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Li J, Yao Z, Duan M, Liu S, Li F, Zhu H, Xia Z, Huang L, Zhou F. MuscNet, a Weighted Voting Model of Multi-Source Connectivity Networks to Predict Mild Cognitive Impairment Using Resting-State Functional MRI. IEEE ACCESS : PRACTICAL INNOVATIONS, OPEN SOLUTIONS 2020; 8:174023-174031. [PMID: 35548102 PMCID: PMC9090182 DOI: 10.1109/access.2020.3025828] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
The neurological disorder mild cognitive impairment (MCI) demonstrates minor impacts on the patient's daily activities and may be ignored as the status of normal aging. But some of the MCI patients may further develop into severe statuses like Alzheimer's disease (AD). The brain functional connectivity network (BFCN) was usually constructed from the resting-state functional magnetic resonance imaging (rs-fMRI) data. This technology has been widely used to detect the neurodegenerative dementia and to reveal the intrinsic mechanism of neural activities. The BFCN edge was usually determined by the pairwise correlation between the brain regions. This study proposed a weighted voting model of multi-source connectivity networks (MuscNet) by integrating multiple BFCNs of different correlation coefficients. Our model was further improved by removing redundant features. The experimental data demonstrated that different BFCNs contributed complementary information to each other and MuscNet outperformed the existing models on detecting MCI patients. The previous study suggested the existence of multiple solutions with similarly good performance for a machine learning problem. The proposed model MuscNet utilized a weighted voting strategy to slightly outperform the existing studies, suggesting an effective way to fuse multiple base models. The reason may need further theoretical investigations about why different base models contribute to each other for the MCI prediction.
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Affiliation(s)
- Jialiang Li
- BioKnow Health Informatics Laboratory, College of Software, Jilin University, Changchun 130012, China
- Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun 130012, China
| | - Zhaomin Yao
- Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun 130012, China
- BioKnow Health Informatics Laboratory, College of Computer Science and Technology, Jilin University, Changchun 130012, China
- Cancer Systems Biology Center, China-Japan Union Hospital of Jilin University, Changchun 130012, China
| | - Meiyu Duan
- Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun 130012, China
- BioKnow Health Informatics Laboratory, College of Computer Science and Technology, Jilin University, Changchun 130012, China
| | - Shuai Liu
- Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun 130012, China
- BioKnow Health Informatics Laboratory, College of Computer Science and Technology, Jilin University, Changchun 130012, China
| | - Fei Li
- BioKnow Health Informatics Laboratory, College of Software, Jilin University, Changchun 130012, China
- Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun 130012, China
| | - Haiyang Zhu
- Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun 130012, China
- BioKnow Health Informatics Laboratory, College of Computer Science and Technology, Jilin University, Changchun 130012, China
| | - Zhiqiang Xia
- Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun 130012, China
- BioKnow Health Informatics Laboratory, College of Computer Science and Technology, Jilin University, Changchun 130012, China
| | - Lan Huang
- Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun 130012, China
- BioKnow Health Informatics Laboratory, College of Computer Science and Technology, Jilin University, Changchun 130012, China
| | - Fengfeng Zhou
- BioKnow Health Informatics Laboratory, College of Software, Jilin University, Changchun 130012, China
- Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun 130012, China
- BioKnow Health Informatics Laboratory, College of Computer Science and Technology, Jilin University, Changchun 130012, China
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Feng X, Li J, Li H, Chen H, Li F, Liu Q, You ZH, Zhou F. Age Is Important for the Early-Stage Detection of Breast Cancer on Both Transcriptomic and Methylomic Biomarkers. Front Genet 2019; 10:212. [PMID: 30984234 PMCID: PMC6448048 DOI: 10.3389/fgene.2019.00212] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2018] [Accepted: 02/27/2019] [Indexed: 12/27/2022] Open
Abstract
Patients at different ages have different rates of cell development and metabolisms. As a result, age should be an essential part of how a disease diagnosis model is trained and optimized. Unfortunately, most of the existing studies have not taken age into account. This study demonstrated that disease diagnosis models could be improved by merely applying individual models for patients of different age groups. Both transcriptomes and methylomes of the TCGA breast cancer dataset (TCGA-BRCA) were utilized for the analysis procedure of feature selection and classification. Our experimental data strongly suggested that disease diagnosis modeling should integrate patient age into the whole experimental design.
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Affiliation(s)
- Xin Feng
- BioKnow Health Informatics Lab, College of Computer Science and Technology, Jilin University, Changchun, China.,Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, China
| | - Jialiang Li
- BioKnow Health Informatics Lab, College of Software, Jilin University, Changchun, China.,Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, China
| | - Han Li
- BioKnow Health Informatics Lab, College of Software, Jilin University, Changchun, China.,Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, China
| | - Hang Chen
- BioKnow Health Informatics Lab, College of Computer Science and Technology, Jilin University, Changchun, China.,Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, China
| | - Fei Li
- BioKnow Health Informatics Lab, College of Software, Jilin University, Changchun, China.,Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, China
| | - Quewang Liu
- BioKnow Health Informatics Lab, College of Computer Science and Technology, Jilin University, Changchun, China.,Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, China
| | - Zhu-Hong You
- Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Ürümqi, China
| | - Fengfeng Zhou
- BioKnow Health Informatics Lab, College of Computer Science and Technology, Jilin University, Changchun, China.,Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, China.,BioKnow Health Informatics Lab, College of Software, Jilin University, Changchun, China.,Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, China
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