A novel machine learning model to predict autism spectrum disorders risk gene

Gök, Murat

doi:10.1007/s00521-018-3502-5

Cited by 26 publications

(13 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Any higher performance indicates that information about existing patterns in the data was obtained, which is the case of this study's best model, since it obtained a considerably higher value of performance. This gained information has been of great value in other studies that use ML algorithms to associate genes with conditions, disorders, and disabilities, with an effectiveness of close to 0.8, which is a similar value as the performance obtained by the best model of this study [12,13,25]. In biological terms, the pattern information that was gained means that the hypothesis the study was based on, that the genes associated with NS-IDS and the miRNAs that regulate them have similar spatio-temporal expression patterns in the human brain, is correct.…”

Section: Discussionsupporting

confidence: 75%

“…The issue of predicting the association between genes and neurodevelopmental disorders has been successfully broached by several authors, including Cogill and Wang [12], Gök [13], and Wang and Wang [25]. However, only the best ML model obtained by Wang and Wang was an ANN type, with an Autoencoder architecture [25].…”

Section: Discussionmentioning

confidence: 99%

“…Cogill and Wang [12] created a ML model that provided a list of candidates miR-NAs for bio-markers in autism, utilizing a database of gene expression of the brain development. Using this same database, Gök [13] demonstrates the utility of considering different types of ML models in the search for better models with higher performance in classification problems in genetics, but focused on the association of lncRNAs with the autism spectrum.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Machine learning algorithms for the identification of potential non-syndromic intellectual disability associated miRNAs

Betancur

Guevara-Coto

Romero

2021

Preprint

View full text Add to dashboard Cite

Background: Intellectual disabilities (IDs) are a group of developmental disorders with high phenotypic and genotypic heterogeneity. Association of genetic elements to IDs has typically been empirically accomplished, however recently, machine learning (ML) has proved to be an excellent instrument to elucidate these associations. miRNAs are short non-coding molecules that participate in spatiotemporal gene regulation, making them relevant for the understanding ID causality. Methods: In this study we used the BrainSpan spatio-temporal expression database to develop a series of machine learning predictors: SVM, RF, FF-ANN, and Stochastic Gradient Descent Classifier. These models were capable of recognizing gene expression profiles. The best classifier was used to label miRNAs associated with NS-IDs using the BrainSpan expression profiles. Results: The model with the best performance was a FF-ANN with 0.78 of F1-score, 0.78 of weighted recall and 0.78 of weighted precision. We used this model to identify miRNAs with high probability to be associated with NS-IDs using the spatio-temporal gene expression profile in the human brain. Labeled miRNAs that were annotated were associated with processes related to either IDs and-or neurodevelopmental processes. Conclusions: The development of a machine learning framework that identified potential NS-ID miRNAs represents an interesting approach for the identification of a potential list of on genes that could be subject for further experimental validation. This study also reinforces the potential of machine learning frameworks in their discovery of potential biomarkers that could improve disease detection and management.

show abstract

Section: Discussionsupporting

confidence: 75%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Machine learning algorithms for the identification of potential non-syndromic intellectual disability associated miRNAs

Betancur

Guevara-Coto

Romero

2021

Preprint

View full text Add to dashboard Cite

show abstract

“…In the search for additional de novo mutations, sequencing studies continue to be an important approach, but the current sequencing cost is still very high, especially for large samples. As an alternative strategy, advanced analytical approaches, which leverage previously implicated genes and prior knowledge, have the potential to enhance risk gene discovery in an efficient and cost-effective manner ( Asif et al, 2018 ; Gök, 2018 ; Brueggeman et al, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

A Machine Learning Approach to Predicting Autism Risk Genes: Validation of Known Genes and Discovery of New Candidates

et al. 2020

View full text Add to dashboard Cite

Autism spectrum disorder (ASD) is a complex neurodevelopmental condition with a strong genetic basis. The role of de novo mutations in ASD has been well established, but the set of genes implicated to date is still far from complete. The current study employs a machine learning-based approach to predict ASD risk genes using features from spatiotemporal gene expression patterns in human brain, gene-level constraint metrics, and other gene variation features. The genes identified through our prediction model were enriched for independent sets of ASD risk genes, and tended to be down-expressed in ASD brains, especially in frontal and parietal cortex. The highest-ranked genes not only included those with strong prior evidence for involvement in ASD (for example, NBEA , HERC1 , and TCF20 ), but also indicated potentially novel candidates, such as, MYCBP2 and CAND1 , which are involved in protein ubiquitination. We also showed that our method outperformed state-of-the-art scoring systems for ranking curated ASD candidate genes. Gene ontology enrichment analysis of our predicted risk genes revealed biological processes clearly relevant to ASD, including neuronal signaling, neurogenesis, and chromatin remodeling, but also highlighted other potential mechanisms that might underlie ASD, such as regulation of RNA alternative splicing and ubiquitination pathway related to protein degradation. Our study demonstrates that human brain spatiotemporal gene expression patterns and gene-level constraint metrics can help predict ASD risk genes. Our gene ranking system provides a useful resource for prioritizing ASD candidate genes.

show abstract

“…In DSM 5, the grouping of pervasive developmental disorders as specified by DSM-IV-TR is modified by placing autism spectrum along with social, communication and restricted or repetitive behaviours [6]. In DSM-IV-TR criteria, pervasive developmental disorders are classified as five separate disorders such as, autistic disorder, childhood disintegrative disorder, Rett's disorder, Asperger's disorder and pervasive developmental disorder.…”

Section: Introductionmentioning

confidence: 99%

Support Vector Machine for Classification of Autism Spectrum Disorder based on Abnormal Structure of Corpus Callosum

Jebapriya¹,

David²,

Kathrine³

et al. 2019

IJACSA

View full text Add to dashboard Cite

Autism Spectrum Disorders (ASD) is quite difficult to diagnose using traditional methods. Early prediction of Autism Spectrum Disorders enhances the in general psychological well-being of the child. These days, the research on Autism Spectrum Disorder is performed at a very high pace than earlier days due to increased rate of ASD affected people. One possible way of diagnosing ASD is through behavioral changes of children at the early ages. Structural imaging ponders point to disturbances in various mind regions, yet the exact neuroanatomical nature of these interruptions stays misty. Portrayal of cerebrum structural contrasts in children with ASD is basic for advancement of biomarkers that may in the long run be utilized to enhance analysis and screen reaction to treatment. In this examination we use machine figuring out how to decide a lot of conditions that together end up being prescient of Autism Spectrum Disorder. This will be of an extraordinary use to doctors, making a difference in identifying Autism Spectrum Disorder at a lot prior organize.

show abstract

A novel machine learning model to predict autism spectrum disorders risk gene

Cited by 26 publications

References 16 publications

Machine learning algorithms for the identification of potential non-syndromic intellectual disability associated miRNAs

Machine learning algorithms for the identification of potential non-syndromic intellectual disability associated miRNAs

A Machine Learning Approach to Predicting Autism Risk Genes: Validation of Known Genes and Discovery of New Candidates

Support Vector Machine for Classification of Autism Spectrum Disorder based on Abnormal Structure of Corpus Callosum

Contact Info

Product

Resources

About