2021
DOI: 10.3389/fgene.2020.628539
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Cell Type-Specific Predictive Models Perform Prioritization of Genes and Gene Sets Associated With Autism

Abstract: Bulk transcriptomic analyses of autism spectrum disorder (ASD) have revealed dysregulated pathways, while the brain cell type-specific molecular pathology of ASD still needs to be studied. Machine learning-based studies can be conducted for ASD, prioritizing high-confidence gene candidates and promoting the design of effective interventions. Using human brain nucleus gene expression of ASD and controls, we construct cell type-specific predictive models for ASD based on individual genes and gene sets, respectiv… Show more

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Cited by 12 publications
(7 citation statements)
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“…Moreover, the efficacy of DL methods can be enhanced by implementing predictive modeling and collaborative filtering techniques. Predictive modeling is a statistical technique that uses ML and data mining to forecast or predict future outcomes and can help promote the diagnosis of ASD [ 216 , 217 ]. Collaborative filtering is another technique that can allow the creation of personalized recommendations based on the interactions, data, and preferences of other users in the system [ 218 ].…”
Section: Machine Learning To Detect Asd Biomarkersmentioning
confidence: 99%
“…Moreover, the efficacy of DL methods can be enhanced by implementing predictive modeling and collaborative filtering techniques. Predictive modeling is a statistical technique that uses ML and data mining to forecast or predict future outcomes and can help promote the diagnosis of ASD [ 216 , 217 ]. Collaborative filtering is another technique that can allow the creation of personalized recommendations based on the interactions, data, and preferences of other users in the system [ 218 ].…”
Section: Machine Learning To Detect Asd Biomarkersmentioning
confidence: 99%
“…Constructing machine learning-based disease prediction model can facilitate the clinical diagnosis of diseases [ 41 43 ]. Using the identified ASD molecular subtype-specific DEGs, we built PLS-based prediction models for classifying molecular subtypes.…”
Section: Resultsmentioning
confidence: 99%
“…The first part used the hybrid gene similarity function (HGS) with Ada-Boost ensemble learning machines. Figure 11 shows a detailed performance measure in terms of true positive rate (TP Rate), false positive rate (FP Rate), Precision, Recall, (11) Accuracy = TP + TN TP + FP + TN + FN F-measure, AUC-ROC and Accuracy, which reached 84.35%, increasing the accuracy of Random Forest by around 4.5%. Moreover, HEC-ASD based on a gradient boosting model used regularization parameters to prevent overfitting the model.…”
Section: Performance Measuresmentioning
confidence: 99%
“…Machine learning techniques are helpful in different biomedical problems such as drug discovery [ 2 , 3 ], gene prediction [ 4 , 5 ], disease gene interactions [ 6 ], genome annotations [ 7 ], gene regulatory network derivation [ 8 ], microarray data classification [ 9 ],and protein function prediction [ 10 ]. Some predictive models [ 11 ] were proposed to identify genes related to ASD and gene sets associated with autism according to specific cell types. They give higher priority to high-confidence candidates genes from Simons Foundation Autism Research Initiative (SFARI) to construct a predictive model.…”
Section: Introductionmentioning
confidence: 99%