Improving the classification of neuropsychiatric conditions using gene ontology terms as features

Quinn, Thomas P.; Lee, Samuel C.; Venkatesh, Svetha; Nguyen, Thin

doi:10.1002/ajmg.b.32727

Cited by 5 publications

(3 citation statements)

References 42 publications

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“…This overlap is consistent with a broad literature supporting common pathway-level signatures across the widely heterogeneous population of ASD probands. If true, it may be advantageous to model pathway-level dysregulation directly, for example in machine learning applications Quinn et al (2018).…”

Section: Discussionmentioning

confidence: 99%

Solving for X: Evidence for sex‐specific autism biomarkers across multiple transcriptomic studies

Lee

Quinn

Lai

et al. 2018

American J of Med Genetics Pt B

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Autism spectrum disorder (ASD) is a markedly heterogeneous condition with a varied phenotypic presentation. Its high concordance among siblings, as well as its clear association with specific genetic disorders, both point to a strong genetic etiology. However, the molecular basis of ASD is still poorly understood, although recent studies point to the existence of sexspecific ASD pathophysiologies and biomarkers. Despite this, little is known about how exactly sex influences the gene expression signatures of ASD probands. In an effort to identify sexdependent biomarkers and characterise their function, we present an analysis of a single pairedend post-mortem brain RNA-Seq data set and a meta-analysis of six blood-based microarray data sets. Here, we identify several genes with sex-dependent dysregulation, and many more with sexindependent dysregulation. Moreover, through pathway analysis, we find that these sexindependent biomarkers have substantially different biological roles than the sex-dependent biomarkers, and that some of these pathways are ubiquitously dysregulated in both post-mortem brain and blood. We conclude by synthesizing the discovered biomarker profiles with the extant literature, by highlighting the advantage of studying sex-specific dysregulation directly, and by This article is protected by copyright. All rights reserved. This is the author manuscript accepted for publication and has undergone full peer review but has not been through the copyediting, typesetting, pagination and proofreading process, which may lead to differences between this version and the Version of Record. Please cite this article as

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Section: Discussionmentioning

confidence: 99%

Solving for X: Evidence for sex‐specific autism biomarkers across multiple transcriptomic studies

Lee

Quinn

Lai

et al. 2018

American J of Med Genetics Pt B

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“…For this, we elected to use the Gene Ontology (GO) Biological Process and Kyoto Encyclopedia of Genes and Genomes (KEGG) annotation databases. Pathways with less than ten associated genes were removed before estimating pathway-level expression by taking the sum of counts for all genes in each pathway, as described and validated in [23]. This results in 3942 GO features and 302 KEGG features that are then used for model training.…”

Section: Engineering Annotation-level Expression From Genesmentioning

confidence: 99%

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

et al. 2020

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Background: 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: 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. Results: 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. Conclusions: 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.

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“…For this, we elected to use the Gene Ontology (GO) Biological Process and Kyoto Encyclopedia of Genes and Genomes (KEGG) annotation databases. Pathways with less than ten associated genes were removed before estimating pathway-level expression by taking the sum of counts for all genes in each pathway, as described and validated in Quinn et al (2018). This results in 3942 GO features and 302 KEGG features that are then used for model training.…”

Section: Engineering Annotation-level Expression From Genesmentioning

confidence: 99%

DeepTRIAGE: Interpretable and Individualised Biomarker Scores using Attention Mechanism for the Classification of Breast Cancer Sub-types

Beykikhoshk

Quinn

Lee

et al. 2019

Preprint

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Motivation: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 that separate tissue samples into distinct groups, 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 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. Results: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. Availability and implementation:The proposed model is implemented in Python using Py-Torch framework. The analysis is done in Python and R. All methods and models are freely available from https://github.com/adham/BiomarkerAttend.

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Improving the classification of neuropsychiatric conditions using gene ontology terms as features

Cited by 5 publications

References 42 publications

Solving for X: Evidence for sex‐specific autism biomarkers across multiple transcriptomic studies

Solving for X: Evidence for sex‐specific autism biomarkers across multiple transcriptomic studies

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

DeepTRIAGE: Interpretable and Individualised Biomarker Scores using Attention Mechanism for the Classification of Breast Cancer Sub-types

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