Publications by authors named "Fangjiang Wu"

Accurate whole-cell segmentation is essential in various biomedical applications, particularly in studying the tumor microenvironment. Despite advancements in machine learning for nuclei segmentation in hematoxylin and eosin (H&E)-stained images, there remains a need for effective whole-cell segmentation methods. This study aimed to develop a deep learning-based pipeline to automatically segment cells in H&E-stained tissues, thereby advancing the capabilities of pathological image analysis.

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Cell-cell communication (CCC) is essential to how life forms and functions. However, accurate, high-throughput mapping of how expression of all genes in one cell affects expression of all genes in another cell is made possible only recently through the introduction of spatially resolved transcriptomics (SRT) technologies, especially those that achieve single-cell resolution. Nevertheless, substantial challenges remain to analyze such highly complex data properly.

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Profiling the binding of T cell receptors (TCRs) of T cells to antigenic peptides presented by MHC proteins is one of the most important unsolved problems in modern immunology. Experimental methods to probe TCR-antigen interactions are slow, labor-intensive, costly, and yield moderate throughput. To address this problem, we developed pMTnet-omni, an Artificial Intelligence (AI) system based on hybrid protein sequence and structure information, to predict the pairing of TCRs of αβ T cells with peptide-MHC complexes (pMHCs).

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Article Synopsis
  • Cell-cell communication (CCC) is crucial for understanding how organisms function, and new spatially resolved transcriptomics technologies (SRTs) enable detailed mapping of gene interactions at the single-cell level, though data complexity remains a challenge.* -
  • The spacia framework utilizes a Bayesian multi-instance learning approach to detect CCCs, overcoming limitations of existing analytical tools by maintaining single-cell resolution and considering multiple senders and receivers.* -
  • Spacia's application across various single-cell SRT technologies revealed important insights into cellular behavior in cancer, such as the roles of different immune cells in prostate cancer and their correlation with patient outcomes.*
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Microscopic examination of pathology slides is essential to disease diagnosis and biomedical research. However, traditional manual examination of tissue slides is laborious and subjective. Tumor whole-slide image (WSI) scanning is becoming part of routine clinical procedures and produces massive data that capture tumor histologic details at high resolution.

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While experimental and informatic techniques around single cell sequencing (scRNA-seq) are advanced, research around mass cytometry (CyTOF) data analysis has severely lagged behind. CyTOF data are notably different from scRNA-seq data in many aspects. This calls for the evaluation and development of computational methods specific for CyTOF data.

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The ability to predict B cell epitopes is critical for biomedical research and many clinical applications. Investigators have observed the phenomenon of T-B reciprocity, in which candidate B cell epitopes with nearby CD4 T cell epitopes have higher chances of being immunogenic. To our knowledge, existing B cell epitope prediction algorithms have not considered this interesting observation.

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