A5091508214- Computational Drug Discovery Methods
- Bioinformatics and Genomic Networks
- HIV Research and Treatment
- Immune Cell Function and Interaction
- T-cell and B-cell Immunology
- Biomedical Text Mining and Ontologies
- Advanced Graph Neural Networks
- SARS-CoV-2 and COVID-19 Research
- Tuberculosis Research and Epidemiology
- Histone Deacetylase Inhibitors Research
- Peptidase Inhibition and Analysis
- Machine Learning in Healthcare
- Genomics and Rare Diseases
- Chemical Synthesis and Analysis
- Asymmetric Hydrogenation and Catalysis
- Scientific Computing and Data Management
- COVID-19 diagnosis using AI
- Cholinesterase and Neurodegenerative Diseases
- vaccines and immunoinformatics approaches
- HIV/AIDS Research and Interventions
- Artificial Intelligence in Healthcare
- Metabolomics and Mass Spectrometry Studies
- Topic Modeling
- Bayesian Modeling and Causal Inference
- Phosphodiesterase function and regulation
Fraunhofer Institute for Algorithms and Scientific Computing
2020-2024
University Hospital Cologne
2024
University of Duisburg-Essen
2016-2019
Walter Reed Army Institute of Research
2014-2016
Henry M. Jackson Foundation
2014-2016
The COVID-19 crisis has elicited a global response by the scientific community that led to burst of publications on pathophysiology virus. However, without coordinated efforts organize this knowledge, it can remain hidden away from individual research groups. By extracting and formalizing knowledge in structured computable form, as form graph, researchers readily reason analyze information much larger scale. Here, we present Knowledge Graph, an expansive cause-and-effect network constructed...
During chronic HIV infection, immune cells become increasingly dysfunctional and exhausted. Little is known about how functions are restored after initiation of antiretroviral therapy (ART). In this study, we assessed cellular metabolic activity evaluated the effect individual antiretrovirals on subsets ex vivo in ART-treated treatment-naive chronically HIV-infected individuals. We observed that respiration was significantly decreased most infection. The correlated to activation inhibitory...
ABSTRACT CD4 + T cells play a pivotal role in the control of chronic viral infections. Recently, nontraditional cell functions beyond helper effects have been described, and for cytolytic HIV infection has suggested. We define here transcriptional, phenotypic, functional profiles HIV-specific cells. Fluidigm BioMark multiparameter flow cytometric analysis revealed distinct transcriptional signature compared to Th1 but shared similar features with CD8 Furthermore, showed comparable killing...
ABSTRACT Effector CD4 T cell responses have been shown to be critically involved in the containment and clearance of viral pathogens. However, their involvement pathogenesis HIV infection is less clear, given additional role as preferred targets. We previously demonstrated that presence HIV-specific somewhat associated with control specific functions, such direct cytolytic activity, can contribute viremia. little known about how induction during acute influences disease progression whether...
The high number of failed pre-clinical and clinical studies for compounds targeting Alzheimer disease (AD) has demonstrated that there is a need to reassess existing strategies. Here, we pursue holistic, mechanism-centric drug repurposing approach combining computational analytics experimental screening data. Based on this integrative workflow, identified 77 druggable modifiers tau phosphorylation (pTau). One the upstream modulators pTau, HDAC6, was screened with 5,632 drugs in tau-specific...
The maturation process of high-affinity antibodies is a result intricate interactions between B cells and follicular helper T (Tfh) occurring in lymphoid germinal centers. HIV infection induces significant chronic immune activation, phenotypic skewing, inflammation driven by years continuous viral replication. High levels viremia as well activation dysfunction have been demonstrated to perturbing impact on the cell memory compartment contribute exhaustion. Counterintuitively, factors...
Abstract Summary The past few weeks have witnessed a worldwide mobilization of the research community in response to novel coronavirus (COVID-19). This global has led burst publications on pathophysiology virus, yet without coordinated efforts organize this knowledge, it can remain hidden away from individual groups. By extracting and formalizing knowledge structured computable form, as form graph, researchers readily reason analyze information much larger scale. Here, we present COVID-19...
Biomedical knowledge graphs (KGs) hold valuable information regarding biomedical entities such as genes, diseases, biological processes, and drugs. KGs have been successfully employed in challenging areas the identification of pathophysiology mechanisms or drug repurposing. The creation high-quality typically requires labor-intensive multi-database integration substantial human expert curation, both which take time contribute to workload data processing annotation. Therefore, use automatic...
The SARS-CoV-2 pandemic has challenged researchers at a global scale. scientific community's massive response resulted in flood of experiments, analyses, hypotheses, and publications, especially the field drug repurposing. However, many proposed therapeutic compounds obtained from specific assays are not agreement thus demonstrate need for singular source COVID-19 related information which rational selection repurposing candidates can be made. In this paper, we present PHARMACOME,...
Abstract Contextual information is widely considered for NLP and knowledge discovery in life sciences since it highly influences the exact meaning of natural language. The scientific challenge not only to extract such context data, but also store this data further query approaches. Classical approaches use RDF triple stores, which have serious limitations. Here, we propose a multiple step graph approach using labeled property graphs based on polyglot persistence systems utilize mining,...
Acute HIV-1 infection is characterized by high viremia and massive depletion of CD4+ T cells throughout all tissue compartments. During this time the latent viral reservoir established but dynamics memory cell subset development, their infectability influence on disease progression during acute has not been carefully described. We therefore investigated populations in RV217 (ECHO) cohort phase infection. Interestingly, while we found only small changes central or effector compartments,...
Contextual information is widely considered for NLP and knowledge discovery in life sciences since it highly influences the exact meaning of natural language. The scientific challenge not only to extract such context data, but also store this data further query approaches. Here, we propose a multiple step graph approach using labeled property graphs based on polyglot persistence systems utilize mining, queries, extraction. We introduce graph-theoretic foundation general concept within...
Abstract Motivation Epilepsy is a multifaceted complex disorder that requires precise understanding of the classification, diagnosis, treatment and disease mechanism governing it. Although scattered resources are available on epilepsy, comprehensive structured knowledge missing. In contemplation to promote multidisciplinary exchange facilitate advancement in clinical management, especially pre-clinical research, disease-specific ontology necessary. The presented designed enable better...
To date, six vaccine strategies have been evaluated in clinical trials for their efficacy at inducing protective immune responses against HIV infection. However, only the ALVAC-HIV/AIDSVAX B/E (RV144 trial) has demonstrated protection, albeit modestly (31%; P = 0.03). One potential correlate of protection was a low-frequency HIV-specific CD4 T cell population with diverse functionality. Although cells, particularly follicular helper (Tfh) are critical effective antibody responses, most...
Modeling biological mechanisms is a key for disease understanding and drug-target identification. However, formulating quantitative models in the field of Alzheimer’s Disease challenged by lack detailed knowledge relevant biochemical processes. Additionally, fitting differential equation systems usually requires time resolved data possibility to perform intervention experiments, which difficult neurological disorders. This work addresses these challenges employing recently published...
Abstract In addition to vaccines, the World Health Organization sees novel medications as an urgent matter fight ongoing COVID-19 pandemic. One possible strategy is identify target proteins, for which a perturbation by existing compound likely benefit patients. order contribute this effort, we present GuiltyTargets-COVID-19 ( https://guiltytargets-covid.eu/ ), machine learning supported web tool candidate drug targets. Using six bulk and three single cell RNA-Seq datasets, together with lung...
Biomedical knowledge graphs (KG) have become crucial for describing biological findings in a structured manner. To keep up with the constantly changing flow of knowledge, their embedded information must be regularly updated latest findings. Natural language processing (NLP) has created new possibilities automating this upkeep by facilitating extraction from free text. However, due to annotated and labeled biomedical data limitations, development completely autonomous systems remains...
Abstract Motivation A global medical crisis like the coronavirus disease 2019 (COVID-19) pandemic requires interdisciplinary and highly collaborative research from all over world. One of key challenges for is a lack interoperability among various heterogeneous data sources. Interoperability, standardization mapping datasets are necessary analysis applications in advanced algorithms such as developing personalized risk prediction modeling. Results To ensure compatibility COVID-19 datasets, we...
Abstract Motivation Drug repurposing is gaining interest due to its high cost-effectiveness, low risks, and improved patient outcomes. However, most drug methods depend on drug-disease-target semantic connections of a single rather than insights from combination data. In this study, we propose SynDRep, novel tool based enriching knowledge graphs (KG) with effects. It predicts the synergistic partner commonly prescribed for target disease, leveraging graph embedding machine learning...
Abstract The SARS-CoV-2 pandemic has challenged researchers at a global scale. scientific community’s massive response resulted in flood of experiments, analyses, hypotheses, and publications, especially the field drug repurposing. However, many proposed therapeutic compounds obtained from specific assays are not agreement thus demonstrate need for singular source COVID-19 related information which rational selection repurposing candidates can be made. In this paper, we present PHARMACOME,...
Abstract Motivation: Epilepsy is a multi-faceted complex disorder that requires precise understanding of the classification, diagnosis, treatment, and disease mechanism governing it. Although scattered resources are available on epilepsy, comprehensive structured knowledge missing. In contemplation to promote multidisciplinary exchange facilitate advancement in clinical management, especially pre-clinical research, disease-specific ontology necessary. The presented designed enable better...
Abstract Modeling biological mechanisms is a key for disease understanding and drug-target identification. However, formulating quantitative models in the field of Alzheimer’s Disease challenged by lack detailed knowledge relevant biochemical processes. Additionally, fitting differential equation systems usually requires time resolved data possibility to perform intervention experiments, which difficult neurological disorders. This work addresses these challenges employing recently published...
Senior researcher Vanessa Lage-Rupprecht and two collaborators talk about what data science means to them illustrate how they managed create a lab coexistence in their drug-repurposing project, which was recently published Patterns. In this article, have developed drug-target-mechanism-oriented model, Human Brain PHARMACOME, presented it as resource the community. Lage-Rupprecht: At Fraunhofer SCAI Bioinformatics, I use my neuroscientific expertise generation of formalized knowledge graphs...