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DeepTRIAGE: interpretable and individualised biomarker scores using attention mechanism for the classification of breast cancer sub-types

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conference contribution
posted on 2020-01-01, 00:00 authored by Adham BeykikhoshkAdham Beykikhoshk, Thomas Quinn, Samuel Lee, Truyen TranTruyen Tran, Svetha VenkateshSvetha Venkatesh
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.

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 important each gene is in predicting the cancer sub-type for each sample. We then perform a principal component analysis of these biomarker scores to visualise the sample heterogeneity, and use a linear model to test whether the major principal axes associate with known clinical phenotypes.

Our model not only classifies cancer sub-types with good accuracy, but simultaneously assigns each patient their own set of interpretable and individualised biomarker scores. These personalised scores describe how important each feature is in the classification of any patient, and can be analysed post-hoc to generate new hypotheses about latent heterogeneity.

We apply the DeepTRIAGE framework to classify the gene expression signatures of luminal A and luminal B breast cancer sub-types, and illustrate its use for genes as well as the GO and KEGG gene sets. Using DeepTRIAGE, we calculate personalised biomarker scores that describe the most important features for classifying an individual patient as luminal A or luminal B. In doing so, DeepTRIAGE simultaneously reveals heterogeneity within the luminal A biomarker scores that significantly associate with tumour stage, placing all luminal samples along a continuum of severity.



Genome Informatics & Australian Bioinformatics and Computational Biology Society Annual Conference. Joint International Conference (2019 : 30th : Sydney, N.S.W.)




Supplement 3


BMC Medical Genomics


Proceedings of the Joint International GIW & ABACBS-2019 Conference: medical genomics (part 2)


1 - 10




Sydney, N.S.W.

Place of publication

London, Eng.

Start date


End date






Publication classification

E1 Full written paper - refereed

Title of proceedings

GIW/ABACBS 2019 : Proceedings of the Joint 30th International Conference on Genome Informatics (GIW) & Australian Bioinformatics and Computational Biology Society (ABACBS) Annual Confernence