Viruses and the human immune system are in a constant state of warfare. While our immune system utilizes different strategies to overcome viral onslaughts, several challenges continue to beset the host response, as well as global scientific actuation against these pathogens. Rapid viral mutation, evolution, molecular tactics for immune evasion, and elusive pathophysiologies are some common obstacles. The recent pandemic clearly demonstrated the need for integrative research efforts to combat the problem of viruses. Modern computational techniques allow for high-throughput, high-resolution, low-cost insights complementing wet lab research. In conjunction with the voluminous information that is available through improved clinical and experimental techniques, it has spurred the need for converting this data to useful knowledge.
Robust application of machine learning (ML) techniques to harness available data is key to solving challenges in molecular virology. This Research Topic aims to bring together interdisciplinary research that utilizes deep learning algorithms to elucidate the underpinnings of viral biosciences. Employment of molecular, cellular, structural, genomics, and systems biology information will be brought under the umbrella of a single comprehensive compendium. In-depth understanding of disease progression in terms of infection and pathogenesis, improved detection techniques, vaccine development, and advanced therapeutic strategies will be achieved, paving a molecular way forward for combating viruses. This cross-disciplinary research collection is also expected to provide improved perception of emerging viruses, leading to better pandemic preparedness for the future.
This Research Topic encourages manuscripts addressing all areas of molecular virology with the incorporation of ML techniques, in the form of original research, reviews or perspectives. Specific topics include, but are not limited to:
• Interactions between viral and host biomolecules
• Molecular detection techniques
• Structural determination of viral proteins
• Relation of structure and dynamics with biomolecular functions
• Infection mechanism and viral pathogenesis
• Molecular mechanisms for regulating host immune response
• Cellular signals that regulate viral transcription and replication
• Bioinformatics driven analysis of viral genomes and evolution.
• Host-factor mediated treatment strategies
• Immunogen development and modern therapeutic designs
• Insights into viral mutations and emerging strains to facilitate high transmissibility
• Development of Prediction Servers and algorithms in the above topics
We are looking for novel ML-driven method development as well as equitable applications of existing ML algorithms. Validation with experimental evidence is required for Frontiers in Molecular Biosciences .
Viruses and the human immune system are in a constant state of warfare. While our immune system utilizes different strategies to overcome viral onslaughts, several challenges continue to beset the host response, as well as global scientific actuation against these pathogens. Rapid viral mutation, evolution, molecular tactics for immune evasion, and elusive pathophysiologies are some common obstacles. The recent pandemic clearly demonstrated the need for integrative research efforts to combat the problem of viruses. Modern computational techniques allow for high-throughput, high-resolution, low-cost insights complementing wet lab research. In conjunction with the voluminous information that is available through improved clinical and experimental techniques, it has spurred the need for converting this data to useful knowledge.
Robust application of machine learning (ML) techniques to harness available data is key to solving challenges in molecular virology. This Research Topic aims to bring together interdisciplinary research that utilizes deep learning algorithms to elucidate the underpinnings of viral biosciences. Employment of molecular, cellular, structural, genomics, and systems biology information will be brought under the umbrella of a single comprehensive compendium. In-depth understanding of disease progression in terms of infection and pathogenesis, improved detection techniques, vaccine development, and advanced therapeutic strategies will be achieved, paving a molecular way forward for combating viruses. This cross-disciplinary research collection is also expected to provide improved perception of emerging viruses, leading to better pandemic preparedness for the future.
This Research Topic encourages manuscripts addressing all areas of molecular virology with the incorporation of ML techniques, in the form of original research, reviews or perspectives. Specific topics include, but are not limited to:
• Interactions between viral and host biomolecules
• Molecular detection techniques
• Structural determination of viral proteins
• Relation of structure and dynamics with biomolecular functions
• Infection mechanism and viral pathogenesis
• Molecular mechanisms for regulating host immune response
• Cellular signals that regulate viral transcription and replication
• Bioinformatics driven analysis of viral genomes and evolution.
• Host-factor mediated treatment strategies
• Immunogen development and modern therapeutic designs
• Insights into viral mutations and emerging strains to facilitate high transmissibility
• Development of Prediction Servers and algorithms in the above topics
We are looking for novel ML-driven method development as well as equitable applications of existing ML algorithms. Validation with experimental evidence is required for Frontiers in Molecular Biosciences .