Classification of autism spectrum disorder by combining brain connectivity and deep neural network classifier

Kong, Yinsong; Gao, Jianliang; Xu, Yunpei; Pan, Yi; Wang, Jianxin; Liu, Jin

doi:10.1016/j.neucom.2018.04.080

Cited by 194 publications

(106 citation statements)

References 24 publications

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“…is is consistent with multiple previous ASD classification studies [26,27]. e improvement was likely due to a combination of attention mechanism and the superior capability of deep learning model on complex data patterns, such as FC features.…”

Section: Performance Comparison On the Whole Abide Datasetsupporting

confidence: 90%

“…Over the past few years, an increasing body of the literature confirmed the success of feature construction using deep learning methods. Deep learning has been demonstrated to outperform traditional machine learning algorithms on numerous recognition and classification tasks [24][25][26][27][28][29], which inspires the researchers in the ASD community to apply deep learning approaches on ASD classification. Earlier, deep neural networks (DNNs) have been applied to identify ASD patients using rs-fMRI [26].…”

Section: Introductionmentioning

confidence: 99%

“…Kong et al [27] constructed individual functional brain networks using the rs-fMRI data from 182 subjects of NYU Langone Medical Center, a data site within ABIDE repository. FC features were used to represent the networks of all subjects and further ranked using F-score.…”

Section: Introductionmentioning

confidence: 99%

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Multichannel Deep Attention Neural Networks for the Classification of Autism Spectrum Disorder Using Neuroimaging and Personal Characteristic Data

et al. 2020

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Autism spectrum disorder (ASD) is a developmental disorder that impacts more than 1.6% of children aged 8 across the United States. It is characterized by impairments in social interaction and communication, as well as by a restricted repertoire of activity and interests. The current standardized clinical diagnosis of ASD remains to be a subjective diagnosis, mainly relying on behavior-based tests. However, the diagnostic process for ASD is not only time consuming, but also costly, causing a tremendous financial burden for patients’ families. Therefore, automated diagnosis approaches have been an attractive solution for earlier identification of ASD. In this work, we set to develop a deep learning model for automated diagnosis of ASD. Specifically, a multichannel deep attention neural network (DANN) was proposed by integrating multiple layers of neural networks, attention mechanism, and feature fusion to capture the interrelationships in multimodality data. We evaluated the proposed multichannel DANN model on the Autism Brain Imaging Data Exchange (ABIDE) repository with 809 subjects (408 ASD patients and 401 typical development controls). Our model achieved a state-of-the-art accuracy of 0.732 on ASD classification by integrating three scales of brain functional connectomes and personal characteristic data, outperforming multiple peer machine learning models in a k-fold cross validation experiment. Additional k-fold and leave-one-site-out cross validation were conducted to test the generalizability and robustness of the proposed multichannel DANN model. The results show promise for deep learning models to aid the future automated clinical diagnosis of ASD.

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Section: Performance Comparison On the Whole Abide Datasetsupporting

confidence: 90%

Section: Introductionmentioning

confidence: 99%

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Multichannel Deep Attention Neural Networks for the Classification of Autism Spectrum Disorder Using Neuroimaging and Personal Characteristic Data

et al. 2020

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“…Second, the more features of miRNAs also should be designed, such as topological features of miR-NAs. Finally, other similarity-based methods [74], collaborative metric learning methods [75] and deep learning methods [76,77] should be adopted. We would provide a more effective computational method to predict essential miRNAs by addressing above limitations in the future.…”

Section: Resultsmentioning

confidence: 99%

PESM: predicting the essentiality of miRNAs based on gradient boosting machines and sequences

Cheng

Wang

et al. 2020

BMC Bioinformatics

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Background: MicroRNAs (miRNAs) are a kind of small noncoding RNA molecules that are direct posttranscriptional regulations of mRNA targets. Studies have indicated that miRNAs play key roles in complex diseases by taking part in many biological processes, such as cell growth, cell death and so on. Therefore, in order to improve the effectiveness of disease diagnosis and treatment, it is appealing to develop advanced computational methods for predicting the essentiality of miRNAs. Result: In this study, we propose a method (PESM) to predict the miRNA essentiality based on gradient boosting machines and miRNA sequences. First, PESM extracts the sequence and structural features of miRNAs. Then it uses gradient boosting machines to predict the essentiality of miRNAs. We conduct the 5-fold cross-validation to assess the prediction performance of our method. The area under the receiver operating characteristic curve (AUC), F-measure and accuracy (ACC) are used as the metrics to evaluate the prediction performance. We also compare PESM with other three competing methods which include miES, Gaussian Naive Bayes and Support Vector Machine. Conclusion: The results of experiments show that PESM achieves the better prediction performance (AUC: 0.9117, F-measure: 0.8572, ACC: 0.8516) than other three computing methods. In addition, the relative importance of all features also further shows that newly added features can be helpful to improve the prediction performance of methods.

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“…It is also time-consuming and needs high computational cost [12,13]. Deep learning approaches, which are another family of machine learning methods, attracted many researchers working in the medical field in recent years [14]. They are preferred to standard machine learning approaches as they need small or no image processing procedure.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Techniques for Automatic Detection of Embryonic Neurodevelopmental Disorders

2020

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The increasing rates of neurodevelopmental disorders (NDs) are threatening pregnant women, parents, and clinicians caring for healthy infants and children. NDs can initially start through embryonic development due to several reasons. Up to three in 1000 pregnant women have embryos with brain defects; hence, the primitive detection of embryonic neurodevelopmental disorders (ENDs) is necessary. Related work done for embryonic ND classification is very limited and is based on conventional machine learning (ML) methods for feature extraction and classification processes. Feature extraction of these methods is handcrafted and has several drawbacks. Deep learning methods have the ability to deduce an optimum demonstration from the raw images without image enhancement, segmentation, and feature extraction processes, leading to an effective classification process. This article proposes a new framework based on deep learning methods for the detection of END. To the best of our knowledge, this is the first study that uses deep learning techniques for detecting END. The framework consists of four stages which are transfer learning, deep feature extraction, feature reduction, and classification. The framework depends on feature fusion. The results showed that the proposed framework was capable of identifying END from embryonic MRI images of various gestational ages. To verify the efficiency of the proposed framework, the results were compared with related work that used embryonic images. The performance of the proposed framework was competitive. This means that the proposed framework can be successively used for detecting END.

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Classification of autism spectrum disorder by combining brain connectivity and deep neural network classifier

Cited by 194 publications

References 24 publications

Multichannel Deep Attention Neural Networks for the Classification of Autism Spectrum Disorder Using Neuroimaging and Personal Characteristic Data

Multichannel Deep Attention Neural Networks for the Classification of Autism Spectrum Disorder Using Neuroimaging and Personal Characteristic Data

PESM: predicting the essentiality of miRNAs based on gradient boosting machines and sequences

Deep Learning Techniques for Automatic Detection of Embryonic Neurodevelopmental Disorders

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