A5103112046- Immunotherapy and Immune Responses
- vaccines and immunoinformatics approaches
- SARS-CoV-2 and COVID-19 Research
- Monoclonal and Polyclonal Antibodies Research
- Machine Learning in Bioinformatics
- Computational Drug Discovery Methods
- Sarcoma Diagnosis and Treatment
- COVID-19 Clinical Research Studies
- RNA and protein synthesis mechanisms
- Bacterial Identification and Susceptibility Testing
- Research Data Management Practices
- Genomics and Phylogenetic Studies
- Influenza Virus Research Studies
- Machine Learning and Data Classification
- Chemical Synthesis and Analysis
- Cancer Immunotherapy and Biomarkers
- Machine Learning and Algorithms
- Bayesian Modeling and Causal Inference
- Bioinformatics and Genomic Networks
- Pharmacy and Medical Practices
- Topic Modeling
- Tryptophan and brain disorders
- CAR-T cell therapy research
- Biomedical Text Mining and Ontologies
- Pharmaceutical Practices and Patient Outcomes
Oslo Cancer Cluster
2023
Sharp Laboratories of Europe (United Kingdom)
2020
Abstract The global population is at present suffering from a pandemic of Coronavirus disease 2019 (COVID-19), caused by the novel coronavirus Severe Acute Respiratory Syndrome 2 (SARS-CoV-2). goal this study was to use artificial intelligence (AI) predict blueprints for designing universal vaccines against SARS-CoV-2, that contain sufficiently broad repertoire T-cell epitopes capable providing coverage and protection across population. To help achieve these aims, we profiled entire...
Increasingly comprehensive characterization of cancer-associated genetic alterations has paved the way for development highly specific therapeutic vaccines. Predicting precisely binding and presentation peptides to major histocompatibility complex (MHC) alleles is an important step toward such therapies. Recent data suggest that both class I II epitopes are critical induction a sustained effective immune response. However, prediction performance MHC been limited compared I.We present...
Neoantigen vaccines make use of tumor-specific mutations to enable the patient's immune system recognize and eliminate cancer. Selecting vaccine elements, however, is a complex task which needs take into account not only underlying antigen presentation pathway but also tumor heterogeneity.
Poor overall survival of hematopoietic stem cell transplantation (HSCT) recipients who developed COVID-19 underlies the importance SARS-CoV-2 vaccination. Previous studies vaccine efficacy have reported weak humoral responses but conflicting results on T immunity. Here, we examined relationship between and response in 48 HSCT received two doses Moderna’s mRNA-1273 or Pfizer/BioNTech’s BNT162b2 vaccines. Nearly all patients had robust immunity regardless protective responses, with 18/48 (37%,...
During the COVID-19 pandemic we utilized an AI-driven T cell epitope prediction tool, NEC Immune Profiler (NIP) to scrutinize and predict regions of immunogenicity (hotspots) from entire SARS-CoV-2 viral proteome. These immunogenic offer potential for development universally protective vaccine candidates. Here, validated characterized responses a set minimal epitopes these AI-identified universal hotspots. Utilizing flow cytometry-based activation-induced marker (AIM) assay, identified 59...
Sarcomas are comprised of diverse bone and connective tissue tumors with few effective therapeutic options for locally advanced unresectable and/or metastatic disease. Recent advances in immunotherapy, particular immune checkpoint inhibition (ICI), have shown promising outcomes several cancer indications. Unfortunately, ICI therapy has provided only modest clinical responses seems moderately a subset the subtypes. To explore parameters governing resistance or escape, we performed whole exome...
The spread of multidrug resistant organisms (MDRO) is a global healthcare challenge. Nosocomial outbreaks caused by MDRO are an important contributor to this threat. Computer-based applications facilitating outbreak detection can be essential address issue. To allow application reusability across institutions, the various heterogeneous microbiology data representations needs transformed into standardised, unambiguous models. In work, we present multi-centric standardisation approach using...
Abstract Motivation Increasingly comprehensive characterisation of cancer associated genetic alteration has paved the way for development highly specific therapeutic vaccines. Predicting precisely binding and presentation peptides by MHC alleles is an important step towards such therapies. Recent data suggest that both class I II epitopes critical induction a sustained effective immune response. However, prediction performance been limited compared to I. Results We present transformer neural...
Abstract The global population is at present suffering from a pandemic of Coronavirus disease 2019 (COVID-19), caused by the novel coronavirus Severe Acute Respiratory Syndrome 2 (SARS-CoV-2). goals this study were to use artificial intelligence (AI) predict blueprints for designing universal vaccines against SARS-CoV-2, that contain sufficiently broad repertoire T-cell epitopes capable providing coverage and protection across population. To help achieve these aims, we profiled entire...
People who use drugs (PWUD) are at a high risk of contracting and developing severe coronavirus disease 2019 (COVID-19) other infectious diseases due to their lifestyle, comorbidities, the detrimental effects opioids on cellular immunity. However, there is limited research vaccine responses in PWUD, particularly regarding role that T cells play immune response acute respiratory syndrome 2 (SARS-CoV-2). Here, we show before vaccination, PWUD did not exhibit an increased frequency preexisting...
Abstract Motivation Word embedding approaches have revolutionized Natural Language Processing NLP research. These aim to map words a low-dimensional vector space in which with similar linguistic features are close the space. also preserve local features, such as analogy. Embedding-based been developed for proteins. To date, treat amino acids words, and proteins treated sentences of acids. evaluated either qualitatively, via visual inspection space, or extrinsically, performance on downstream...
Abstract This protocol predicts blueprints for vaccine design that contain a broad repertoire of T-cell epitopes optimized the global population. The first requires screening SARS-CoV-2 proteome using immunogenicity predictors to generate comprehensive epitope maps. Then, these maps are used as input Monte Carlo simulations designed identify statistically significant “epitope hotspot” regions in virus most likely be immunogenic. hotspots share homology with proteins human removed reduce...
Abstract The development of therapeutic cancer vaccines to immunize against tumor antigens constitutes a promising modality. Mutation associated are considered major targets given their specificity cells. These mutations specific the patients and require tailor-made vaccine targeting identified in each tumor. Many tumoral genome most patients, but only small fraction (around 1%) is suitable as target. Herein, we report data documenting prediction performance algorithm used for design TG4050,...
Abstract Sarcomas are comprised of diverse bone and connective tissue tumors with few effective therapeutic options for locally advanced unresectable and/or metastatic disease. Recent advances in immunotherapy, particular immune checkpoint inhibition (ICI), have shown promising outcomes several cancer indications. Unfortunately, ICI therapy has provided only modest clinical responses seems moderately a subset the subtypes. To explore parameters governing resistance or escape, we performed...
Abstract Motivation We propose a system that learns consistent representations of biological entities, such as proteins and diseases, based on knowledge graph additional data modalities, like structured annotations free text describing the entities. In contrast to similar approaches, we explicitly incorporate consistency into learning process. particular, use these identify novel associated with diseases; relationships could be used prioritize protein targets for new drugs. Results show our...
Abstract Background: Word embedding approaches have revolutionized natural language processing (NLP) research. These aim to map words a low-dimensional vector space, in which with similar linguistic features cluster together. Embedding-based methods also been developed for proteins, where are amino acids and sentences proteins. The learned embeddings evaluated qualitatively, via visual inspection of the space extrinsically, performance comparison on downstream protein prediction tasks....