions of protein structure in 1D and 2D (secondary structure, solvent accessibility, transmembrane segments, disordered regions, protein flexibility, and disulfide bridges) and predictions of protein function (functional effects of sequence variation or point mutations, Gene Ontology (GO) terms, subcellular localization, and protein-, RNA-, and DNA binding). PredictProtein's infrastructure has moved to the LCSB increasing throughput; the use of MMseqs2 sequence search reduced runtime five-fold (apparently without lowering performance of prediction methods); user interface elements improved usability, and new prediction methods were added. PredictProtein recently included predictions from deep learning embeddings (GO and secondary structure) and a method for the prediction of proteins and residues binding DNA, RNA, or other proteins. PredictProtein.org aspires to provide reliable predictions to computational and experimental biologists alike. All scripts and methods are freely available for offline execution in high-throughput settings. [less ▲] Detailed reference viewed: 75 (1 UL)![]() Ostaszewski, Marek ![]() ![]() in Molecular systems biology (2021), 17(10), 10387 We need to effectively combine the knowledge from surging literature with complex datasets to propose mechanistic models of SARS-CoV-2 infection, improving data interpretation and predicting key targets ... [more ▼] We need to effectively combine the knowledge from surging literature with complex datasets to propose mechanistic models of SARS-CoV-2 infection, improving data interpretation and predicting key targets of intervention. Here, we describe a large-scale community effort to build an open access, interoperable and computable repository of COVID-19 molecular mechanisms. The COVID-19 Disease Map (C19DMap) is a graphical, interactive representation of disease-relevant molecular mechanisms linking many knowledge sources. Notably, it is a computational resource for graph-based analyses and disease modelling. To this end, we established a framework of tools, platforms and guidelines necessary for a multifaceted community of biocurators, domain experts, bioinformaticians and computational biologists. The diagrams of the C19DMap, curated from the literature, are integrated with relevant interaction and text mining databases. We demonstrate the application of network analysis and modelling approaches by concrete examples to highlight new testable hypotheses. This framework helps to find signatures of SARS-CoV-2 predisposition, treatment response or prioritisation of drug candidates. Such an approach may help deal with new waves of COVID-19 or similar pandemics in the long-term perspective. [less ▲] Detailed reference viewed: 110 (3 UL)![]() Welter, Danielle ![]() ![]() ![]() Poster (2020, November 27) When the COVID-19 pandemic hit in early 2020, a lot of research efforts were quickly redirected towards studies on SARS-CoV2 and COVID-19 disease, from the sequencing and assembly of viral genomes to the ... [more ▼] When the COVID-19 pandemic hit in early 2020, a lot of research efforts were quickly redirected towards studies on SARS-CoV2 and COVID-19 disease, from the sequencing and assembly of viral genomes to the elaboration of robust testing methodologies and the development of treatment and vaccination strategies. At the same time, a flurry of scientific publications around SARS-CoV-2 and COVID-19 began to appear, making it increasingly difficult for researchers to stay up-to-date with latest trends and developments in this rapidly evolving field. The BioKB platform is a pipeline which, by exploiting text mining and semantic technologies, helps researchers easily access semantic content of thousands of abstracts and full text articles. The content of the articles is analysed and concepts from a range of contexts, including proteins, species, chemicals, diseases and biological processes are tagged based on existing dictionaries of controlled terms. Co-occurring concepts are classified based on their asserted relationship and the resulting subject-relation-object triples are stored in a publicly accessible human- and machine-readable knowledge base. All concepts in the BioKB dictionaries are linked to stable, persistent identifiers, either a resource accession such as an Ensembl, Uniprot or PubChem ID for genes, proteins and chemicals, or an ontology term ID for diseases, phenotypes and other ontology terms. In order to improve COVID-19 related text mining, we extended the underlying dictionaries to include many additional viral species (via NCBI Taxonomy identifiers), phenotypes from the Human Phenotype Ontology (HPO), COVID-related concepts including clinical and laboratory tests from the COVID-19 ontology, as well as additional diseases (DO) and biological processes (GO). We also added all viral proteins found in UniProt and gene entries from EntrezGene to increase the sensitivity of the text mining pipeline to viral data. To date, BioKB has indexed over 270’000 sentences from 21’935 publications relating to coronavirus infections, with publications dating from 1963 to 2021, 3’863 of which were published this year. We are currently working to further refine the text mining pipeline by training it on the extraction of increasingly complex relations such as protein-phenotype relationships. We are also regularly adding new terms to our dictionaries for areas where coverage is currently low, such as clinical and laboratory tests and procedures and novel drug treatments. [less ▲] Detailed reference viewed: 144 (15 UL)![]() Kratochvil, Miroslav ![]() ![]() in GigaScience (2020), 9(11), Background: The amount of data generated in large clinical and phenotyping studies that use single-cell cytometry is constantly growing. Recent technological advances allow the easy generation of data ... [more ▼] Background: The amount of data generated in large clinical and phenotyping studies that use single-cell cytometry is constantly growing. Recent technological advances allow the easy generation of data with hundreds of millions of single-cell data points with >40 parameters, originating from thousands of individual samples. The analysis of that amount of high-dimensional data becomes demanding in both hardware and software of high-performance computational resources. Current software tools often do not scale to the datasets of such size; users are thus forced to downsample the data to bearable sizes, in turn losing accuracy and ability to detect many underlying complex phenomena. Results: We present GigaSOM.jl, a fast and scalable implementation of clustering and dimensionality reduction for flow and mass cytometry data. The implementation of GigaSOM.jl in the high-level and high-performance programming language Julia makes it accessible to the scientific community and allows for efficient handling and processing of datasets with billions of data points using distributed computing infrastructures. We describe the design of GigaSOM.jl, measure its performance and horizontal scaling capability, and showcase the functionality on a large dataset from a recent study. Conclusions: GigaSOM.jl facilitates the use of commonly available high-performance computing resources to process the largest available datasets within minutes, while producing results of the same quality as the current state-of-art software. Measurements indicate that the performance scales to much larger datasets. The example use on the data from a massive mouse phenotyping effort confirms the applicability of GigaSOM.jl to huge-scale studies. [less ▲] Detailed reference viewed: 107 (13 UL)![]() Vega Moreno, Carlos Gonzalo ![]() ![]() ![]() Scientific Conference (2020, September 04) Standardisation of biomedical knowledge into systems biology models is essential for the study of the biological function. However, biomedical knowledge curation is a laborious manual process aggravated ... [more ▼] Standardisation of biomedical knowledge into systems biology models is essential for the study of the biological function. However, biomedical knowledge curation is a laborious manual process aggravated by the ever increasing growth of biomedical literature. High quality curation currently relies on pathway databases where outsider participation is minimal. The increasing demand of systems biology knowledge presents new challenges regarding curation, calling for new collaborative functionalities to improve quality control of the review process. These features are missing in the current systems biology environment, whose tools are not well suited for an open community-based model curation workflow. On one hand, diagram editors such as CellDesigner or Newt provide limited annotation features. On the other hand, most popular text annotations tools are not aimed for biomedical text annotation or model curation. Detaching the model curation and annotation tasks from diagram editing improves model iteration and centralizes the annotation of such models with supporting evidence. In this vain, we present BioKC, a web-based platform for systematic quality-controlled collaborative curation and annotation of biomedical knowledge following the standard data model from Systems Biology Markup Language (SBML). [less ▲] Detailed reference viewed: 142 (6 UL)![]() Ostaszewski, Marek ![]() ![]() in Scientific Data (2020) Detailed reference viewed: 100 (5 UL)![]() Vega Moreno, Carlos Gonzalo ![]() ![]() ![]() in bioRxiv (2020) Curation of biomedical knowledge into standardised and inter-operable systems biology models is essential for studying complex biological processes. However, systems-level curation is a laborious manual ... [more ▼] Curation of biomedical knowledge into standardised and inter-operable systems biology models is essential for studying complex biological processes. However, systems-level curation is a laborious manual process, especially when facing ever increasing growth of domain literature. Currently, these systems-level curation efforts concentrate around dedicated pathway databases, with a limited input from the research community. The demand for systems biology knowledge increases with new findings demonstrating elaborate relationships between multiple molecules, pathways and cells. This new challenge calls for novel collaborative tools and platforms allowing to improve the quality and the output of the curation process. In particular, in the current systems biology environment, curation tools lack reviewing features and are not well suited for an open, community-based curation workflows. An important concern is the complexity of the curation process and the limitations of the tools supporting it. Currently, systems-level curation combines model-building with diagram layout design. However, diagram editing tools offer limited annotation features. On the other hand, text-oriented tools have insufficient capabilities representing and annotating relationships between biological entities. Separating model curation and annotation from diagram editing enables iterative and distributed building of annotated models. Here, we present BioKC (Biological Knowledge Curation), a web-based collaborative platform for the curation and annotation of biomedical knowledge following the standard data model from Systems Biology Markup Language (SBML).Competing Interest StatementThe authors have declared no competing interest. [less ▲] Detailed reference viewed: 227 (14 UL)![]() ; ; et al in Pediatric Obesity (2020) Background: Pharmacological treatment options for adolescents with obesity are very limited. Glucagon-like-peptide-1 (GLP-1) receptor-agonist could be a treatment option for adolescent obesity. Objective ... [more ▼] Background: Pharmacological treatment options for adolescents with obesity are very limited. Glucagon-like-peptide-1 (GLP-1) receptor-agonist could be a treatment option for adolescent obesity. Objective: To investigate the effect of exenatide extended-release on body-mass-index (BMI)- SDS as primary outcome, and glucose-metabolism, cardiometabolic risk factors liver steatosis, and other BMI metrics as secondary outcomes, and its safety and tolerability in adolescents with obesity. Methods: Six-months, randomized, double-blinded, parallel, placebo-controlled clinical trial in patients (n=44, 10-18 years, females n=22) with BMI-SDS>2.0 or age-adapted-BMI>30 kg/m² according to WHO were included. Patients received lifestyle intervention and were randomized to exenatide extended-release 2 mg (n=22) or placebo (n=22) sub-cutaneousinjections given once weekly. Oral-glucose-tolerance-tests (OGTT) were conducted at the beginning and end of the intervention. Results: Exenatide reduced (p<0.05) BMI-SDS (-0.09; -0.18, 0.00), % BMI 95th percentile (- 2.9%; -5.4, -0.3), weight (-3 kg; -5.8, -0.1), waist circumference (-3.2 cm; -5.8, -0.7), subcutaneous adipose tissue (-552 cm3; -989, -114), 2-hour-glucose during OGTT (-15.3 mg/dL; -27.5, -3.1), total cholesterol (11.6 mg/dL; -21.7, -1.5) and BMI (-0.83 kg/m²; -1.68, 0.01) without significant change in liver fat content (-1.36; -3.12, 0.4; p=0.06) in comparison to placebo. Safety and tolerability profiles were comparable to placebo with the exception of mild adverse events being more frequent in exenatide-treated patients. Conclusions: Treatment of adolescents with severe obesity with extended-release exenatide is generally well tolerated and leads to a modest reduction in BMI metrics and improvement in glucose tolerance and cholesterol. The study indicates that the treatment provides additional beneficial effects beyond BMI-reduction for the patient group. [less ▲] Detailed reference viewed: 36 (0 UL)![]() Becker, Regina ![]() ![]() ![]() in GigaScience (2019), 8(12), The new European legislation on data protection, namely, the General Data Protection Regulation (GDPR), has introduced comprehensive requirements for the documentation about the processing of personal ... [more ▼] The new European legislation on data protection, namely, the General Data Protection Regulation (GDPR), has introduced comprehensive requirements for the documentation about the processing of personal data as well as informing the data subjects of its use. GDPR’s accountability principle requires institutions, projects, and data hubs to document their data processings and demonstrate compliance with the GDPR. In response to this requirement, we see the emergence of commercial data-mapping tools, and institutions creating GDPR data register with such tools. One shortcoming of this approach is the genericity of tools, and their process-based model not capturing the project-based, collaborative nature of data processing in biomedical research.We have developed a software tool to allow research institutions to comply with the GDPR accountability requirement and map the sometimes very complex data flows in biomedical research. By analysing the transparency and record-keeping obligations of each GDPR principle, we observe that our tool effectively meets the accountability requirement.The GDPR is bringing data protection to center stage in research data management, necessitating dedicated tools, personnel, and processes. Our tool, DAISY, is tailored specifically for biomedical research and can help institutions in tackling the documentation challenge brought about by the GDPR. DAISY is made available as a free and open source tool on Github. DAISY is actively being used at the Luxembourg Centre for Systems Biomedicine and the ELIXIR-Luxembourg data hub. [less ▲] Detailed reference viewed: 296 (61 UL)![]() ; ; et al in Epilepsia Open (2019) Detailed reference viewed: 64 (2 UL)![]() ; Schneider, Reinhard ![]() in Alzheimer's and Dementia: the Journal of the Alzheimer's Association (2019) Multiple immunity biomarkers have been suggested as tracers of neuroinflammation in neurodegeneration. This study aimed to verify findings in cerebrospinal fluid (CSF) samples of Alzheimer's disease (AD ... [more ▼] Multiple immunity biomarkers have been suggested as tracers of neuroinflammation in neurodegeneration. This study aimed to verify findings in cerebrospinal fluid (CSF) samples of Alzheimer's disease (AD) and Parkinson's disease (PD) subjects from the network of the European, Innovative Medicines Initiative–funded project AETIONOMY. [less ▲] Detailed reference viewed: 95 (3 UL)![]() Hoksza, David ![]() ![]() ![]() in Bioinformatics (2019) SUMMARY: The complexity of molecular networks makes them difficult to navigate and interpret, creating a need for specialized software. MINERVA is a web platform for visualization, exploration and ... [more ▼] SUMMARY: The complexity of molecular networks makes them difficult to navigate and interpret, creating a need for specialized software. MINERVA is a web platform for visualization, exploration and management of molecular networks. Here, we introduce an extension to MINERVA architecture that greatly facilitates the access and use of the stored molecular network data. It allows to incorporate such data in analytical pipelines via a programmatic access interface, and to extend the platform's visual exploration and analytics functionality via plugin architecture. This is possible for any molecular network hosted by the MINERVA platform encoded in well-recognized systems biology formats. To showcase the possibilities of the plugin architecture, we have developed several plugins extending the MINERVA core functionalities. In the article, we demonstrate the plugins for interactive tree traversal of molecular networks, for enrichment analysis and for mapping and visualization of known disease variants or known adverse drug reactions to molecules in the network. AVAILABILITY AND IMPLEMENTATION: Plugins developed and maintained by the MINERVA team are available under the AGPL v3 license at https://git-r3lab.uni.lu/minerva/plugins/. The MINERVA API and plugin documentation is available at https://minerva-web.lcsb.uni.lu. [less ▲] Detailed reference viewed: 185 (6 UL)![]() Hoksza, David ![]() ![]() ![]() in Briefings in bioinformatics (2019) The understanding of complex biological networks often relies on both a dedicated layout and a topology. Currently, there are three major competing layout-aware systems biology formats, but there are no ... [more ▼] The understanding of complex biological networks often relies on both a dedicated layout and a topology. Currently, there are three major competing layout-aware systems biology formats, but there are no software tools or software libraries supporting all of them. This complicates the management of molecular network layouts and hinders their reuse and extension. In this paper, we present a high-level overview of the layout formats in systems biology, focusing on their commonalities and differences, review their support in existing software tools, libraries and repositories and finally introduce a new conversion module within the MINERVA platform. The module is available via a REST API and offers, besides the ability to convert between layout-aware systems biology formats, the possibility to export layouts into several graphical formats. The module enables conversion of very large networks with thousands of elements, such as disease maps or metabolic reconstructions, rendering it widely applicable in systems biology. [less ▲] Detailed reference viewed: 198 (3 UL)![]() ; ; Ostaszewski, Marek ![]() in Chemometrics and Intelligent Laboratory Systems (2019) Bioactive peptides from protein hydrolysates with antihypertensive properties have a great effect in health, which warrants their pharmaceutical use. Nevertheless, the process of their production may ... [more ▼] Bioactive peptides from protein hydrolysates with antihypertensive properties have a great effect in health, which warrants their pharmaceutical use. Nevertheless, the process of their production may affect their efficacy. In this study, we investigate the inhibitory activities of various hydrolysates on angiotensin-converting enzyme (ACE) in relation to the chemical diversity of corresponding bioactive peptides. This depends on the enzyme specificity and process conditions used for the production of hydrolysates. In order to mitigate the uncontrolled chemical alteration in bioactive peptides, we propose a computational approach using the random vector functional link (RVFL) network based on the sine-cosine algorithm (SCA) to find optimal processing parameters, and to predict the ACE inhibition activity. The SCA is used to determine the optimal configuration of RVFL, improving the prediction performance. The experimental results show that the performance measures of the proposed model are better than the state-of-the-art methods. [less ▲] Detailed reference viewed: 103 (1 UL)![]() Gu, Wei ![]() in BMC Bioinformatics (2019), 20(1), 164 For large international research consortia, such as those funded by the European Union’s Horizon 2020 programme or the Innovative Medicines Initiative, good data coordination practices and tools are ... [more ▼] For large international research consortia, such as those funded by the European Union’s Horizon 2020 programme or the Innovative Medicines Initiative, good data coordination practices and tools are essential for the successful collection, organization and analysis of the resulting data. Research consortia are attempting ever more ambitious science to better understand disease, by leveraging technologies such as whole genome sequencing, proteomics, patient-derived biological models and computer-based systems biology simulations. [less ▲] Detailed reference viewed: 250 (13 UL) |
||