Since the term “artificial intelligence (AI)” was formally proposed in 1956 at the Dartmouth conference, it was rapidly developed and derived various branches, such as machine/deep learning. It can learn laws from data and improve the system itself through computational algorithms. The applications of machine learning include natural language processing, image recognition and data mining, among others.
In Biology, the advancement of high-throughput technologies has enabled rapid accumulation of omics data, including sequencing data from the genome, epigenome, proteome, metabolome, as well as the phenome. The dimensions of biological data are usually heterogeneous, and the relationship between omics data is far from linear, making it difficult to decode the underlying molecular interactions. AI-based methodologies, such as machine learning and deep learning approaches, allow for the handling of such big and complex data and the capturing of nonlinear and hierarchical features. Implementations of AI-based omics data analysis have helped researchers understand the underlying biology, and industries to increase productivity – known as precision medicine in humans and precision breeding in agriculture.
This Research Topic aims to collect findings related to AI application in biological omics data analysis, AI-based method development and evaluation, as well as reviews or perspectives regarding future use of AI methods in omics data analysis in all life science. We welcome submissions including Original Research, Methods, Protocols, Reviews, Reports and Perspectives in the following (but not limited to) sub-themes:
• Development of AI-based approaches for omics data analysis;
• Tutorials or protocols for explaining the use of AI-based methods;
• Benchmark studies for comparatively evaluating existing AI software in omics data analysis;
• Application of AI-related methods to interpret Omics data
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
Since the term “artificial intelligence (AI)” was formally proposed in 1956 at the Dartmouth conference, it was rapidly developed and derived various branches, such as machine/deep learning. It can learn laws from data and improve the system itself through computational algorithms. The applications of machine learning include natural language processing, image recognition and data mining, among others.
In Biology, the advancement of high-throughput technologies has enabled rapid accumulation of omics data, including sequencing data from the genome, epigenome, proteome, metabolome, as well as the phenome. The dimensions of biological data are usually heterogeneous, and the relationship between omics data is far from linear, making it difficult to decode the underlying molecular interactions. AI-based methodologies, such as machine learning and deep learning approaches, allow for the handling of such big and complex data and the capturing of nonlinear and hierarchical features. Implementations of AI-based omics data analysis have helped researchers understand the underlying biology, and industries to increase productivity – known as precision medicine in humans and precision breeding in agriculture.
This Research Topic aims to collect findings related to AI application in biological omics data analysis, AI-based method development and evaluation, as well as reviews or perspectives regarding future use of AI methods in omics data analysis in all life science. We welcome submissions including Original Research, Methods, Protocols, Reviews, Reports and Perspectives in the following (but not limited to) sub-themes:
• Development of AI-based approaches for omics data analysis;
• Tutorials or protocols for explaining the use of AI-based methods;
• Benchmark studies for comparatively evaluating existing AI software in omics data analysis;
• Application of AI-related methods to interpret Omics data
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.