情爱
发表于 2025-3-23 10:14:14
,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.
Spirometry
发表于 2025-3-23 17:11:22
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CRUDE
发表于 2025-3-23 18:27:20
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
唤起
发表于 2025-3-23 23:57:39
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FOVEA
发表于 2025-3-24 04:51:04
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个人长篇演说
发表于 2025-3-24 08:59:03
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
该得
发表于 2025-3-24 11:45:36
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recession
发表于 2025-3-24 17:55:23
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客观
发表于 2025-3-24 21:36:02
,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
一回合
发表于 2025-3-25 00:09:08
,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