About this Research Topic
This Research Topic focuses on the use of computational approaches to:
1) Process multiplexed imaging data to extract biologically and clinically relevant information of tissues
2) Use multiplexed imaging data for models or training algorithms
3) Develop spatial statistics to describe biological process, and/or
4) Develop methods to characterize cell types, tissue regions, or disease states.
5) Extract cell-cell interactions critical for immunological or pathological projects.
Some examples include:
1) The application of machine learning principles to image processing, cell segmentation, or even identification of cell types.
2) Agent-based or multiscale modeling approaches.
3) Spatial statistics and descriptions of multicellular units
4) Data compression or presentation formats for multiplexed images
5) Extraction and quantification of non-cellular components like the extracellular matrix
6) Integration with other datasets on the same tissue such as single cell RNA sequencing
7) Application of graph-based tools to understand nodes and connections in the system
8) 3D reconstruction from 2D images
9) integration of multiple imaging modalities
10) New batch correction methods, and
11) The role of morphological analysis in cell-type identification.
12) Establishing methods for taking into account the mobility of immune cells in fixed tissues.
Statement: C.M.S. is a scientific advisor to, has stock options in, and has received research funding from Enable Medicine, Inc.
Keywords: Multiplexed Imaging, Machine learning, single-cell, bioinformatics, modeling, immunology
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