Titlebook: Bioinformatics Research and Applications; 20th International S Wei Peng,Zhipeng Cai,Pavel Skums Conference proceedings 2024 The Editor(s) (

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,scCoRR: A Data-Driven Self-correction Framework for Labeled scRNA-Seq Data,ised deep neural network is trained with cross-entropy loss and a contrastive regularization term to predict the types of the remaining cells. During this process, the labels of some cells are corrected from one cell type to another, a phenomenon that can also be elucidated from various biological perspectives.

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Conference proceedings 2024unming, China, in July 19–21, 2024...The 93 full papers  included in this book were carefully reviewed and selected from 236 submissions. The symposium provides a forum for the exchange of ideas and results among researchers, developers, and practitioners working on all aspects of bioinformatics and

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Exploring Hierarchical Structures of Cell Types in scRNA-seq Data,al structures that perform functions differently. Constructing a hierarchical structure of cell types is crucial for revealing relationships between cell types. Existing hierarchical methods construct cell type hierarchy with fixed branches which cannot reflect actual cell type hierarchy, and cannot

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,A Hybrid Feature Fusion Network for Predicting HER2 Status on H &E-Stained Histopathology Images, Breast cancer is the most common and lethal cancer among women worldwide, and about 25% of breast cancer patients have HER2 overexpression/amplification. At present, the commonly evaluating HER2 status methods are tissue-consuming and prone to analysis or interpretation errors. Therefore, this stud

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,scCoRR: A Data-Driven Self-correction Framework for Labeled scRNA-Seq Data, evaluation metrics within single-cell research are intertwined with cell labels. While annotating cell labels often requires prior biological knowledge for clustering, this is frequently approached from a clustering perspective rather than considering the heterogeneity of individual cells. Building