RNA is a polymeric molecule essential in various biological roles. It is a kind of nucleic acid that constitutes one of the four major macromolecules essential for all known forms of life. Many viruses encode the genetic informatics using RNA genome that caused multiple epidemics or pandemics, such as influenza and coronavirus. Advances in virus detection and discovery methodologies could improve the understanding of the RNA virosphere. In addition, some RNA molecules play an active role with cells by interacting with other biological entities, controlling gene expression, or communicating responses to cellular signals. However, traditional wet-lab experiments are usually labor-intensive and time-consuming. There is an urgent need to develop computational models (e.g. machine learning and statistical methods) to analyze, integrate, and interpret RNA data for human diseases in diagnosis, treatment, and prognostics.
This Research Topic will group focus on the latest advances in applying and developing various kinds of computational methods, such as machine learning and statistical techniques, to analyze RNA data towards RNA viruses (e.g., influenza, coronavirus, Zika) and non-coding RNAs (e.g., miRNA, siRNA, piRNA). The analyses would explore the biological disease mechanism and facilitate the precaution, diagnosis, and treatment of human diseases. We hope to inspire computer scientist researchers and statisticians to develop efficient methods and tools specialized in gaining a better understanding of complex diseases. We encourage researchers working in these fields to use RNA data in a combination of different omics data to explore disease mechanisms from various perspectives, including but not limited to genomic data, transcriptomic data, proteomic data, metabolic data, medical images, and electronic medical records.
With massive RNA data available, there is a considerable desire to integrate, analyze and interpret these data through computational approaches. Thus, we think it is a great time to discuss the topics of advanced computational methods to analyze RNA data for human diseases. Now, we are launching a Research Topic aiming to provide a spotlight on current advances in the field. The areas to be covered in this Research Topic may include but are not limited to the following:
1. RNA virus bioinformatics
2. RNA virus genetics
3. RNA virus evolution
4. RNA virus discovery
5. RNA-disease association prediction
6. RNA-RNA interactions
7. RNA-protein interaction prediction
8. RNA-drug interactions
9. RNA biomarker detection
RNA is a polymeric molecule essential in various biological roles. It is a kind of nucleic acid that constitutes one of the four major macromolecules essential for all known forms of life. Many viruses encode the genetic informatics using RNA genome that caused multiple epidemics or pandemics, such as influenza and coronavirus. Advances in virus detection and discovery methodologies could improve the understanding of the RNA virosphere. In addition, some RNA molecules play an active role with cells by interacting with other biological entities, controlling gene expression, or communicating responses to cellular signals. However, traditional wet-lab experiments are usually labor-intensive and time-consuming. There is an urgent need to develop computational models (e.g. machine learning and statistical methods) to analyze, integrate, and interpret RNA data for human diseases in diagnosis, treatment, and prognostics.
This Research Topic will group focus on the latest advances in applying and developing various kinds of computational methods, such as machine learning and statistical techniques, to analyze RNA data towards RNA viruses (e.g., influenza, coronavirus, Zika) and non-coding RNAs (e.g., miRNA, siRNA, piRNA). The analyses would explore the biological disease mechanism and facilitate the precaution, diagnosis, and treatment of human diseases. We hope to inspire computer scientist researchers and statisticians to develop efficient methods and tools specialized in gaining a better understanding of complex diseases. We encourage researchers working in these fields to use RNA data in a combination of different omics data to explore disease mechanisms from various perspectives, including but not limited to genomic data, transcriptomic data, proteomic data, metabolic data, medical images, and electronic medical records.
With massive RNA data available, there is a considerable desire to integrate, analyze and interpret these data through computational approaches. Thus, we think it is a great time to discuss the topics of advanced computational methods to analyze RNA data for human diseases. Now, we are launching a Research Topic aiming to provide a spotlight on current advances in the field. The areas to be covered in this Research Topic may include but are not limited to the following:
1. RNA virus bioinformatics
2. RNA virus genetics
3. RNA virus evolution
4. RNA virus discovery
5. RNA-disease association prediction
6. RNA-RNA interactions
7. RNA-protein interaction prediction
8. RNA-drug interactions
9. RNA biomarker detection
