Devising an image analyzer dedicated to the automatic quantification of immunohistochemical staining for clinical oncology implies developing a method for the delimitation of tumoral cell nests, setting aside tumoral stroma, while accounting for the topology of the staining. The representation of images by neighborhood graphs can bring an answer to both requirements. In this paper, a methodological approach is presented. It consists in a preliminary study dealing with nuclear immunostaining images of breast cancer. Segmentation of the graph structure allows to separate clusters of cancer cells and the analysis of this structure can account for the focal or diffuse aspect of the staining within the tumor.
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Neural Netw
December 2024
College of Science, North China University of Science and Technology, Tangshan, 063210, China. Electronic address:
The class imbalance problem is one of the difficult factors affecting the performance of traditional classifiers. The oversampling technique is the most common way to solve the class imbalance problem. They alleviate the performance impact of the class imbalance problem on traditional machine learning by augmenting minority instance feature representation.
View Article and Find Full Text PDFGenome Biol
January 2025
Department of Molecular, Cell and Developmental Biology, University of California, Los Angeles, 90095, CA, USA.
Deciphering the link between tissue architecture and function requires methods to identify and interpret patterns in spatial arrangement of cells. We present SMORE, an approach to detect patterns in sequential arrangements of cells and examine their associated gene expression specializations. Applied to retina, brain, and embryonic tissue maps, SMORE identifies novel spatial motifs, including one that offers a new mechanism of action for type 1b bipolar cells.
View Article and Find Full Text PDFComput Biol Chem
December 2024
School of Computer and Communication, Lanzhou University of Technology, Lanzhou 730050, China. Electronic address:
The metabolic level within an organism typically reflects its health status. Studying the relationship between human diseases and metabolites helps enhance medical professionals' ability for early disease diagnosis and risk prediction. However, traditional biological experimental methods often require substantial resources and manpower, and there is still room for improvement in the performance of existing predictive models.
View Article and Find Full Text PDFNeural Netw
December 2024
School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, China. Electronic address:
Graph Neural Networks (GNNs) have garnered significant attention for their success in learning the representation of homophilic or heterophilic graphs. However, they cannot generalize well to real-world graphs with different levels of homophily. In response, the Poisson-Charlier Network (PCNet) (Li et al.
View Article and Find Full Text PDFJ Chem Inf Model
January 2025
Department of Computer Science and Technology, Shantou University, Shantou 515063, China.
The human microbiota may influence the effectiveness of drug therapy by activating or inactivating the pharmacological properties of drugs. Computational methods have demonstrated their ability to screen reliable microbe-drug associations and uncover the mechanism by which drugs exert their functions. However, the previous prediction methods failed to completely exploit the neighborhood topologies of the microbe and drug entities and the diverse correlations between the microbe-drug entity pair and the other entities.
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