Molecular Classification and Interpretation of Amyotrophic Lateral Sclerosis Using Deep Convolution Neural Networks and Shapley Values

Amyotrophic lateral sclerosis (ALS) is a prototypical neurodegenerative disease characterized by progressive degeneration of motor neurons to severely effect the functionality to control voluntary muscle movement. Most of the non-additive genetic aberrations responsible for ALS make its molecular cl...

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Autores principales:	Abdul Karim, Zheng Su, Phillip K. West, Matthew Keon, The NYGC ALS Consortium, Jannah Shamsani, Samuel Brennan, Ted Wong, Ognjen Milicevic, Guus Teunisse, Hima Nikafshan Rad, Abdul Sattar
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	machine learning ALS classification interpretation target identification Genetics QH426-470
Acceso en línea:	https://doaj.org/article/97d17789761541b2810bd6763781cc5c
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spelling	oai:doaj.org-article:97d17789761541b2810bd6763781cc5c2021-11-25T17:41:33ZMolecular Classification and Interpretation of Amyotrophic Lateral Sclerosis Using Deep Convolution Neural Networks and Shapley Values10.3390/genes121117542073-4425https://doaj.org/article/97d17789761541b2810bd6763781cc5c2021-10-01T00:00:00Zhttps://www.mdpi.com/2073-4425/12/11/1754https://doaj.org/toc/2073-4425Amyotrophic lateral sclerosis (ALS) is a prototypical neurodegenerative disease characterized by progressive degeneration of motor neurons to severely effect the functionality to control voluntary muscle movement. Most of the non-additive genetic aberrations responsible for ALS make its molecular classification very challenging along with limited sample size, curse of dimensionality, class imbalance and noise in the data. Deep learning methods have been successful in many other related areas but have low minority class accuracy and suffer from the lack of explainability when used directly with RNA expression features for ALS molecular classification. In this paper, we propose a deep-learning-based molecular ALS classification and interpretation framework. Our framework is based on training a convolution neural network (CNN) on images obtained from converting RNA expression values into pixels based on DeepInsight similarity technique. Then, we employed Shapley additive explanations (SHAP) to extract pixels with higher relevance to ALS classifications. These pixels were mapped back to the genes which made them up. This enabled us to classify ALS samples with high accuracy for a minority class along with identifying genes that might be playing an important role in ALS molecular classifications. Taken together with RNA expression images classified with CNN, our preliminary analysis of the genes identified by SHAP interpretation demonstrate the value of utilizing Machine Learning to perform molecular classification of ALS and uncover disease-associated genes.Abdul KarimZheng SuPhillip K. WestMatthew KeonThe NYGC ALS ConsortiumJannah ShamsaniSamuel BrennanTed WongOgnjen MilicevicGuus TeunisseHima Nikafshan RadAbdul SattarMDPI AGarticlemachine learningALSclassificationinterpretationtarget identificationGeneticsQH426-470ENGenes, Vol 12, Iss 1754, p 1754 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	machine learning ALS classification interpretation target identification Genetics QH426-470
spellingShingle	machine learning ALS classification interpretation target identification Genetics QH426-470 Abdul Karim Zheng Su Phillip K. West Matthew Keon The NYGC ALS Consortium Jannah Shamsani Samuel Brennan Ted Wong Ognjen Milicevic Guus Teunisse Hima Nikafshan Rad Abdul Sattar Molecular Classification and Interpretation of Amyotrophic Lateral Sclerosis Using Deep Convolution Neural Networks and Shapley Values
description	Amyotrophic lateral sclerosis (ALS) is a prototypical neurodegenerative disease characterized by progressive degeneration of motor neurons to severely effect the functionality to control voluntary muscle movement. Most of the non-additive genetic aberrations responsible for ALS make its molecular classification very challenging along with limited sample size, curse of dimensionality, class imbalance and noise in the data. Deep learning methods have been successful in many other related areas but have low minority class accuracy and suffer from the lack of explainability when used directly with RNA expression features for ALS molecular classification. In this paper, we propose a deep-learning-based molecular ALS classification and interpretation framework. Our framework is based on training a convolution neural network (CNN) on images obtained from converting RNA expression values into pixels based on DeepInsight similarity technique. Then, we employed Shapley additive explanations (SHAP) to extract pixels with higher relevance to ALS classifications. These pixels were mapped back to the genes which made them up. This enabled us to classify ALS samples with high accuracy for a minority class along with identifying genes that might be playing an important role in ALS molecular classifications. Taken together with RNA expression images classified with CNN, our preliminary analysis of the genes identified by SHAP interpretation demonstrate the value of utilizing Machine Learning to perform molecular classification of ALS and uncover disease-associated genes.
format	article
author	Abdul Karim Zheng Su Phillip K. West Matthew Keon The NYGC ALS Consortium Jannah Shamsani Samuel Brennan Ted Wong Ognjen Milicevic Guus Teunisse Hima Nikafshan Rad Abdul Sattar
author_facet	Abdul Karim Zheng Su Phillip K. West Matthew Keon The NYGC ALS Consortium Jannah Shamsani Samuel Brennan Ted Wong Ognjen Milicevic Guus Teunisse Hima Nikafshan Rad Abdul Sattar
author_sort	Abdul Karim
title	Molecular Classification and Interpretation of Amyotrophic Lateral Sclerosis Using Deep Convolution Neural Networks and Shapley Values
title_short	Molecular Classification and Interpretation of Amyotrophic Lateral Sclerosis Using Deep Convolution Neural Networks and Shapley Values
title_full	Molecular Classification and Interpretation of Amyotrophic Lateral Sclerosis Using Deep Convolution Neural Networks and Shapley Values
title_fullStr	Molecular Classification and Interpretation of Amyotrophic Lateral Sclerosis Using Deep Convolution Neural Networks and Shapley Values
title_full_unstemmed	Molecular Classification and Interpretation of Amyotrophic Lateral Sclerosis Using Deep Convolution Neural Networks and Shapley Values
title_sort	molecular classification and interpretation of amyotrophic lateral sclerosis using deep convolution neural networks and shapley values
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/97d17789761541b2810bd6763781cc5c
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Molecular Classification and Interpretation of Amyotrophic Lateral Sclerosis Using Deep Convolution Neural Networks and Shapley Values

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