Natali, Eriberto

Natali, Eriberto


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  • Publikation
    RWD-Cockpit. Application for quality assessment of real-world data
    (JMIR Publications, 18.10.2022) Degen, Markus; Babrak, Lmar; Smakaj, Erand; Agac, Teyfik; Asprion, Petra; Grimberg, Frank; Van der Werf, Daan; Van Ginkel, Erwin Willem; Tosoni, Deniz David; Clay, Ieuan; Brodbeck, Dominique; Natali, Eriberto; Schkommodau, Erik; Miho, Enkelejda [in: JMIR Formative Research]
    Digital technologies are transforming the health care system. A large part of information is generated as real-world data (RWD). Data from electronic health records and digital biomarkers have the potential to reveal associations between the benefits and adverse events of medicines, establish new patient-stratification principles, expose unknown disease correlations, and inform on preventive measures. The impact for health care payers and providers, the biopharmaceutical industry, and governments is massive in terms of health outcomes, quality of care, and cost. However, a framework to assess the preliminary quality of RWD is missing, thus hindering the conduct of population-based observational studies to support regulatory decision-making and real-world evidence.
    01A - Beitrag in wissenschaftlicher Zeitschrift
  • Publikation
    Machine learning detects anti-DENV signatures in antibody repertoire sequences
    (Frontiers, 11.10.2021) Horst, Alexander; Smakaj, Erand; Natali, Eriberto; Tosoni, Deniz David; Babrak, Lmar; Meier, Patrick; Miho, Enkelejda [in: Frontiers in Artificial Intelligence]
    Dengue infection is a global threat. As of today, there is no universal dengue fever treatment or vaccines unreservedly recommended by the World Health Organization. The investigation of the specific immune response to dengue virus would support antibody discovery as therapeutics for passive immunization and vaccine design. High-throughput sequencing enables the identification of the multitude of antibodies elicited in response to dengue infection at the sequence level. Artificial intelligence can mine the complex data generated and has the potential to uncover patterns in entire antibody repertoires and detect signatures distinctive of single virus-binding antibodies. However, these machine learning have not been harnessed to determine the immune response to dengue virus. In order to enable the application of machine learning, we have benchmarked existing methods for encoding biological and chemical knowledge as inputs and have investigated novel encoding techniques. We have applied different machine learning methods such as neural networks, random forests, and support vector machines and have investigated the parameter space to determine best performing algorithms for the detection and prediction of antibody patterns at the repertoire and antibody sequence levels in dengue-infected individuals. Our results show that immune response signatures to dengue are detectable both at the antibody repertoire and at the antibody sequence levels. By combining machine learning with phylogenies and network analysis, we generated novel sequences that present dengue-binding specific signatures. These results might aid further antibody discovery and support vaccine design.
    01A - Beitrag in wissenschaftlicher Zeitschrift
  • Publikation
    Prospective artificial intelligence to dissect the dengue immune response and discover therapeutics
    (Frontiers, 15.06.2021) Natali, Eriberto; Babrak, Lmar; Miho, Enkelejda [in: frontiers in immunology]
    Dengue virus (DENV) poses a serious threat to global health as the causative agent of dengue fever. The virus is endemic in more than 128 countries resulting in approximately 390 million infection cases each year. Currently, there is no approved therapeutic for treatment nor a fully efficacious vaccine. The development of therapeutics is confounded and hampered by the complexity of the immune response to DENV, in particular to sequential infection with different DENV serotypes (DENV1–5). Researchers have shown that the DENV envelope (E) antigen is primarily responsible for the interaction and subsequent invasion of host cells for all serotypes and can elicit neutralizing antibodies in humans. The advent of high-throughput sequencing and the rapid advancements in computational analysis of complex data, has provided tools for the deconvolution of the DENV immune response. Several types of complex statistical analyses, machine learning models and complex visualizations can be applied to begin answering questions about the B- and T-cell immune responses to multiple infections, antibody-dependent enhancement, identification of novel therapeutics and advance vaccine research.
    01A - Beitrag in wissenschaftlicher Zeitschrift