Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

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

Version 1 : Received: 2 October 2021 / Approved: 4 October 2021 / Online: 4 October 2021 (12:50:04 CEST)

How to cite: Karim, A.; Su, Z.; K.West, P.; Keon, M.; ALS Consortium, T.N.; Shamsani, J.; Brennan, S.; Wong, T.; Milicevic, O.; Teunisse, G.; Nikafshan Rad, H.; Sattar, A. Molecular Classification and Interpretation of Amyotrophic Lateral Sclerosis Using Deep Convolution Neural Networks and Shapley Values. Preprints 2021, 2021100059 (doi: 10.20944/preprints202110.0059.v1). Karim, A.; Su, Z.; K.West, P.; Keon, M.; ALS Consortium, T.N.; Shamsani, J.; Brennan, S.; Wong, T.; Milicevic, O.; Teunisse, G.; Nikafshan Rad, H.; Sattar, A. Molecular Classification and Interpretation of Amyotrophic Lateral Sclerosis Using Deep Convolution Neural Networks and Shapley Values. Preprints 2021, 2021100059 (doi: 10.20944/preprints202110.0059.v1).

Abstract

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 explainailbilty 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 utilising Machine Learning to perform molecular classification of ALS and uncover disease-associated genes.

Keywords

Machine learning; ALS; Classification; Interpretation; Target Identification

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