Abstract
Autism Spectrum Disorder (ASD) is an etiologically and clinically heterogeneous neurodevelopmental disorder with more than 800 putative risk genes. This heterogeneity, coupled with the low penetrance of most ASD-associated mutations presents a challenge in identifying the relevant genetic determinants of ASD. We developed a machine learning semi-supervised gene scoring and classification method based on network propagation using a variant of the random walk with restart algorithm to identify and rank genes according to their association to know ASD-related genes. The method combines information from protein-protein interactions and positive (disease-related) and negative (disease-unrelated) genes. Our results indicate that the proposed method can classify held-out known disease genes in a cross-validation setting with good performance (area under the receiver operating curve \(\sim \)0.85, area under the precision-recall curve \(\sim \)0.8 and Matthews correlation coefficient 0.57). We found a set of top-ranking novel candidate genes identified by the method to be significantly enriched for pathways related to synaptic transmission and ion transport and specific neurotransmitter-associated pathways previously shown to be associated with ASD. Most of the novel candidate genes were found to be targeted by denovo single nucleotide variants in ASD patients.
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Notes
- 1.
https://gene.sfari.org/database/human-gene/, accessed 1 May 2018.
- 2.
http://amp.pharm.mssm.edu/Enrichr, accessed 1 Jun. 2018.
- 3.
http://denovo-db.gs.washington.edu/denovo-db/, accessed 1 February 2019.
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Acknowledgments
The authors would like to acknowledge the support by the UID/MULTI/04046/2019 centre grant from FCT, Portugal (to BioISI). A.M. is recipient of a fellowship from BioSys PhD programme (Ref SFRH/BD52485/2014) from FCT (Portugal). This work used the EGI infrastructure with the support of NCG-INGRID-PT (Portugal) and BIFI (Spain).
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Martiniano, H.F.M.C., Asif, M., Vicente, A.M., Correia, L. (2020). Network Propagation-Based Semi-supervised Identification of Genes Associated with Autism Spectrum Disorder. In: Raposo, M., Ribeiro, P., Sério, S., Staiano, A., Ciaramella, A. (eds) Computational Intelligence Methods for Bioinformatics and Biostatistics. CIBB 2018. Lecture Notes in Computer Science(), vol 11925. Springer, Cham. https://doi.org/10.1007/978-3-030-34585-3_21
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