DeepTRIAGE: interpretable and individualised biomarker scores using attention mechanism for the classification of breast cancer sub-types
深度分类:使用注意机制对乳腺癌亚型分类的可解释和个性化生物标记评分
作者: Adham BeykikhoshkThomas P. QuinnSamuel C. LeeTruyen TranSvetha Venkatesh
作者单位: 1Centre for Pattern Recognition and Data Analytics, Deakin University, Geelong, Australia
刊名: BMC Medical Genomics, 2020, Vol.13 (43), pp.531-7
来源数据库: Springer Nature Journal
DOI: 10.1186/s12920-020-0658-5
关键词: Breast cancerPrecision medicineTCGADeep learning
英文摘要: Abstract(#br)Background(#br)Breast cancer is a collection of multiple tissue pathologies, each with a distinct molecular signature that correlates with patient prognosis and response to therapy. Accurately differentiating between breast cancer sub-types is an important part of clinical decision-making. Although this problem has been addressed using machine learning methods in the past, there remains unexplained heterogeneity within the established sub-types that cannot be resolved by the commonly used classification algorithms. Methods(#br)In this paper, we propose a novel deep learning architecture, called DeepTRIAGE (Deep learning for the TRactable Individualised Analysis of Gene Expression), which uses an attention mechanism to obtain personalised biomarker scores that describe how...
全文获取路径: Springer Nature  (合作)
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影响因子:3.466 (2012)

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