Predicting
            BRCA
            mutation and stratifying targeted therapy response using multimodal learning: a multicenter study

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OCRA indexes peer-reviewed papers from PubMed and CrossRef, enriched with MeSH terms, author profiles, and citation metrics. Papers are categorized by cancer type, research method, and clinical relevance.

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Yes. Use the search bar to filter papers by MeSH term, author name, journal, keyword, or cancer type. Each paper includes linked MeSH terms and author profiles for further exploration.

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Bo Xu

doi:10.1080/07853890.2024.2399759

Predicting BRCA mutation and stratifying targeted therapy response using multimodal learning: a multicenter study

Bo Xu · 2024-09-11

The status of We included 1695 slides from 1417 patients with ovarian, breast, prostate, and pancreatic cancers across three independent cohorts. Using a self-attention mechanism, we constructed a multi-instance attention model (MIAM) to detect Across the four cancer types, MIAM-C outperformed the deep learning-based MIAM in identifying the The MIAM-C model accurately detected

Predicting BRCA mutation and stratifying targeted therapy response using multimodal learning: a multicenter study

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