This paper is in the following e-collection/theme issue:

Decision Support for Health Professionals (1185) Autism Spectrum Disorder (ASD) (109) Artificial Intelligence (1371) Machine Learning (1465) Clinical Information and Decision Making (1407) Clinical Informatics (1022) Diagnostic Tools in Mental Health (242) Precision Medicine and Genetics (48)

Published on in Vol 9, No 4 (2021): April

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/24754, first published .
Diagnostic Classification and Prognostic Prediction Using Common Genetic Variants in Autism Spectrum Disorder: Genotype-Based Deep Learning

Diagnostic Classification and Prognostic Prediction Using Common Genetic Variants in Autism Spectrum Disorder: Genotype-Based Deep Learning

Diagnostic Classification and Prognostic Prediction Using Common Genetic Variants in Autism Spectrum Disorder: Genotype-Based Deep Learning

Authors of this article:

Haishuai Wang1, 2 Author Orcid Image ;   Paul Avillach1 Author Orcid Image
Article Authors Cited by (36) Tweetations (3) Metrics

Journals

  1. Qiao S, Pang S, Luo G, Pan S, Chen T, Lv Z. FLDS: An Intelligent Feature Learning Detection System for Visualizing Medical Images Supporting Fetal Four-Chamber Views. IEEE Journal of Biomedical and Health Informatics 2022;26(10):4814 View
  2. Gupta C, Chandrashekar P, Jin T, He C, Khullar S, Chang Q, Wang D. Bringing machine learning to research on intellectual and developmental disabilities: taking inspiration from neurological diseases. Journal of Neurodevelopmental Disorders 2022;14(1) View
  3. Gao L, Wang C, Song X, Tian L, Qu Z, Han Y, Zhang X. The Sensory Abnormality Mediated Partially the Efficacy of Repetitive Transcranial Magnetic Stimulation on Treating Comorbid Sleep Disorder in Autism Spectrum Disorder Children. Frontiers in Psychiatry 2022;12 View
  4. Lee J, Ha S, Ahn J, Lee S, Choi J, Cheon K. The Role of Ion Channel-Related Genes in Autism Spectrum Disorder: A Study Using Next-Generation Sequencing. Frontiers in Genetics 2021;12 View
  5. Chaki J, Woźniak M. Deep learning for neurodegenerative disorder (2016 to 2022): A systematic review. Biomedical Signal Processing and Control 2023;80:104223 View
  6. Ma Z, Xu L, Li Q, Li X, Shi Y, Zhang X, Yang Y, Wang J, Fan L, Wu L. Prediction Model for Sensory Perception Abnormality in Autism Spectrum Disorder. International Journal of Molecular Sciences 2023;24(3):2367 View
  7. Arumugam A, Velrajkumar P, Sriramakrishnan G, Muppidi S. Feedback Artificial Tree‐Anti Corona Virus Optimization enabled deep learning for detecting autism spectrum disorder. Expert Systems 2022;39(9) View
  8. Qiao S, Pang S, Luo G, Pan S, Yu Z, Chen T, Lv Z. RLDS: An explainable residual learning diagnosis system for fetal congenital heart disease. Future Generation Computer Systems 2022;128:205 View
  9. Wang H, Bowe B, Cui Z, Yang H, Swamidass S, Xie Y, Al-Aly Z. A Deep Learning Approach for the Estimation of Glomerular Filtration Rate. IEEE Transactions on NanoBioscience 2022;21(4):560 View
  10. Wang H, Tao G, Ma J, Jia S, Chi L, Yang H, Zhao Z, Tao J. Predicting the Epidemics Trend of COVID-19 Using Epidemiological-Based Generative Adversarial Networks. IEEE Journal of Selected Topics in Signal Processing 2022;16(2):276 View
  11. Joudar S, Albahri A, Hamid R. Triage and priority-based healthcare diagnosis using artificial intelligence for autism spectrum disorder and gene contribution: A systematic review. Computers in Biology and Medicine 2022;146:105553 View
  12. Qiao S, Pan S, Luo G, Pang S, Chen T, Singh A, Lv Z. A Pseudo-Siamese Feature Fusion Generative Adversarial Network for Synthesizing High-Quality Fetal Four-Chamber Views. IEEE Journal of Biomedical and Health Informatics 2023;27(3):1193 View
  13. Nisar S, Haris M. Neuroimaging genetics approaches to identify new biomarkers for the early diagnosis of autism spectrum disorder. Molecular Psychiatry 2023;28(12):4995 View
  14. Wang M, Richmond L, Schleider J, Nelson B, Luhmann C. Predicting internalizing symptoms with machine learning: identifying individuals that need care. Journal of American College Health 2023:1 View
  15. S R R, Mounika S. Autism Spectrum Disorder Classification Using Machine Learning and Deep Learning- A Survey. EAI Endorsed Transactions on Pervasive Health and Technology 2023;9 View
  16. Alowais S, Alghamdi S, Alsuhebany N, Alqahtani T, Alshaya A, Almohareb S, Aldairem A, Alrashed M, Bin Saleh K, Badreldin H, Al Yami M, Al Harbi S, Albekairy A. Revolutionizing healthcare: the role of artificial intelligence in clinical practice. BMC Medical Education 2023;23(1) View
  17. Balasubramanian J, Gururaj B, Gayatri N. An effective autism spectrum disorder screening method using machine learning classification techniques. Concurrency and Computation: Practice and Experience 2024;36(2) View
  18. Sriramakrishnan G, Rani V, Thatavarti S, Maram B. Chronological pelican remora optimization-enabled deep learning for detection of autism spectrum disorder. Signal, Image and Video Processing 2024;18(1):515 View
  19. Taz T, Arslanturk S. Unveiling genetic variant-level biomarkers for aggressive prostate cancer. Informatics in Medicine Unlocked 2023;43:101410 View
  20. Barnett E, Onete D, Salekin A, Faraone S. Genomic Machine Learning Meta-regression: Insights on Associations of Study Features With Reported Model Performance. IEEE/ACM Transactions on Computational Biology and Bioinformatics 2024;21(1):169 View
  21. Xu Q, Yang J, Zhang H, Jie X, Bandara D. Enhancing Few-Shot Action Recognition Using Skeleton Temporal Alignment and Adversarial Training. IEEE Access 2024;12:31745 View
  22. Bandara D, Riccardi K. Graph Node Classification to Predict Autism Risk in Genes. Genes 2024;15(4):447 View
  23. Fatima A, Masood S. Machine learning approaches for neurological disease prediction: A systematic review. Expert Systems 2024;41(9) View
  24. Xie X, Zhou R, Fang Z, Zhang Y, Wang Q, Liu X. Seeing beyond words: Visualizing autism spectrum disorder biomarker insights. Heliyon 2024;10(9):e30420 View
  25. Nussinov R, Yavuz B, Demirel H, Arici M, Jang H, Tuncbag N. Review: Cancer and neurodevelopmental disorders: multi-scale reasoning and computational guide. Frontiers in Cell and Developmental Biology 2024;12 View
  26. Chen W, Yang J, Sun Z, Zhang X, Tao G, Ding Y, Gu J, Bu J, Wang H. DeepASD: a deep adversarial-regularized graph learning method for ASD diagnosis with multimodal data. Translational Psychiatry 2024;14(1) View
  27. Ding Y, Zhang H, Qiu T. Deep learning approach to predict autism spectrum disorder: a systematic review and meta-analysis. BMC Psychiatry 2024;24(1) View
  28. Wu Q, Morrow E, Uzun E, Fariselli P. A deep learning model for prediction of autism status using whole-exome sequencing data. PLOS Computational Biology 2024;20(11):e1012468 View

Books/Policy Documents

  1. Faria B, Ribeiro M, Simões R, Valente S, Rocha N. Information Systems and Technologies. View
  2. Mbatha S, Ecker R, Dlamini Z. Colorectal Cancer Treatment Resistance. View