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Wang H, Han X, Ren J, Cheng H, Li H, Li Y, Li X. A prognostic prediction model for ovarian cancer using a cross-modal view correlation discovery network. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2024; 21:736-764. [PMID: 38303441 DOI: 10.3934/mbe.2024031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/03/2024]
Abstract
Ovarian cancer is a tumor with different clinicopathological and molecular features, and the vast majority of patients have local or extensive spread at the time of diagnosis. Early diagnosis and prognostic prediction of patients can contribute to the understanding of the underlying pathogenesis of ovarian cancer and the improvement of therapeutic outcomes. The occurrence of ovarian cancer is influenced by multiple complex mechanisms, including the genome, transcriptome and proteome. Different types of omics analysis help predict the survival rate of ovarian cancer patients. Multi-omics data of ovarian cancer exhibit high-dimensional heterogeneity, and existing methods for integrating multi-omics data have not taken into account the variability and inter-correlation between different omics data. In this paper, we propose a deep learning model, MDCADON, which utilizes multi-omics data and cross-modal view correlation discovery network. We introduce random forest into LASSO regression for feature selection on mRNA expression, DNA methylation, miRNA expression and copy number variation (CNV), aiming to select important features highly correlated with ovarian cancer prognosis. A multi-modal deep neural network is used to comprehensively learn feature representations of each omics data and clinical data, and cross-modal view correlation discovery network is employed to construct the multi-omics discovery tensor, exploring the inter-relationships between different omics data. The experimental results demonstrate that MDCADON is superior to the existing methods in predicting ovarian cancer prognosis, which enables survival analysis for patients and facilitates the determination of follow-up treatment plans. Finally, we perform Gene Ontology (GO) term analysis and biological pathway analysis on the genes identified by MDCADON, revealing the underlying mechanisms of ovarian cancer and providing certain support for guiding ovarian cancer treatments.
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Affiliation(s)
- Huiqing Wang
- College of Computer Science and Technology (College of Data Science), Taiyuan University of Technology, Taiyuan 030024, China
| | - Xiao Han
- College of Computer Science and Technology (College of Data Science), Taiyuan University of Technology, Taiyuan 030024, China
| | - Jianxue Ren
- College of Computer Science and Technology (College of Data Science), Taiyuan University of Technology, Taiyuan 030024, China
| | - Hao Cheng
- College of Computer Science and Technology (College of Data Science), Taiyuan University of Technology, Taiyuan 030024, China
| | - Haolin Li
- College of Computer Science and Technology (College of Data Science), Taiyuan University of Technology, Taiyuan 030024, China
| | - Ying Li
- College of Computer Science and Technology (College of Data Science), Taiyuan University of Technology, Taiyuan 030024, China
| | - Xue Li
- College of Computer Science and Technology (College of Data Science), Taiyuan University of Technology, Taiyuan 030024, China
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Bansal B, Sahoo A. Multi-omics data fusion using adaptive GTO guided Non-negative matrix factorization for cancer subtype discovery. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 228:107246. [PMID: 36434961 DOI: 10.1016/j.cmpb.2022.107246] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/10/2022] [Revised: 11/13/2022] [Accepted: 11/14/2022] [Indexed: 06/16/2023]
Abstract
BACKGROUND AND OBJECTIVE Cancer subtype discovery is essential for personalized clinical treatment. With the onset of progressive profile techniques for cancer, a large amount of heterogeneous and high-dimensional transcriptomic, proteomic and genomic datasets are easily accumulated. Integrative clustering of such multi-omics data is crucial to recognize their latent structure and to acknowledge the correlation within and across them. Although the integrative analysis of diversified multi-omics data is informative, it is challenging when multiplicity in data inflicts poor accordance w.r.t. clustering structure. The objective of this work is to develop an effective integrative analysis framework that encapsulates the heterogeneity of various biological mechanisms and predicts homogeneous subgroups of cancer patients. METHOD In this paper, improved sparse-joint non-negative matrix factorization (sparse-jNMF) has been devised for the problem of cancer-subtype discovery. The initial points of sparse-jNMF have improved using a novel meta-heuristic algorithm adaptive gorilla troops optimizer (Ada-GTO). Improving the initialization of sparse-jNMF enhances its convergence behavior and further strengthens the clustering performance. In addition, the consensus clustering approach has been adopted to construct a patient-patient similarity matrix for obtaining stable clusters of patient samples. RESULT The proposed framework has been applied to 4 different real-life multi-omics cancer datasets, namely colon adenocarcinoma, breast-invasive carcinoma, kidney-renal clear-cell carcinoma, and lung adenocarcinoma. The proposed method results in patient clusters with better silhouette scores and cluster purity than classical initialization and similar meta-heuristics for initial point estimation approaches. Survival probabilities estimated using Kaplan-Meier (KM) curve show statistically significant difference (p < 0.05) for the homogenous cancer patient clusters obtained using the proposed method as compared to iCluster. The presented approach further identified the somatic mutations for the classified subgroups, which is beneficial to provide targeted treatments. CONCLUSION This paper proposes Ada-GTO guided sparse-jNMF framework for cancer subtype discovery, considering the information from multiple omic features that provide comprehension. The proposed meta-guided framework outperforms all other state-of-the-art counterparts. It also has great potential for obtaining the homogeneous subgroups of other diseases.
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Affiliation(s)
- Bhavana Bansal
- Department of CSE & IT, Jaypee Institute of Information Technology, Noida, India.
| | - Anita Sahoo
- Department of CSE & IT, Jaypee Institute of Information Technology, Noida, India
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Zhao L, Ma Y, Chen S, Zhou J. Multi-view co-clustering with multi-similarity. APPL INTELL 2022. [DOI: 10.1007/s10489-022-04385-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
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Zhang L, Xu F. Asynchronous spiking neural P systems with rules on synapses and coupled neurons. Knowl Based Syst 2022. [DOI: 10.1016/j.knosys.2022.109896] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2022]
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Wen LY, Zhang XM, Li QF, Min F. KGA: integrating KPCA and GAN for microbial data augmentation. INT J MACH LEARN CYB 2022. [DOI: 10.1007/s13042-022-01707-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
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Wang Y, Tang S, Ma R, Zamit I, Wei Y, Pan Y. Multi-modal intermediate integrative methods in neuropsychiatric disorders: A review. Comput Struct Biotechnol J 2022; 20:6149-6162. [DOI: 10.1016/j.csbj.2022.11.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Revised: 11/04/2022] [Accepted: 11/04/2022] [Indexed: 11/09/2022] Open
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Robust unsupervised feature selection via sparse and minimum-redundant subspace learning with dual regularization. Neurocomputing 2022. [DOI: 10.1016/j.neucom.2022.09.074] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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Li C, Lin J, Yang T, Xiao Y, Jiang Q, Shang H. Physical activity and risk of multiple sclerosis: A Mendelian randomization study. Front Immunol 2022; 13:872126. [PMID: 36211369 PMCID: PMC9532251 DOI: 10.3389/fimmu.2022.872126] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2022] [Accepted: 09/07/2022] [Indexed: 11/29/2022] Open
Abstract
Multiple evidence from epidemiological studies has suggested association between physical activity and risk of multiple sclerosis (MS). However, the conclusion was still controversial between studies, and whether the association was causal or confounded is elusive. To evaluate the role of physical activity with different intensities in the risk of MS, we first estimated their genetic correlation, and then conducted two-sample and multivariable Mendelian randomization analyses based on summary statistics from previous large genome-wide association studies. A significant genetic correlation was identified between moderate physical activity and the risk of MS (genetic correlation: -0.15, SE=0.05, P=2.9E-03). Meanwhile, higher moderate physical activity was significantly associated with a reduced risk of MS (OR:0.87, 95% CI:0.80-0.96, P=3.45E-03). Such association was further verified using summary statistics from another study on overall physical activity (OR:0.36, 95% CI:0.17-0.76, P=6.82E-03). The results were robust under all sensitivity analyses. Current results suggested moderate physical activity could reduce the risk of MS. These findings help better understand the role of physical activity in MS, and provide some lifestyle recommendations for individuals susceptible to MS.
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Rapid Person Re-Identification via Sub-space Consistency Regularization. Neural Process Lett 2022. [DOI: 10.1007/s11063-022-11002-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/15/2022]
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Logarithmic Negation of Basic Probability Assignment and Its Application in Target Recognition. INFORMATION 2022. [DOI: 10.3390/info13080387] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
The negation of probability distribution is a new perspective from which to obtain information. Dempster–Shafer (D–S) evidence theory, as an extension of possibility theory, is widely used in decision-making-level fusion. However, how to reasonably construct the negation of basic probability assignment (BPA) in D–S evidence theory is an open issue. This paper proposes a new negation of BPA, logarithmic negation. It solves the shortcoming of Yin’s negation that maximal entropy cannot be obtained when there are only two focal elements in the BPA. At the same time, the logarithmic negation of BPA inherits the good properties of the negation of probability, such as order reversal, involution, convergence, degeneration, and maximal entropy. Logarithmic negation degenerates into Gao’s negation when the values of the elements all approach 0. In addition, the data fusion method based on logarithmic negation has a higher belief value of the correct target in target recognition application.
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Clustering of noised and heterogeneous multi-view data with graph learning and projection decomposition. Knowl Based Syst 2022. [DOI: 10.1016/j.knosys.2022.109736] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
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Yu Y, Zhou G, Huang H, Xie S, Zhao Q. A semi-supervised label-driven auto-weighted strategy for multi-view data classification. Knowl Based Syst 2022. [DOI: 10.1016/j.knosys.2022.109694] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/31/2022]
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Wang S, Chen Y, Yi S, Chao G. Frobenius norm-regularized robust graph learning for multi-view subspace clustering. APPL INTELL 2022. [DOI: 10.1007/s10489-022-03816-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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Liang N, Yang Z, Li Z, Han W. Incomplete multi-view clustering with incomplete graph-regularized orthogonal non-negative matrix factorization. APPL INTELL 2022. [DOI: 10.1007/s10489-022-03551-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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Salient and consensus representation learning based incomplete multiview clustering. APPL INTELL 2022. [DOI: 10.1007/s10489-022-03530-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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16
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Face aging with pixel-level alignment GAN. APPL INTELL 2022. [DOI: 10.1007/s10489-022-03541-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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Sun L, Wen J, Wang J, Zhao Y, Zhang B, Wu J, Xu Y. Two‐view attention‐guided convolutional neural network for mammographic image classification. CAAI TRANSACTIONS ON INTELLIGENCE TECHNOLOGY 2022. [DOI: 10.1049/cit2.12096] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022] Open
Affiliation(s)
- Lilei Sun
- College of Computer Science and Technology Guizhou University Guiyang China
- Shenzhen Key Laboratory of Visual Object Detection and Recognition Harbin Institute of Technology Shenzhen China
| | - Jie Wen
- Shenzhen Key Laboratory of Visual Object Detection and Recognition Harbin Institute of Technology Shenzhen China
- Harbin Institute of Technology Shenzhen China
| | - Junqian Wang
- Shenzhen Key Laboratory of Visual Object Detection and Recognition Harbin Institute of Technology Shenzhen China
- Harbin Institute of Technology Shenzhen China
| | - Yong Zhao
- College of Computer Science and Technology Guizhou University Guiyang China
- School of Electronic and Computer Engineering Shenzhen Graduate School of Peking University Shenzhen China
| | - Bob Zhang
- Department of Computer and Information Science University of Macau Taipa China
| | - Jian Wu
- Science for Life Laboratory KTH Royal Institute of Technology Stockholm Sweden
| | - Yong Xu
- Shenzhen Key Laboratory of Visual Object Detection and Recognition Harbin Institute of Technology Shenzhen China
- Harbin Institute of Technology Shenzhen China
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Wang S, Chen Y, Cen Y, Zhang L, Wang H, Voronin V. Nonconvex low-rank and sparse tensor representation for multi-view subspace clustering. APPL INTELL 2022. [DOI: 10.1007/s10489-022-03406-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
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Wu X, Ji S, Wang J, Guo Y. Speech synthesis with face embeddings. APPL INTELL 2022. [DOI: 10.1007/s10489-022-03227-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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Multi-view k-proximal plane clustering. APPL INTELL 2022. [DOI: 10.1007/s10489-022-03176-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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21
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Zhu J, Wang H, Li H, Zhang Q. Fast multi-view twin hypersphere support vector machine with consensus and complementary principles. APPL INTELL 2022. [DOI: 10.1007/s10489-021-02986-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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22
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Machine learning techniques for diagnosis of alzheimer disease, mild cognitive disorder, and other types of dementia. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2021.103293] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
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Chen H, Tai X, Wang W. Multi-view subspace clustering with inter-cluster consistency and intra-cluster diversity among views. APPL INTELL 2022. [DOI: 10.1007/s10489-021-02895-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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24
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Weighted multi-view co-clustering (WMVCC) for sparse data. APPL INTELL 2022. [DOI: 10.1007/s10489-021-02405-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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25
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Zhang YF, Wang YQ, Li GG, Gao QQ, Gao Q, Xiong ZY, Zhang M. A novel clustering algorithm based on the gravity-mass-square ratio and density core with a dynamic denoising radius. APPL INTELL 2021. [DOI: 10.1007/s10489-021-02753-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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Rethinking modeling Alzheimer's disease progression from a multi-task learning perspective with deep recurrent neural network. Comput Biol Med 2021; 138:104935. [PMID: 34656869 DOI: 10.1016/j.compbiomed.2021.104935] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2021] [Revised: 10/07/2021] [Accepted: 10/08/2021] [Indexed: 11/22/2022]
Abstract
Alzheimer's disease (AD) is a severe neurodegenerative disorder that usually starts slowly and progressively worsens. Predicting the progression of Alzheimer's disease with longitudinal analysis on the time series data has recently received increasing attention. However, training an accurate progression model for brain network faces two major challenges: missing features, and the small sample size during the follow-up study. According to our analysis on the AD progression task, we thoroughly analyze the correlation among the multiple predictive tasks of AD progression at multiple time points. Thus, we propose a multi-task learning framework that can adaptively impute missing values and predict future progression over time from a subject's historical measurements. Progression is measured in terms of MRI volumetric measurements, trajectories of a cognitive score and clinical status. To this end, we propose a new perspective of predicting the AD progression with a multi-task learning paradigm. In our multi-task learning paradigm, we hypothesize that the inherent correlations exist among: (i). the prediction tasks of clinical diagnosis, cognition and ventricular volume at each time point; (ii). the tasks of imputation and prediction; and (iii). the prediction tasks at multiple future time points. According to our findings of the task correlation, we develop an end-to-end deep multi-task learning method to jointly improve the performance of assigning missing value and prediction. We design a balanced multi-task dynamic weight optimization. With in-depth analysis and empirical evidence on Alzheimer's Disease Neuroimaging Initiative (ADNI), we show the benefits and flexibility of the proposed multi-task learning model, especially for the prediction at the M60 time point. The proposed approach achieves 5.6%, 5.7%, 4.0% and 11.8% improvement with respect to mAUC, BCA and MAE (ADAS-Cog13 and Ventricles), respectively.
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Wang H, Hu J, Song Y, Zhang L, Bai S, Yi Z. Multi-view fusion segmentation for brain glioma on CT images. APPL INTELL 2021. [DOI: 10.1007/s10489-021-02784-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
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A dual-branch model for diagnosis of Parkinson’s disease based on the independent and joint features of the left and right gait. APPL INTELL 2021. [DOI: 10.1007/s10489-020-02182-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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Howlett J, Hill SM, Ritchie CW, Tom BDM. Disease Modelling of Cognitive Outcomes and Biomarkers in the European Prevention of Alzheimer's Dementia Longitudinal Cohort. Front Big Data 2021; 4:676168. [PMID: 34490422 PMCID: PMC8417903 DOI: 10.3389/fdata.2021.676168] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2021] [Accepted: 07/30/2021] [Indexed: 12/04/2022] Open
Abstract
A key challenge for the secondary prevention of Alzheimer’s dementia is the need to identify individuals early on in the disease process through sensitive cognitive tests and biomarkers. The European Prevention of Alzheimer’s Dementia (EPAD) consortium recruited participants into a longitudinal cohort study with the aim of building a readiness cohort for a proof-of-concept clinical trial and also to generate a rich longitudinal data-set for disease modelling. Data have been collected on a wide range of measurements including cognitive outcomes, neuroimaging, cerebrospinal fluid biomarkers, genetics and other clinical and environmental risk factors, and are available for 1,828 eligible participants at baseline, 1,567 at 6 months, 1,188 at one-year follow-up, 383 at 2 years, and 89 participants at three-year follow-up visit. We novelly apply state-of-the-art longitudinal modelling and risk stratification approaches to these data in order to characterise disease progression and biological heterogeneity within the cohort. Specifically, we use longitudinal class-specific mixed effects models to characterise the different clinical disease trajectories and a semi-supervised Bayesian clustering approach to explore whether participants can be stratified into homogeneous subgroups that have different patterns of cognitive functioning evolution, while also having subgroup-specific profiles in terms of baseline biomarkers and longitudinal rate of change in biomarkers.
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Affiliation(s)
- James Howlett
- MRC Biostatistics Unit, University of Cambridge, Cambridge, United Kingdom
| | - Steven M Hill
- MRC Biostatistics Unit, University of Cambridge, Cambridge, United Kingdom
| | - Craig W Ritchie
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom
| | - Brian D M Tom
- MRC Biostatistics Unit, University of Cambridge, Cambridge, United Kingdom
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Qiao H, Chen L, Ye Z, Zhu F. Early Alzheimer's disease diagnosis with the contrastive loss using paired structural MRIs. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2021; 208:106282. [PMID: 34343744 DOI: 10.1016/j.cmpb.2021.106282] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/30/2021] [Accepted: 07/08/2021] [Indexed: 06/13/2023]
Abstract
BACKGROUND AND OBJECTIVE Alzheimer's Disease (AD) is a chronic and fatal neurodegenerative disease with progressive impairment of memory. Brain structural magnetic resonance imaging (sMRI) has been widely applied as important biomarkers of AD. Various machine learning approaches, especially deep learning-based models, have been proposed for the early diagnosis of AD and monitoring the disease progression on sMRI data. However, the requirement for a large number of training images still hinders the extensive usage of AD diagnosis. In addition, due to the similarities in human whole-brain structure, finding the subtle brain changes is essential to extract discriminative features from limited sMRI data effectively. METHODS In this work, we proposed two types of contrastive losses with paired sMRIs to promote the diagnostic performance using group categories (G-CAT) and varying subject mini-mental state examination (S-MMSE) information, respectively. Specifically, G-CAT contrastive loss layer was used to learn the closer feature representation from sMRIs with the same categories, while ranking information from S-MMSE assists the model to explore subtle changes between individuals. RESULTS The model was trained on ADNI-1. Comparison with baseline methods was performed on MIRIAD and ADNI-2. For the classification task on MIRIAD, S-MMSE achieves 93.5% of accuracy, 96.6% of sensitivity, and 94.9% of specificity, respectively. G-CAT and S-MMSE both reach remarkable performance in terms of classification sensitivity and specificity respectively. Comparing with state-of-the-art methods, we found this proposed method could achieve comparable results with other approaches. CONCLUSION The proposed model could extract discriminative features under whole-brain similarity. Extensive experiments also support the accuracy of this model, i.e., it provides better ability to identify uncertain samples, especially for the classification task of subjects with MMSE in 22-27. Source code is freely available at https://github.com/fengduqianhe/ADComparative.
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Affiliation(s)
- Hezhe Qiao
- Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing 400714, China; University of Chinese Academy of Sciences, BeiJing 100049, China.
| | - Lin Chen
- Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing 400714, China.
| | - Zi Ye
- Johns Hopkins University, Baltimore, MD 21218, United States of America.
| | - Fan Zhu
- Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing 400714, China.
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A patient network-based machine learning model for disease prediction: The case of type 2 diabetes mellitus. APPL INTELL 2021. [DOI: 10.1007/s10489-021-02533-w] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
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Ebrahimi A, Luo S, Chiong R. Deep sequence modelling for Alzheimer's disease detection using MRI. Comput Biol Med 2021; 134:104537. [PMID: 34118752 DOI: 10.1016/j.compbiomed.2021.104537] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2021] [Revised: 05/27/2021] [Accepted: 05/27/2021] [Indexed: 12/21/2022]
Abstract
BACKGROUND Alzheimer's disease (AD) is one of the deadliest diseases in developed countries. Treatments following early AD detection can significantly delay institutionalisation and extend patients' independence. There has been a growing focus on early AD detection using artificial intelligence. Convolutional neural networks (CNNs) have proven revolutionary for image-based applications and have been applied to brain scans. In recent years, studies have utilised two-dimensional (2D) CNNs on magnetic resonance imaging (MRI) scans for AD detection. To apply a 2D CNN on three-dimensional (3D) MRI volumes, each MRI scan is split into 2D image slices. A CNN is trained over the image slices by calculating a loss function between each subject's label and each image slice's predicted output. Although 2D CNNs can discover spatial dependencies in an image slice, they cannot understand the temporal dependencies among 2D image slices in a 3D MRI volume. This study aims to resolve this issue by modelling the sequence of MRI features produced by a CNN with deep sequence-based networks for AD detection. METHOD The CNN utilised in this paper was ResNet-18 pre-trained on an ImageNet dataset. The employed sequence-based models were the temporal convolutional network (TCN) and different types of recurrent neural networks. Several deep sequence-based models and configurations were implemented and compared for AD detection. RESULTS Our proposed TCN model achieved the best classification performance with 91.78% accuracy, 91.56% sensitivity and 92% specificity. CONCLUSION Our results show that applying sequence-based models can improve the classification accuracy of 2D and 3D CNNs for AD detection by up to 10%.
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Affiliation(s)
- Amir Ebrahimi
- School of Electrical Engineering and Computing, The University of Newcastle, NSW 2308, Australia
| | - Suhuai Luo
- School of Electrical Engineering and Computing, The University of Newcastle, NSW 2308, Australia
| | - Raymond Chiong
- School of Electrical Engineering and Computing, The University of Newcastle, NSW 2308, Australia.
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Wang S, Du Z, Ding M, Rodriguez-Paton A, Song T. KG-DTI: a knowledge graph based deep learning method for drug-target interaction predictions and Alzheimer’s disease drug repositions. APPL INTELL 2021. [DOI: 10.1007/s10489-021-02454-8] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
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