TCGA-BRCA
PulseAugur coverage of TCGA-BRCA — every cluster mentioning TCGA-BRCA across labs, papers, and developer communities, ranked by signal.
4 day(s) with sentiment data
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New DAIF framework learns optimal data fusion for multimodal learning
Researchers have developed DAIF, a novel framework for multimodal supervised learning that adaptively determines how to fuse information from different data sources. Unlike existing methods that rely on pre-defined fusi…
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Pathology foundation models show real signal but not uniformly morphological
A new study published on arXiv investigates the effectiveness of pathology foundation models in identifying molecular signals within tissue morphology. Researchers found that while these models can predict gene expressi…
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Machine learning models benchmarked for breast cancer prediction using multi-omics data
Researchers have benchmarked several machine learning models for predicting Estrogen Receptor (ER) status in breast cancer using multi-omics data. The study found that RNA expression data provided the strongest predicti…
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New CGRL framework enhances whole-slide image classification in pathology
Researchers have developed a new framework called CGRL for improving whole-slide image classification in computational pathology. This method uses class-level concept prototypes derived from disease prompts to guide the…
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New PPLS framework offers calibrated uncertainty and improved accuracy
Researchers have developed a new framework for Probabilistic Partial Least Squares (PPLS) that addresses practical limitations in existing fitting pipelines. This framework combines noise pre-estimation, constrained lik…
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Study finds feature dimensionality more critical than model complexity for breast cancer classification
A new study published on arXiv evaluates machine learning models for classifying breast cancer subtypes using gene expression data from TCGA-BRCA. The research found that feature dimensionality significantly impacts cla…
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SCOUT transformer generates concept-grounded pathology reports from whole-slide images
Researchers have developed SCOUT, a novel multimodal transformer framework designed for generating concept-grounded pathology reports from whole-slide images. This approach integrates local histological patterns, whole-…
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Deep learning predicts breast cancer subtypes from pathology images
Researchers have developed a new deep learning framework to classify breast cancer subtypes using histopathology images, potentially reducing the need for costly molecular assays. The method employs a multi-objective pa…