About this Research Topic
In recent years, a large number of high-throughput data have been acquired in the field of biology. Machine learning has been reported to be an effective tool for data analysis in a number of fields. Based on the above background, there are many reports on the use of these methods in the life sciences. However, machine learning in general is evaluated based on the prediction of new data, and does not always reveal biological knowledge hidden in the data. Also, in some international conferences on machine learning, only the novelty of the method is focused on, and not enough consideration is given to whether biological knowledge was actually obtained by the method.
In this project, we will feature research that focuses on using machine learning to gain biological insights hidden in data. In particular, we are looking for research that uses machine learning methods to discover knowledge related to the design principle of life phenomena, such as causal regulatory structures between individual molecules and genes, cell types and dynamics of differentiation and development, ecological relationships between cells and individuals, and mechanochemical properties in cells and tissues. We are looking for research examples that use machine learning methods to discover knowledge related to the design principle of life phenomena, such as the type of life, the dynamics of differentiation and development, ecological relationships between cells and individuals, and mechanochemical properties in cells and tissues:
• Network Inference (reaction network, metabolic network, Cell-to-cell interaction network)
• Cell type estimation, spatial structure estimation
• Estimation of differentiation and developmental lineage
• Estimating the structure of ecosystems (Metagenomics, Immuno-repertoire, Ecology)
• Mechanochemical properties (Forces, force fields, flow, and morphology in cells and tissues)
• Bioimaging data (time-lapse imaging, single-molecule imaging, 3D/4D imaging data, Raman imaging)
• Estimating the structure of ecosystems (Metagenomics, Immuno-repertoire, Ecology)
• Sequencing data (RNAseq, ChIP-seq, CAGE-seq, ATAC-seq, HiC-seq, etc.)
• Behavioral data of Cells and animals
In this project, we will feature research that focuses on using machine learning to gain biological insights hidden in data. In particular, we are looking for research that uses machine learning methods to discover knowledge related to the design principle of life phenomena, such as causal regulatory structures between individual molecules and genes, cell types and dynamics of differentiation and development, ecological relationships between cells and individuals, and mechanochemical properties in cells and tissues. We are looking for research examples that use machine learning methods to discover knowledge related to the design principle of life phenomena, such as the type of life, the dynamics of differentiation and development, ecological relationships between cells and individuals, and mechanochemical properties in cells and tissues:
• Network Inference (reaction network, metabolic network, Cell-to-cell interaction network)
• Cell type estimation, spatial structure estimation
• Estimation of differentiation and developmental lineage
• Estimating the structure of ecosystems (Metagenomics, Immuno-repertoire, Ecology)
• Mechanochemical properties (Forces, force fields, flow, and morphology in cells and tissues)
• Bioimaging data (time-lapse imaging, single-molecule imaging, 3D/4D imaging data, Raman imaging)
• Estimating the structure of ecosystems (Metagenomics, Immuno-repertoire, Ecology)
• Sequencing data (RNAseq, ChIP-seq, CAGE-seq, ATAC-seq, HiC-seq, etc.)
• Behavioral data of Cells and animals
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.
