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dc.contributor.authorKarim, Abdul
dc.contributor.authorSu, Zheng
dc.contributor.authorWest, Phillip K
dc.contributor.authorKeon, Matthew
dc.contributor.authorThe Nygc Als Consortium
dc.contributor.authorShamsani, Jannah
dc.contributor.authorBrennan, Samuel
dc.contributor.authorWong, Ted
dc.contributor.authorMilicevic, Ognjen
dc.contributor.authorTeunisse, Guus
dc.contributor.authorRad, Hima Nikafshan
dc.contributor.authorSattar, Abdul
dc.date.accessioned2021-12-15T07:03:37Z
dc.date.available2021-12-15T07:03:37Z
dc.date.issued2021
dc.identifier.issn2073-4425
dc.identifier.doi10.3390/genes12111754
dc.identifier.urihttp://hdl.handle.net/10072/410831
dc.description.abstractAmyotrophic 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.
dc.description.peerreviewedYes
dc.languageeng
dc.publisherMDPI AG
dc.relation.ispartofpagefrom1754
dc.relation.ispartofpageto1754
dc.relation.ispartofissue11
dc.relation.ispartofjournalGenes
dc.relation.ispartofvolume12
dc.subject.fieldofresearchGenetics
dc.subject.fieldofresearchMicrobiology
dc.subject.fieldofresearchcode3105
dc.subject.fieldofresearchcode3107
dc.subject.keywordsALS
dc.subject.keywordsclassification
dc.subject.keywordsinterpretation
dc.subject.keywordsmachine learning
dc.subject.keywordstarget identification
dc.titleMolecular Classification and Interpretation of Amyotrophic Lateral Sclerosis Using Deep Convolution Neural Networks and Shapley Values
dc.typeJournal article
dc.type.descriptionC1 - Articles
dcterms.bibliographicCitationKarim, A; Su, Z; West, PK; Keon, M; The Nygc Als Consortium, ; Shamsani, J; Brennan, S; Wong, T; Milicevic, O; Teunisse, G; Rad, HN; Sattar, A, Molecular Classification and Interpretation of Amyotrophic Lateral Sclerosis Using Deep Convolution Neural Networks and Shapley Values., Genes, 2021, 12 (11), pp. 1754
dcterms.dateAccepted2021-10-23
dcterms.licensehttps:// creativecommons.org/licenses/by/ 4.0
dc.date.updated2021-12-06T01:13:00Z
dc.description.versionVersion of Record (VoR)
gro.rights.copyright© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
gro.hasfulltextFull Text
gro.griffith.authorSattar, Abdul


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