The last decade has seen rapid development in the use of computational techniques at bulk tissue and single-cell level. However, our knowledge remains limited in this regard, and further progress is needed, especially in inflammatory and degenerative diseases. Controlling, modeling, or predicting cellular phenotype in this context using artificial intelligence (AI) will greatly improve the available in vitro, in situ, in vivo, and in silico methods, but also aid in the understanding of disease pathology and therapeutic efficiency. These methods not only have ramifications for our pathophysiological understanding of tissue function but are also important for advancing AI methods in cell culture, tissue explants, or in vivo for immunologically relevant characteristics of single cells, cell populations, and tissues to predict cell or tissue function.
This Research Topic aims to advance bio-image and data analysis machine learning for image-based cell phenotyping that can support and automate experimental decisions, even before performing downstream biological interpretation, and to identify inflammatory or inflammatory disease-related phenotypes or immune-related cell signatures to understand inflammatory models or diseases.
We encourage the submission of both original research, review, and mini-review articles pertaining to high throughput imaging and approaches that cover but are not limited to approaches that:
• Determine or predict events relevant to cell or tissue function, disease onset, progression, and diagnosis
• Determine, predict, or control cell phenotype or tissue function in the context of inflammation
• Determine or predict inflammatory or anti-inflammatory properties of cells and cell populations
• Identify predictive signatures of immune-mediator cells or cells affected by immune-mediator cells
• Identify molecular or cellular targets of immune cells
• Utilize extracellular cues, advanced image generation, and analyses for AI-based control and prediction of cell structural properties and phenotype
The last decade has seen rapid development in the use of computational techniques at bulk tissue and single-cell level. However, our knowledge remains limited in this regard, and further progress is needed, especially in inflammatory and degenerative diseases. Controlling, modeling, or predicting cellular phenotype in this context using artificial intelligence (AI) will greatly improve the available in vitro, in situ, in vivo, and in silico methods, but also aid in the understanding of disease pathology and therapeutic efficiency. These methods not only have ramifications for our pathophysiological understanding of tissue function but are also important for advancing AI methods in cell culture, tissue explants, or in vivo for immunologically relevant characteristics of single cells, cell populations, and tissues to predict cell or tissue function.
This Research Topic aims to advance bio-image and data analysis machine learning for image-based cell phenotyping that can support and automate experimental decisions, even before performing downstream biological interpretation, and to identify inflammatory or inflammatory disease-related phenotypes or immune-related cell signatures to understand inflammatory models or diseases.
We encourage the submission of both original research, review, and mini-review articles pertaining to high throughput imaging and approaches that cover but are not limited to approaches that:
• Determine or predict events relevant to cell or tissue function, disease onset, progression, and diagnosis
• Determine, predict, or control cell phenotype or tissue function in the context of inflammation
• Determine or predict inflammatory or anti-inflammatory properties of cells and cell populations
• Identify predictive signatures of immune-mediator cells or cells affected by immune-mediator cells
• Identify molecular or cellular targets of immune cells
• Utilize extracellular cues, advanced image generation, and analyses for AI-based control and prediction of cell structural properties and phenotype
