MicroRNAs (miRNAs) are a set of short non-coding RNAs that play significant regulatory roles in cells. The study of miRNA data produced by Next-Generation Sequencing techniques can be of valid help for the analysis of multifactorial diseases, such as Multiple Sclerosis (MS). Although extensive studies have been conducted on young adults affected by MS, very little work has been done to investigate the pathogenic mechanisms in pediatric patients, and none from a machine learning perspective. In this work, we report the experimental results of a classification study aimed at evaluating the effectiveness of machine learning methods in automatically distinguishing pediatric MS from healthy children, based on their miRNA expression profiles. Additionally, since Attention Deficit Hyperactivity Disorder (ADHD) shares some cognitive impairments with pediatric MS, we also included patients affected by ADHD in our study. Encouraging results were obtained with an artificial neural network model based on a set of features automatically selected by feature selection algorithms. The results obtained show that models developed on automatically selected features overcome models based on a set of features selected by human experts. Developing an automatic predictive model can support clinicians in early MS diagnosis and provide new insights that can help find novel molecular pathways involved in MS disease.

MicroRNA expression classification for pediatric multiple sclerosis identification

Casalino, Gabriella;Castellano, Giovanna
;
Nuzziello, Nicoletta;Vessio, Gennaro
2023-01-01

Abstract

MicroRNAs (miRNAs) are a set of short non-coding RNAs that play significant regulatory roles in cells. The study of miRNA data produced by Next-Generation Sequencing techniques can be of valid help for the analysis of multifactorial diseases, such as Multiple Sclerosis (MS). Although extensive studies have been conducted on young adults affected by MS, very little work has been done to investigate the pathogenic mechanisms in pediatric patients, and none from a machine learning perspective. In this work, we report the experimental results of a classification study aimed at evaluating the effectiveness of machine learning methods in automatically distinguishing pediatric MS from healthy children, based on their miRNA expression profiles. Additionally, since Attention Deficit Hyperactivity Disorder (ADHD) shares some cognitive impairments with pediatric MS, we also included patients affected by ADHD in our study. Encouraging results were obtained with an artificial neural network model based on a set of features automatically selected by feature selection algorithms. The results obtained show that models developed on automatically selected features overcome models based on a set of features selected by human experts. Developing an automatic predictive model can support clinicians in early MS diagnosis and provide new insights that can help find novel molecular pathways involved in MS disease.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11586/363789
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