@inproceedings{7b93cd7c823b404da1352fb168e0c7dd,
title = "Identification of Autistic Risk Genes Using Developmental Brain Gene Expression Data",
abstract = "Recently, the serious impairments of ASD cause a series of pending issues to increase a major burden of health and finance globally. In this work, we propose an effective convolutional neural network (CNN) - based model to identify the potential autistic risk genes based on the developmental brain gene expression profiles. Based on the 10-fold cross validations, the simulation experiments demonstrate that the proposed model shows supreme classification results as compared to the other state-of-the-art classifiers. In such an imbalanced dataset, the proposed CNN model achieves the F1-score of 63.07 ± 3.9 and the area under ROC curve of 0.6940. In case study, 70% out of the top-10 predicted risk genes have been confirmed to increase the risk of developing ASD via published literatures. The effectiveness enables our model to serve as a candidate tool for accelerating the identification of autistic genetic abnormalities.",
keywords = "Autism spectrum disorders (ASD), Autistic biomarkers, Developmental brain gene expression data, Gene prioritization",
author = "Huang, {Zhi An} and Huang, {Yu An} and You, {Zhu Hong} and Shanwen Zhang and Yu, {Chang Qing} and Wenzhun Huang",
note = "Publisher Copyright: {\textcopyright} 2020, Springer Nature Switzerland AG.; 16th International Conference on Intelligent Computing, ICIC 2020 ; Conference date: 02-10-2020 Through 05-10-2020",
year = "2020",
doi = "10.1007/978-3-030-60802-6_29",
language = "英语",
isbn = "9783030608019",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "326--338",
editor = "De-Shuang Huang and Kang-Hyun Jo",
booktitle = "Intelligent Computing Theories and Application - 16th International Conference, ICIC 2020, Proceedings",
}