Omics sciences revolutionized research in areas such as biology, biotechnology, medicine, and agri-food sciences. With the novelty of high-throughput techniques, researchers may encounter difficulties to manage and interpret the huge amount of data obtained. Bioinformatics and computational biology provide novel tools and data analysis approaches that, on the one hand, offer powerful solutions to the omics sciences, but on the other hand remain very difficult to use for researchers lacking specific training in computational tools and informatics. This appears particularly true when very sophisticated approaches are applied from the field of data science and artificial intelligence (AI), in particular, machine learning and statistical learning, and soft-computing approaches, such as deep neural networks or genetic algorithms.
Data science and AI approaches for the analysis of very complex and heterogeneous data, such as from multi-omics and inter-omics experiments, are more and more important in order to pave the way for novel concepts in, e.g., personalized medicine or novel biotechnological applications. Researchers from the field of bioinformatics and computational biology must collaborate in parallel with (bio-) medical/biotechnological and computer science focused communities in order to develop innovative and user-friendly bioinformatics tools in the era of big data.
This Research Topic welcomes articles presenting novel developments in the field of artificial intelligence in biology and medicine, and their applications to the analysis of high-throughput data from omics and inter-omics approaches.
Different type of articles can be published within this Research Topic: Original Research, Systematic Review, Mini-Review, Case Report, Hypothesis. Other types of articles can be proposed to the Topic Editors who will evaluate their suitability.
Omics sciences revolutionized research in areas such as biology, biotechnology, medicine, and agri-food sciences. With the novelty of high-throughput techniques, researchers may encounter difficulties to manage and interpret the huge amount of data obtained. Bioinformatics and computational biology provide novel tools and data analysis approaches that, on the one hand, offer powerful solutions to the omics sciences, but on the other hand remain very difficult to use for researchers lacking specific training in computational tools and informatics. This appears particularly true when very sophisticated approaches are applied from the field of data science and artificial intelligence (AI), in particular, machine learning and statistical learning, and soft-computing approaches, such as deep neural networks or genetic algorithms.
Data science and AI approaches for the analysis of very complex and heterogeneous data, such as from multi-omics and inter-omics experiments, are more and more important in order to pave the way for novel concepts in, e.g., personalized medicine or novel biotechnological applications. Researchers from the field of bioinformatics and computational biology must collaborate in parallel with (bio-) medical/biotechnological and computer science focused communities in order to develop innovative and user-friendly bioinformatics tools in the era of big data.
This Research Topic welcomes articles presenting novel developments in the field of artificial intelligence in biology and medicine, and their applications to the analysis of high-throughput data from omics and inter-omics approaches.
Different type of articles can be published within this Research Topic: Original Research, Systematic Review, Mini-Review, Case Report, Hypothesis. Other types of articles can be proposed to the Topic Editors who will evaluate their suitability.