Identifying Genes Associated With Autism Spectrum Disorders by Random Walk Method With Significance Tests

Zhao, Yan; Zhao, Ping; Liang, Haiyan; Zhang, Xiaolin

doi:10.1109/access.2020.3019516

Cited by 6 publications

(2 citation statements)

References 52 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Confirmed variants in RIPK1 44 , CAPN3 45 , KAT6A 46 , TACR3 47 , GJB2 48 , FAM98C 24 , PRKN, SLC3A1, CUBN, and PYGM were seen in one or two children in this study. Interestingly, rs751037529 in PRKN has been identified as a pathogenic variant associated with early-onset Parkinson’s disease 49 .…”

Section: Discussionsupporting

confidence: 62%

Pathogenic/likely pathogenic mutations identified in Vietnamese children diagnosed with autism spectrum disorder using high-resolution SNP genotyping platform

Bui,

Ton,

Nguyen

et al. 2024

Sci Rep

View full text Add to dashboard Cite

Among the most prevalent neurodevelopmental disorders, Autism Spectrum Disorder (ASD) is highly diverse showing a broad phenotypic spectrum. ASD also couples with a broad range of mutations, both de novo and inherited. In this study, we used a proprietary SNP genotyping chip to analyze the genomic DNA of 250 Vietnamese children diagnosed with ASD. Our Single Nucleotide Polymorphism (SNP) genotyping chip directly targets more than 800 thousand SNPs in the genome. Our primary focus was to identify pathogenic/likely pathogenic mutations that are potentially linked to more severe symptoms of autism. We identified and validated 23 pathogenic/likely pathogenic mutations in this initial study. The data shows that these mutations were detected in several cases spanning multiple biological pathways. Among the confirmed SNPs, mutations were identified in genes previously known to be strongly associated with ASD such as SLCO1B1, ACADSB, TCF4, HCP5, MOCOS, SRD5A2, MCCC2, DCC, and PRKN while several other mutations are known to associate with autistic traits or other neurodevelopmental disorders. Some mutations were found in multiple patients and some patients carried multiple pathogenic/likely pathogenic mutations. These findings contribute to the identification of potential targets for therapeutic solutions in what is considered a genetically heterogeneous neurodevelopmental disorder.

show abstract

Section: Discussionsupporting

confidence: 62%

Pathogenic/likely pathogenic mutations identified in Vietnamese children diagnosed with autism spectrum disorder using high-resolution SNP genotyping platform

Bui,

Ton,

Nguyen

et al. 2024

Sci Rep

View full text Add to dashboard Cite

show abstract

“…In 2020, Wang et al utilized a autoencoder network for representation learning of gene expression data, followed by a random forest network-derived K-mer method for feature representation of gene transcript sequences, and finally three machine learning models, including logistic regression (LR), SVM and random forest (RF), combined with ten-fold cross-validation were used to predict and rank RNA sequences, respectively, and RF was selected as the final model [ 15 ]. Zhao et al developed the random walk method based on AutDB for predicting genes associated with ASD [ 16 ]. 2021, Hasan et al collected 1055 data from toddlers and 705 data from adults by Q&A, including age, family history of ASD, and app used, and further used machine learning methods to predict whether they had ASD or not[ 17 ].…”

Section: Introductionmentioning

confidence: 99%

DeepASDPred: a CNN-LSTM-based deep learning method for Autism spectrum disorders risk RNA identification

2023

View full text Add to dashboard Cite

Background Autism spectrum disorders (ASD) are a group of neurodevelopmental disorders characterized by difficulty communicating with society and others, behavioral difficulties, and a brain that processes information differently than normal. Genetics has a strong impact on ASD associated with early onset and distinctive signs. Currently, all known ASD risk genes are able to encode proteins, and some de novo mutations disrupting protein-coding genes have been demonstrated to cause ASD. Next-generation sequencing technology enables high-throughput identification of ASD risk RNAs. However, these efforts are time-consuming and expensive, so an efficient computational model for ASD risk gene prediction is necessary. Results In this study, we propose DeepASDPerd, a predictor for ASD risk RNA based on deep learning. Firstly, we use K-mer to feature encode the RNA transcript sequences, and then fuse them with corresponding gene expression values to construct a feature matrix. After combining chi-square test and logistic regression to select the best feature subset, we input them into a binary classification prediction model constructed by convolutional neural network and long short-term memory for training and classification. The results of the tenfold cross-validation proved our method outperformed the state-of-the-art methods. Dataset and source code are available at https://github.com/Onebear-X/DeepASDPred is freely available. Conclusions Our experimental results show that DeepASDPred has outstanding performance in identifying ASD risk RNA genes.

show abstract

Effective Classification of Autism Spectrum Disorder Using Adaptive Support Vector Machine

George¹,

Blessie²

2022

Lecture Notes in Electrical Engineering

View full text Add to dashboard Cite

Identifying Genes Associated With Autism Spectrum Disorders by Random Walk Method With Significance Tests

Cited by 6 publications

References 52 publications

Pathogenic/likely pathogenic mutations identified in Vietnamese children diagnosed with autism spectrum disorder using high-resolution SNP genotyping platform

Pathogenic/likely pathogenic mutations identified in Vietnamese children diagnosed with autism spectrum disorder using high-resolution SNP genotyping platform

DeepASDPred: a CNN-LSTM-based deep learning method for Autism spectrum disorders risk RNA identification

Effective Classification of Autism Spectrum Disorder Using Adaptive Support Vector Machine

Contact Info

Product

Resources

About