ArticleBioinformatics (Oxford, England)2020
Morbigenous brain region and gene detection with a genetically evolved random neural network cluster approach in late mild cognitive impairment.
Article in Bioinformatics (Oxford, England), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis that pooled it.
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Who cites it
20 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Resting-state abnormalities in functional connectivity of the default mode network in autism spectrum disorder: a meta-analysis.Brain imaging and behavior · 2021Pooled it
- Mutual-assistance learning for trustworthy biomarker discovery and disease prediction.Briefings in bioinformatics · 2025Article
- BIGFormer: A Graph Transformer With Local Structure Awareness for Diagnosis and Pathogenesis Identification of Alzheimer's Disease Using Imaging Genetic Data.IEEE journal of biomedical and health informatics · 2025Article
- The effect of seed location on functional connectivity: evidence from an image-based meta-analysis.Frontiers in neuroscience · 2023Article
- Altered intrinsic brain activity in patients with CSF1R-related leukoencephalopathy.Brain imaging and behavior · 2022Article
- GWLS: A Novel Model for Predicting Cognitive Function Scores in Patients With End-Stage Renal Disease.Frontiers in aging neuroscience · 2022Article
- Identification of Pathogenetic Brain RegionsFrontiers in neuroscience · 2022Article
- Research on Voxel-Based Features Detection and Analysis of Alzheimer's Disease Using Random Survey Support Vector Machine.Frontiers in neuroinformatics · 2022Article
- Discovery of Genetic Biomarkers for Alzheimer's Disease Using Adaptive Convolutional Neural Networks Ensemble and Genome-Wide Association Studies.Interdisciplinary sciences, computational life sciences · 2021Article
- Frequency-specific alterations of the resting-state BOLD signals in nocturnal enuresis: an fMRI Study.Scientific reports · 2021Article
- Changes in local brain function in mild cognitive impairment due to semantic dementia.CNS neuroscience & therapeutics · 2021Article
- Identify DNA-Binding Proteins Through the Extreme Gradient Boosting Algorithm.Frontiers in genetics · 2021Review
- Local Functional MR Change Pattern and Its Association With Cognitive Function in Objectively-Defined Subtle Cognitive Decline.Frontiers in aging neuroscience · 2021Article
- Constructing Dynamic Brain Functional Networks via Hyper-Graph Manifold Regularization for Mild Cognitive Impairment Classification.Frontiers in neuroscience · 2021Article
- Changes of Regional Neural Activity Homogeneity in Preclinical Alzheimer's Disease: Compensation and Dysfunction.Frontiers in neuroscience · 2021Article
- Integration and Segregation of Dynamic Functional Connectivity States for Mild Cognitive Impairment Revealed by Graph Theory Indicators.Contrast media & molecular imaging · 2021Article
- Multiple Laplacian Regularized RBF Neural Network for Assessing Dry Weight of Patients With End-Stage Renal Disease.Frontiers in physiology · 2021Article
- Assessing Dry Weight of Hemodialysis Patients via Sparse Laplacian Regularized RVFL Neural Network with LBioMed research international · 2021Article
- ACEP: improving antimicrobial peptides recognition through automatic feature fusion and amino acid embedding.BMC genomics · 2020Article
- Constructing Dynamic Functional Networks via Weighted Regularization and Tensor Low-Rank Approximation for Early Mild Cognitive Impairment Classification.Frontiers in cell and developmental biology · 2020Article
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Authors and funding
5 authors.
Funding
Abstract
motivationThe multimodal data fusion analysis becomes another important field for brain disease detection and increasing researches concentrate on using neural network algorithms to solve a range of problems. However, most current neural network optimizing strategies focus on internal nodes or hidden layer numbers, while ignoring the advantages of external optimization. Additionally, in the multimodal data fusion analysis of brain science, the problems of small sample size and high-dimensional data are often encountered due to the difficulty of data collection and the specialization of brain science data, which may result in the lower generalization performance of neural network.
resultsWe propose a genetically evolved random neural network cluster (GERNNC) model. Specifically, the fusion characteristics are first constructed to be taken as the input and the best type of neural network is selected as the base classifier to form the initial random neural network cluster. Second, the cluster is adaptively genetically evolved. Based on the GERNNC model, we further construct a multi-tasking framework for the classification of patients with brain disease and the extraction of significant characteristics. In a study of genetic data and functional magnetic resonance imaging data from the Alzheimer's Disease Neuroimaging Initiative, the framework exhibits great classification performance and strong morbigenous factor detection ability. This work demonstrates that how to effectively detect pathogenic components of the brain disease on the high-dimensional medical data and small samples. AVAILABILITY AND IMPLEMENTATION: The Matlab code is available at https://github.com/lizi1234560/GERNNC.git.
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