A5032456819- COVID-19 Clinical Research Studies
- Neutrophil, Myeloperoxidase and Oxidative Mechanisms
- Blood disorders and treatments
- Gene expression and cancer classification
- Cancer Genomics and Diagnostics
- Single-cell and spatial transcriptomics
- RNA modifications and cancer
- Immune cells in cancer
- Bioinformatics and Genomic Networks
- Advanced biosensing and bioanalysis techniques
- Computational Drug Discovery Methods
- SARS-CoV-2 and COVID-19 Research
- Epigenetics and DNA Methylation
- Machine Learning in Materials Science
- Inflammasome and immune disorders
- SARS-CoV-2 detection and testing
- Neonatal Respiratory Health Research
- Ethics in Clinical Research
- Ferroptosis and cancer prognosis
- CRISPR and Genetic Engineering
- Histone Deacetylase Inhibitors Research
- Mass Spectrometry Techniques and Applications
- Genomics and Chromatin Dynamics
- Magnetism in coordination complexes
- RNA and protein synthesis mechanisms
German Center for Neurodegenerative Diseases
2021-2025
University of Bonn
2019-2021
Abstract Background The SARS-CoV-2 pandemic is currently leading to increasing numbers of COVID-19 patients all over the world. Clinical presentations range from asymptomatic, mild respiratory tract infection, severe cases with acute distress syndrome, failure, and death. Reports on a dysregulated immune system in call for better characterization understanding changes system. Methods In order dissect COVID-19-driven host responses, we performed RNA-seq whole blood cell transcriptomes...
Immune cells play a major role in the pathogenesis of COPD. Changes distribution and cellular functions immune cells, such as alveolar macrophages (AMs) neutrophils are well known; however, their transcriptional reprogramming contribution to pathophysiology COPD still not fully understood.To determine changes lipid metabolism cell type within bronchoalveolar lavage fluid, we analysed whole transcriptomes lipidomes sorted CD45+Lin-HLA-DR+CD66b-Autofluorescencehi AMs from controls patients.We...
Tumor-associated macrophages (TAMs) are frequently the most abundant immune cells in cancers and associated with poor survival. Here, we generated TAM molecular signatures from K14cre;Cdh1
Abstract With the cost/yield-ratio of drug development becoming increasingly unfavourable, recent work has explored machine learning to accelerate early stages process. Given current success deep generative models across domains, we here investigated their application property-based proposal new small molecules for development. Specifically, trained a latent diffusion model— DrugDiff —paired with predictor guidance generate novel compounds variety desired molecular properties. The...
As the number and complexity of transcriptomic datasets increase, there is a rising demand for accessible user-friendly analysis tools. Here, we present hCoCena (horizontal construction co-expression networks analysis), toolbox facilitating single dataset, as well joint multiple datasets. We describe steps workspace setup, formatting tables, data processing, network integration. then detail procedures gene clustering, set enrichment analysis, transcription factor analysis. For complete...
Abstract Autoencoders are frequently used to embed molecules for training of downstream deep learning models. However, evaluation the chemical information quality in latent spaces is lacking and model architectures often arbitrarily chosen. Unoptimized may not only negatively affect space but also increase energy consumption during training, making models unsustainable. We conducted systematic experiments better understand how autoencoder architecture affects reconstruction it can be...
SUMMARY The SARS-CoV-2 pandemic is currently leading to increasing numbers of COVID-19 patients all over the world. Clinical presentations range from asymptomatic, mild respiratory tract infection, severe cases with acute distress syndrome, failure, and death. Reports on a dysregulated immune system in calls for better characterization understanding changes system. Here, we profiled whole blood transcriptomes 39 10 control donors enabling data-driven stratification based molecular phenotype....
Transcriptome-based gene co-expression analysis has become a standard procedure for structured and contextualized understanding comparison of different conditions phenotypes. Since large study designs with broad variety are costly laborious, extensive comparisons hindered when utilizing only single dataset. Thus, there is an increased need tools that allow the integration multiple transcriptomic datasets subsequent joint analysis, which can provide more systematic co-functionality within...
Omics-based technologies are driving major advances in precision medicine, but efforts still required to consolidate their use drug discovery. In this work, we exemplify the of multi-omics support development 3-chloropiperidines, a new class candidate anticancer agents. Combined analyses transcriptome and chromatin accessibility elucidated mechanisms underlying sensitivity test Furthermore, implemented versatile strategy for integration RNA- ATAC-seq (Assay Transposase-Accessible Chromatin)...
Generative models trained with Differential Privacy (DP) are becoming increasingly prominent in the creation of synthetic data for downstream applications. Existing literature, however, primarily focuses on basic benchmarking datasets and tends to report promising results only elementary metrics relatively simple distributions. In this paper, we initiate a systematic analysis how DP generative perform their natural application scenarios, specifically focusing real-world gene expression data....
Generative models trained with Differential Privacy (DP) are becoming increasingly prominent in the creation of synthetic data for downstream applications. Existing literature, however, primarily focuses on basic benchmarking datasets and tends to report promising results only elementary metrics relatively simple distributions. In this paper, we initiate a systematic analysis how DP generative perform their natural application scenarios, specifically focusing real-world gene expression data....
With the cost/yield-ratio of drug development becoming increasingly unfavourable, recent work has explored machine learning to accelerate early stages process. Given current success deep generative models across domains, we here investigated their application property-based proposal new small molecules for development. Specifically, trained a latent diffusion model - DrugDiff paired with predictor guidance generate novel compounds variety desired molecular properties. The architecture was...
Tumor-associated macrophages (TAMs) are frequently the most abundant immune cells in murine and human cancers associated with poor survival. Here we generated TAM molecular signatures from K14cre;Cdh1flox/flox;Trp53flox/flox (KEP) MMTV-NeuT (NeuT) transgenic mice which resemble invasive lobular carcinoma (ILC) HER2+ tumors, respectively. Determination of TAM-specific breast cancer required relationship analysis healthy mammary tissue macrophages, since comparison other macrophage populations...
Abstract Omics-based technologies are driving major advances in precision medicine but efforts still required to consolidate their use drug discovery. In this work, we exemplify the of multi-omics support development 3-chloropiperidines (3-CePs), a new class candidate anticancer agents. Combined analyses transcriptome and chromatin accessibility elucidated mechanisms underlying sensitivity test Further, implemented versatile strategy for integration RNA-seq ATAC-seq data, able accelerate...