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Zhang S, Wang Z, Wang Y, Zhu Y, Zhou Q, Jian X, Zhao G, Qiu J, Xia K, Tang B, Mutz J, Li J, Li B. A metabolomic profile of biological aging in 250,341 individuals from the UK Biobank. Nat Commun 2024; 15:8081. [PMID: 39278973 PMCID: PMC11402978 DOI: 10.1038/s41467-024-52310-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2024] [Accepted: 09/02/2024] [Indexed: 09/18/2024] Open
Abstract
The metabolomic profile of aging is complex. Here, we analyse 325 nuclear magnetic resonance (NMR) biomarkers from 250,341 UK Biobank participants, identifying 54 representative aging-related biomarkers associated with all-cause mortality. We conduct genome-wide association studies (GWAS) for these 325 biomarkers using whole-genome sequencing (WGS) data from 95,372 individuals and perform multivariable Mendelian randomization (MVMR) analyses, discovering 439 candidate "biomarker - disease" causal pairs at the nominal significance level. We develop a metabolomic aging score that outperforms other aging metrics in predicting short-term mortality risk and exhibits strong potential for discriminating aging-accelerated populations and improving disease risk prediction. A longitudinal analysis of 13,263 individuals enables us to calculate a metabolomic aging rate which provides more refined aging assessments and to identify candidate anti-aging and pro-aging NMR biomarkers. Taken together, our study has presented a comprehensive aging-related metabolomic profile and highlighted its potential for personalized aging monitoring and early disease intervention.
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Affiliation(s)
- Shiyu Zhang
- National Clinical Research Center for Geriatric Disorders, Department of Geriatrics, Xiangya Hospital & Center for Medical Genetics, School of Life Sciences, Central South University, Changsha, Hunan, 410008, China
- Xiangya School of Medicine, Central South University, Changsha, Hunan, 410013, China
| | - Zheng Wang
- National Clinical Research Center for Geriatric Disorders, Department of Geriatrics, Xiangya Hospital & Center for Medical Genetics, School of Life Sciences, Central South University, Changsha, Hunan, 410008, China
- Key Laboratory of Hunan Province in Neurodegenerative Disorders, Department of Neurology, Xiangya Hospital, Central South University, Changsha, Hunan, 410008, China
- Hunan Key Laboratory of Molecular Precision Medicine, Xiangya Hospital, Central South University, Changsha, Hunan, 410008, China
| | - Yijing Wang
- National Clinical Research Center for Geriatric Disorders, Department of Geriatrics, Xiangya Hospital & Center for Medical Genetics, School of Life Sciences, Central South University, Changsha, Hunan, 410008, China
| | - Yixiao Zhu
- National Clinical Research Center for Geriatric Disorders, Department of Geriatrics, Xiangya Hospital & Center for Medical Genetics, School of Life Sciences, Central South University, Changsha, Hunan, 410008, China
| | - Qiao Zhou
- National Clinical Research Center for Geriatric Disorders, Department of Geriatrics, Xiangya Hospital & Center for Medical Genetics, School of Life Sciences, Central South University, Changsha, Hunan, 410008, China
| | - Xingxing Jian
- National Clinical Research Center for Geriatric Disorders, Department of Geriatrics, Xiangya Hospital & Center for Medical Genetics, School of Life Sciences, Central South University, Changsha, Hunan, 410008, China
| | - Guihu Zhao
- National Clinical Research Center for Geriatric Disorders, Department of Geriatrics, Xiangya Hospital & Center for Medical Genetics, School of Life Sciences, Central South University, Changsha, Hunan, 410008, China
| | - Jian Qiu
- National Clinical Research Center for Geriatric Disorders, Department of Geriatrics, Xiangya Hospital & Center for Medical Genetics, School of Life Sciences, Central South University, Changsha, Hunan, 410008, China
- Key Laboratory of Hunan Province in Neurodegenerative Disorders, Department of Neurology, Xiangya Hospital, Central South University, Changsha, Hunan, 410008, China
- Hunan Key Laboratory of Molecular Precision Medicine, Xiangya Hospital, Central South University, Changsha, Hunan, 410008, China
| | - Kun Xia
- MOE Key Laboratory of Pediatric Rare Diseases & Hunan Key Laboratory of Medical Genetics, Central South University, Changsha, Hunan, 410008, China
| | - Beisha Tang
- National Clinical Research Center for Geriatric Disorders, Department of Geriatrics, Xiangya Hospital & Center for Medical Genetics, School of Life Sciences, Central South University, Changsha, Hunan, 410008, China
- Key Laboratory of Hunan Province in Neurodegenerative Disorders, Department of Neurology, Xiangya Hospital, Central South University, Changsha, Hunan, 410008, China
- Department of Neurology & Multi-omics Research Center for Brain Disorders, The First Affiliated Hospital University of South China, Hengyang, Hunan, China
| | - Julian Mutz
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK.
| | - Jinchen Li
- National Clinical Research Center for Geriatric Disorders, Department of Geriatrics, Xiangya Hospital & Center for Medical Genetics, School of Life Sciences, Central South University, Changsha, Hunan, 410008, China.
- Key Laboratory of Hunan Province in Neurodegenerative Disorders, Department of Neurology, Xiangya Hospital, Central South University, Changsha, Hunan, 410008, China.
- Bioinformatics Center, Xiangya Hospital & Furong Laboratory, Changsha, Hunan, 410008, China.
| | - Bin Li
- National Clinical Research Center for Geriatric Disorders, Department of Geriatrics, Xiangya Hospital & Center for Medical Genetics, School of Life Sciences, Central South University, Changsha, Hunan, 410008, China.
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Maekawa E, Grua EM, Nakamura CA, Scazufca M, Araya R, Peters T, van de Ven P. Bayesian Networks for Prescreening in Depression: Algorithm Development and Validation. JMIR Ment Health 2024; 11:e52045. [PMID: 38963925 PMCID: PMC11258528 DOI: 10.2196/52045] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/21/2023] [Revised: 04/02/2024] [Accepted: 04/17/2024] [Indexed: 07/06/2024] Open
Abstract
BACKGROUND Identifying individuals with depressive symptomatology (DS) promptly and effectively is of paramount importance for providing timely treatment. Machine learning models have shown promise in this area; however, studies often fall short in demonstrating the practical benefits of using these models and fail to provide tangible real-world applications. OBJECTIVE This study aims to establish a novel methodology for identifying individuals likely to exhibit DS, identify the most influential features in a more explainable way via probabilistic measures, and propose tools that can be used in real-world applications. METHODS The study used 3 data sets: PROACTIVE, the Brazilian National Health Survey (Pesquisa Nacional de Saúde [PNS]) 2013, and PNS 2019, comprising sociodemographic and health-related features. A Bayesian network was used for feature selection. Selected features were then used to train machine learning models to predict DS, operationalized as a score of ≥10 on the 9-item Patient Health Questionnaire. The study also analyzed the impact of varying sensitivity rates on the reduction of screening interviews compared to a random approach. RESULTS The methodology allows the users to make an informed trade-off among sensitivity, specificity, and a reduction in the number of interviews. At the thresholds of 0.444, 0.412, and 0.472, determined by maximizing the Youden index, the models achieved sensitivities of 0.717, 0.741, and 0.718, and specificities of 0.644, 0.737, and 0.766 for PROACTIVE, PNS 2013, and PNS 2019, respectively. The area under the receiver operating characteristic curve was 0.736, 0.801, and 0.809 for these 3 data sets, respectively. For the PROACTIVE data set, the most influential features identified were postural balance, shortness of breath, and how old people feel they are. In the PNS 2013 data set, the features were the ability to do usual activities, chest pain, sleep problems, and chronic back problems. The PNS 2019 data set shared 3 of the most influential features with the PNS 2013 data set. However, the difference was the replacement of chronic back problems with verbal abuse. It is important to note that the features contained in the PNS data sets differ from those found in the PROACTIVE data set. An empirical analysis demonstrated that using the proposed model led to a potential reduction in screening interviews of up to 52% while maintaining a sensitivity of 0.80. CONCLUSIONS This study developed a novel methodology for identifying individuals with DS, demonstrating the utility of using Bayesian networks to identify the most significant features. Moreover, this approach has the potential to substantially reduce the number of screening interviews while maintaining high sensitivity, thereby facilitating improved early identification and intervention strategies for individuals experiencing DS.
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Affiliation(s)
- Eduardo Maekawa
- Department of Electronic and Computer Engineering, University of Limerick, Limerick, Ireland
- Health Research Institute, University of Limerick, Limerick, Ireland
| | - Eoin Martino Grua
- Department of Electronic and Computer Engineering, University of Limerick, Limerick, Ireland
- Health Research Institute, University of Limerick, Limerick, Ireland
| | - Carina Akemi Nakamura
- Departamento de Psiquiatria, Faculdade de Medicina da Universidade de Sao Paulo, Universidade de Sao Paulo, Sao Paulo, Brazil
| | - Marcia Scazufca
- Departamento de Psiquiatria, Faculdade de Medicina da Universidade de Sao Paulo, Universidade de Sao Paulo, Sao Paulo, Brazil
- Instituto de Psiquiatria, Hospital das Clinicas da Faculdade de Medicina da Universidade de Sao Paulo, Faculdade de Medicina, Universidade de Sao Paulo, Sao Paulo, Brazil
| | - Ricardo Araya
- Centre for Global Mental Health, King's College London, London, United Kingdom
| | - Tim Peters
- Bristol Dental School, University of Bristol, Bristol, United Kingdom
| | - Pepijn van de Ven
- Department of Electronic and Computer Engineering, University of Limerick, Limerick, Ireland
- Health Research Institute, University of Limerick, Limerick, Ireland
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Tang J, Mou M, Zheng X, Yan J, Pan Z, Zhang J, Li B, Yang Q, Wang Y, Zhang Y, Gao J, Li S, Yang H, Zhu F. Strategy for Identifying a Robust Metabolomic Signature Reveals the Altered Lipid Metabolism in Pituitary Adenoma. Anal Chem 2024; 96:4745-4755. [PMID: 38417094 DOI: 10.1021/acs.analchem.3c03796] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/01/2024]
Abstract
Despite the well-established connection between systematic metabolic abnormalities and the pathophysiology of pituitary adenoma (PA), current metabolomic studies have reported an extremely limited number of metabolites associated with PA. Moreover, there was very little consistency in the identified metabolite signatures, resulting in a lack of robust metabolic biomarkers for the diagnosis and treatment of PA. Herein, we performed a global untargeted plasma metabolomic profiling on PA and identified a highly robust metabolomic signature based on a strategy. Specifically, this strategy is unique in (1) integrating repeated random sampling and a consensus evaluation-based feature selection algorithm and (2) evaluating the consistency of metabolomic signatures among different sample groups. This strategy demonstrated superior robustness and stronger discriminative ability compared with that of other feature selection methods including Student's t-test, partial least-squares-discriminant analysis, support vector machine recursive feature elimination, and random forest recursive feature elimination. More importantly, a highly robust metabolomic signature comprising 45 PA-specific differential metabolites was identified. Moreover, metabolite set enrichment analysis of these potential metabolic biomarkers revealed altered lipid metabolism in PA. In conclusion, our findings contribute to a better understanding of the metabolic changes in PA and may have implications for the development of diagnostic and therapeutic approaches targeting lipid metabolism in PA. We believe that the proposed strategy serves as a valuable tool for screening robust, discriminating metabolic features in the field of metabolomics.
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Affiliation(s)
- Jing Tang
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
- Department of Bioinformatics, Chongqing Medical University, Chongqing 400016, China
| | - Minjie Mou
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Xin Zheng
- Multidisciplinary Center for Pituitary Adenoma of Chongqing, Department of Neuosurgery, Xinqiao Hospital, Army Medical University, Chongqing 400037, China
| | - Jin Yan
- Multidisciplinary Center for Pituitary Adenoma of Chongqing, Department of Neuosurgery, Xinqiao Hospital, Army Medical University, Chongqing 400037, China
| | - Ziqi Pan
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Jinsong Zhang
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Bo Li
- School of Pharmaceutical Sciences, Chongqing University, Chongqing 401331, China
| | - Qingxia Yang
- School of Pharmaceutical Sciences, Chongqing University, Chongqing 401331, China
| | - Yunxia Wang
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Ying Zhang
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Jianqing Gao
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Song Li
- Multidisciplinary Center for Pituitary Adenoma of Chongqing, Department of Neuosurgery, Xinqiao Hospital, Army Medical University, Chongqing 400037, China
| | - Hui Yang
- Multidisciplinary Center for Pituitary Adenoma of Chongqing, Department of Neuosurgery, Xinqiao Hospital, Army Medical University, Chongqing 400037, China
| | - Feng Zhu
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou 330110, China
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Han J, Li H, Lin H, Wu P, Wang S, Tu J, Lu J. Depression prediction based on LassoNet-RNN model: A longitudinal study. Heliyon 2023; 9:e20684. [PMID: 37842633 PMCID: PMC10570602 DOI: 10.1016/j.heliyon.2023.e20684] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Revised: 09/21/2023] [Accepted: 10/04/2023] [Indexed: 10/17/2023] Open
Abstract
Depression has become a widespread health concern today. Understanding the influencing factors can promote human mental health as well as provide a basis for exploring preventive measures. Combining LassoNet with recurrent neural network (RNN), this study constructed a screening model ,LassoNet-RNN, for identifying influencing factors of individual depression. Based on multi-wave surveys of China Health and Retirement Longitudinal Study (CHARLS) dataset (11,661 observations), we analyzed the multivariate time series data and recognized 27 characteristic variables selected from four perspectives: demographics, health-related risk factors, household economic status, and living environment. Additionally, the importance rankings of the characteristic variables were obtained. These results offered insightful recommendations for theoretical developments and practical decision making in public health.
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Affiliation(s)
- Jiatong Han
- School of Computer Science, Nanjing Audit University, China
| | - Hao Li
- School of Computer Science, Nanjing Audit University, China
| | - Han Lin
- Jiangsu Key Laboratory of Public Project Audit, School of Engineering Audit, Nanjing Audit University, China
| | - Pingping Wu
- Jiangsu Key Laboratory of Public Project Audit, School of Engineering Audit, Nanjing Audit University, China
| | - Shidan Wang
- School of Computer Science, Nanjing Audit University, China
| | - Juan Tu
- Key Laboratory of Modern Acoustics (MOE), School of Physics, Nanjing University, China
| | - Jing Lu
- Key Laboratory of Modern Acoustics (MOE), School of Physics, Nanjing University, China
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Variation of DNA methylation on the IRX1/2 genes is responsible for the neural differentiation propensity in human induced pluripotent stem cells. Regen Ther 2022; 21:620-630. [PMID: 36514370 PMCID: PMC9719094 DOI: 10.1016/j.reth.2022.11.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2022] [Revised: 11/05/2022] [Accepted: 11/17/2022] [Indexed: 12/05/2022] Open
Abstract
Introduction Human induced pluripotent stem cells (hiPSCs) are useful tools for reproducing neural development in vitro. However, each hiPSC line has a different ability to differentiate into specific lineages, known as differentiation propensity, resulting in reduced reproducibility and increased time and funding requirements for research. To overcome this issue, we searched for predictive signatures of neural differentiation propensity of hiPSCs focusing on DNA methylation, which is the main modulator of cellular properties. Methods We obtained 32 hiPSC lines and their comprehensive DNA methylation data using the Infinium MethylationEPIC BeadChip. To assess the neural differentiation efficiency of these hiPSCs, we measured the percentage of neural stem cells on day 7 of induction. Using the DNA methylation data of undifferentiated hiPSCs and their measured differentiation efficiency into neural stem cells as the set of data, and HSIC Lasso, a machine learning-based nonlinear feature selection method, we attempted to identify neural differentiation-associated differentially methylated sites. Results Epigenome-wide unsupervised clustering cannot distinguish hiPSCs with varying differentiation efficiencies. In contrast, HSIC Lasso identified 62 CpG sites that could explain the neural differentiation efficiency of hiPSCs. Features selected by HSIC Lasso were particularly enriched within 3 Mbp of chromosome 5, harboring IRX1, IRX2, and C5orf38 genes. Within this region, DNA methylation rates were correlated with neural differentiation efficiency and were negatively correlated with gene expression of the IRX1/2 genes, particularly in female hiPSCs. In addition, forced expression of the IRX1/2 impaired the neural differentiation ability of hiPSCs in both sexes. Conclusion We for the first time showed that the DNA methylation state of the IRX1/2 genes of hiPSCs is a predictive biomarker of their potential for neural differentiation. The predictive markers for neural differentiation efficiency identified in this study may be useful for the selection of suitable undifferentiated hiPSCs prior to differentiation induction.
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Automatic Identification of a Depressive State in Primary Care. Healthcare (Basel) 2022; 10:healthcare10122347. [PMID: 36553871 PMCID: PMC9777617 DOI: 10.3390/healthcare10122347] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Revised: 11/04/2022] [Accepted: 11/19/2022] [Indexed: 11/24/2022] Open
Abstract
The Center for Epidemiologic Studies Depression Scale (CES-D) performs well in screening depression in primary care. However, people are looking for alternatives because it screens for too many items. With the popularity of social media platforms, facial movement can be recorded ecologically. Considering that there are nonverbal behaviors, including facial movement, associated with a depressive state, this study aims to establish an automatic depression recognition model to be easily used in primary healthcare. We integrated facial activities and gaze behaviors to establish a machine learning algorithm (Kernal Ridge Regression, KRR). We compared different algorithms and different features to achieve the best model. The results showed that the prediction effect of facial and gaze features was higher than that of only facial features. In all of the models we tried, the ridge model with a periodic kernel showed the best performance. The model showed a mutual fund R-squared (R2) value of 0.43 and a Pearson correlation coefficient (r) value of 0.69 (p < 0.001). Then, the most relevant variables (e.g., gaze directions and facial action units) were revealed in the present study.
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Leong SX, Leong YX, Koh CSL, Tan EX, Nguyen LBT, Chen JRT, Chong C, Pang DWC, Sim HYF, Liang X, Tan NS, Ling XY. Emerging nanosensor platforms and machine learning strategies toward rapid, point-of-need small-molecule metabolite detection and monitoring. Chem Sci 2022; 13:11009-11029. [PMID: 36320477 PMCID: PMC9516957 DOI: 10.1039/d2sc02981b] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2022] [Accepted: 09/05/2022] [Indexed: 11/25/2022] Open
Abstract
Speedy, point-of-need detection and monitoring of small-molecule metabolites are vital across diverse applications ranging from biomedicine to agri-food and environmental surveillance. Nanomaterial-based sensor (nanosensor) platforms are rapidly emerging as excellent candidates for versatile and ultrasensitive detection owing to their highly configurable optical, electrical and electrochemical properties, fast readout, as well as portability and ease of use. To translate nanosensor technologies for real-world applications, key challenges to overcome include ultralow analyte concentration down to ppb or nM levels, complex sample matrices with numerous interfering species, difficulty in differentiating isomers and structural analogues, as well as complex, multidimensional datasets of high sample variability. In this Perspective, we focus on contemporary and emerging strategies to address the aforementioned challenges and enhance nanosensor detection performance in terms of sensitivity, selectivity and multiplexing capability. We outline 3 main concepts: (1) customization of designer nanosensor platform configurations via chemical- and physical-based modification strategies, (2) development of hybrid techniques including multimodal and hyphenated techniques, and (3) synergistic use of machine learning such as clustering, classification and regression algorithms for data exploration and predictions. These concepts can be further integrated as multifaceted strategies to further boost nanosensor performances. Finally, we present a critical outlook that explores future opportunities toward the design of next-generation nanosensor platforms for rapid, point-of-need detection of various small-molecule metabolites.
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Affiliation(s)
- Shi Xuan Leong
- Division of Chemistry and Biological Chemistry, School of Chemistry, Chemical Engineering and Biotechnology, Nanyang Technological University Singapore
| | - Yong Xiang Leong
- Division of Chemistry and Biological Chemistry, School of Chemistry, Chemical Engineering and Biotechnology, Nanyang Technological University Singapore
| | - Charlynn Sher Lin Koh
- Division of Chemistry and Biological Chemistry, School of Chemistry, Chemical Engineering and Biotechnology, Nanyang Technological University Singapore
| | - Emily Xi Tan
- Division of Chemistry and Biological Chemistry, School of Chemistry, Chemical Engineering and Biotechnology, Nanyang Technological University Singapore
| | - Lam Bang Thanh Nguyen
- Division of Chemistry and Biological Chemistry, School of Chemistry, Chemical Engineering and Biotechnology, Nanyang Technological University Singapore
| | - Jaslyn Ru Ting Chen
- Division of Chemistry and Biological Chemistry, School of Chemistry, Chemical Engineering and Biotechnology, Nanyang Technological University Singapore
| | - Carice Chong
- Division of Chemistry and Biological Chemistry, School of Chemistry, Chemical Engineering and Biotechnology, Nanyang Technological University Singapore
| | - Desmond Wei Cheng Pang
- Division of Chemistry and Biological Chemistry, School of Chemistry, Chemical Engineering and Biotechnology, Nanyang Technological University Singapore
| | - Howard Yi Fan Sim
- Division of Chemistry and Biological Chemistry, School of Chemistry, Chemical Engineering and Biotechnology, Nanyang Technological University Singapore
| | - Xiaochen Liang
- Division of Chemistry and Biological Chemistry, School of Chemistry, Chemical Engineering and Biotechnology, Nanyang Technological University Singapore
| | - Nguan Soon Tan
- Lee Kong Chian School of Medicine, Nanyang Technological University Singapore
- School of Biological Sciences, Nanyang Technological University Singapore
| | - Xing Yi Ling
- Division of Chemistry and Biological Chemistry, School of Chemistry, Chemical Engineering and Biotechnology, Nanyang Technological University Singapore
- Lee Kong Chian School of Medicine, Nanyang Technological University Singapore
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Predicting Hypertension Subtypes with Machine Learning Using Targeted Metabolites and Their Ratios. Metabolites 2022; 12:metabo12080755. [PMID: 36005627 PMCID: PMC9416693 DOI: 10.3390/metabo12080755] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Revised: 08/02/2022] [Accepted: 08/04/2022] [Indexed: 11/16/2022] Open
Abstract
Hypertension is a major global health problem with high prevalence and complex associated health risks. Primary hypertension (PHT) is most common and the reasons behind primary hypertension are largely unknown. Endocrine hypertension (EHT) is another complex form of hypertension with an estimated prevalence varying from 3 to 20% depending on the population studied. It occurs due to underlying conditions associated with hormonal excess mainly related to adrenal tumours and sub-categorised: primary aldosteronism (PA), Cushing’s syndrome (CS), pheochromocytoma or functional paraganglioma (PPGL). Endocrine hypertension is often misdiagnosed as primary hypertension, causing delays in treatment for the underlying condition, reduced quality of life, and costly antihypertensive treatment that is often ineffective. This study systematically used targeted metabolomics and high-throughput machine learning methods to predict the key biomarkers in classifying and distinguishing the various subtypes of endocrine and primary hypertension. The trained models successfully classified CS from PHT and EHT from PHT with 92% specificity on the test set. The most prominent targeted metabolites and metabolite ratios for hypertension identification for different disease comparisons were C18:1, C18:2, and Orn/Arg. Sex was identified as an important feature in CS vs. PHT classification.
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Petrick LM, Shomron N. AI/ML-driven advances in untargeted metabolomics and exposomics for biomedical applications. CELL REPORTS. PHYSICAL SCIENCE 2022; 3:100978. [PMID: 35936554 PMCID: PMC9354369 DOI: 10.1016/j.xcrp.2022.100978] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Metabolomics describes a high-throughput approach for measuring a repertoire of metabolites and small molecules in biological samples. One utility of untargeted metabolomics, unbiased global analysis of the metabolome, is to detect key metabolites as contributors to, or readouts of, human health and disease. In this perspective, we discuss how artificial intelligence (AI) and machine learning (ML) have promoted major advances in untargeted metabolomics workflows and facilitated pivotal findings in the areas of disease screening and diagnosis. We contextualize applications of AI and ML to the emerging field of high-resolution mass spectrometry (HRMS) exposomics, which unbiasedly detects endogenous metabolites and exogenous chemicals in human tissue to characterize exposure linked with disease outcomes. We discuss the state of the science and suggest potential opportunities for using AI and ML to improve data quality, rigor, detection, and chemical identification in untargeted metabolomics and exposomics studies.
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Affiliation(s)
- Lauren M. Petrick
- The Bert Strassburger Metabolic Center, Sheba Medical Center, Tel-Hashomer, Israel
- Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Institute for Exposomics Research, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Noam Shomron
- Faculty of Medicine, Edmond J. Safra Center for Bioinformatics, Sagol School of Neuroscience, Center for Nanoscience and Nanotechnology, Center for Innovation Laboratories (TILabs), Tel Aviv University, Tel Aviv, Israel
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You Y, Lai X, Pan Y, Zheng H, Vera J, Liu S, Deng S, Zhang L. Artificial intelligence in cancer target identification and drug discovery. Signal Transduct Target Ther 2022; 7:156. [PMID: 35538061 PMCID: PMC9090746 DOI: 10.1038/s41392-022-00994-0] [Citation(s) in RCA: 74] [Impact Index Per Article: 37.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2021] [Revised: 03/14/2022] [Accepted: 04/05/2022] [Indexed: 02/08/2023] Open
Abstract
Artificial intelligence is an advanced method to identify novel anticancer targets and discover novel drugs from biology networks because the networks can effectively preserve and quantify the interaction between components of cell systems underlying human diseases such as cancer. Here, we review and discuss how to employ artificial intelligence approaches to identify novel anticancer targets and discover drugs. First, we describe the scope of artificial intelligence biology analysis for novel anticancer target investigations. Second, we review and discuss the basic principles and theory of commonly used network-based and machine learning-based artificial intelligence algorithms. Finally, we showcase the applications of artificial intelligence approaches in cancer target identification and drug discovery. Taken together, the artificial intelligence models have provided us with a quantitative framework to study the relationship between network characteristics and cancer, thereby leading to the identification of potential anticancer targets and the discovery of novel drug candidates.
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Affiliation(s)
- Yujie You
- College of Computer Science, Sichuan University, Chengdu, 610065, China
| | - Xin Lai
- Laboratory of Systems Tumor Immunology, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) and Universitätsklinikum Erlangen, Erlangen, 91052, Germany
| | - Yi Pan
- Faculty of Computer Science and Control Engineering, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Room D513, 1068 Xueyuan Avenue, Shenzhen University Town, Shenzhen, 518055, China
| | - Huiru Zheng
- School of Computing, Ulster University, Belfast, BT15 1ED, UK
| | - Julio Vera
- Laboratory of Systems Tumor Immunology, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) and Universitätsklinikum Erlangen, Erlangen, 91052, Germany
| | - Suran Liu
- College of Computer Science, Sichuan University, Chengdu, 610065, China
| | - Senyi Deng
- Institute of Thoracic Oncology, Department of Thoracic Surgery, West China Hospital, Sichuan University, Chengdu, 610065, China.
| | - Le Zhang
- College of Computer Science, Sichuan University, Chengdu, 610065, China.
- Key Laboratory of Systems Biology, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Hangzhou, 310024, China.
- Key Laboratory of Systems Health Science of Zhejiang Province, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou, 310024, China.
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Hwangbo S, Lee S, Lee S, Hwang H, Kim I, Park T. Kernel-based hierarchical structural component models for pathway analysis. Bioinformatics 2022; 38:3078-3086. [PMID: 35460238 DOI: 10.1093/bioinformatics/btac276] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2021] [Revised: 04/08/2022] [Indexed: 11/14/2022] Open
Abstract
MOTIVATION Pathway analyses have led to more insight into the underlying biological functions related to the phenotype of interest in various types of omics data. Pathway-based statistical approaches have been actively developed, but most of them do not consider correlations among pathways. Because it is well known that there are quite a few biomarkers that overlap between pathways, these approaches may provide misleading results. In addition, most pathway-based approaches tend to assume that biomarkers within a pathway have linear associations with the phenotype of interest, even though the relationships are more complex. RESULTS To model complex effects including nonlinear effects, we propose a new approach, Hierarchical structural CoMponent analysis using Kernel (HisCoM-Kernel). The proposed method models nonlinear associations between biomarkers and phenotype by extending the kernel machine regression and analyzes entire pathways simultaneously by using the biomarker-pathway hierarchical structure. HisCoM-Kernel is a flexible model that can be applied to various omics data. It was successfully applied to three omics datasets generated by different technologies. Our simulation studies showed that HisCoM-Kernel provided higher statistical power than other existing pathway-based methods in all datasets. The application of HisCoM-Kernel to three types of omics dataset showed its superior performance compared to existing methods in identifying more biologically meaningful pathways, including those reported in previous studies. AVAILABILITY AND IMPLEMENTATION Freely available at http://statgen.snu.ac.kr/software/HisCom-Kernel/. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Suhyun Hwangbo
- Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, 151-747, Korea.,Department of Genomic Medicine, Seoul National University Hospital, Seoul, 03080, Korea
| | - Sungyoung Lee
- Department of Genomic Medicine, Seoul National University Hospital, Seoul, 03080, Korea
| | - Seungyeoun Lee
- Department of Mathematics and Statistics, Sejong University, Sejong, 05006, Korea
| | - Heungsun Hwang
- Department of Psychology, McGill University, Montreal, QC, H3A 1B1, Canada
| | - Inyoung Kim
- Department of Statistics, Virginia Tech, Blacksburg, Virginia, 24060, U.S.A
| | - Taesung Park
- Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, 151-747, Korea.,Department of Statistics, Seoul National University, Seoul, 151-747, Korea
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12
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Ide-Okochi A, Samiso T, Kanamori Y, He M, Sakaguchi M, Fujimura K. Depression, Insomnia, and Probable Post-Traumatic Stress Disorder among Survivors of the 2016 Kumamoto Earthquake and Related Factors during the Recovery Period Amidst the COVID-19 Pandemic. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph19074403. [PMID: 35410082 PMCID: PMC8998281 DOI: 10.3390/ijerph19074403] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 02/01/2022] [Revised: 03/25/2022] [Accepted: 04/04/2022] [Indexed: 11/28/2022]
Abstract
The aftereffects of the severe 2016 Kumamoto earthquake were complicated by the COVID-19 pandemic. This study aimed to identify mental health problems and related factors among survivors five years after the earthquake and clarify its long-term effects. A cross-sectional survey was conducted in 2020 among 19,212 survivors affected by the earthquake who moved from temporary to permanent housing. We analysed 8966 respondents (5135 women, 3831 men; mean age 62.25 ± 17.29 years). Logistic regression analysis was conducted to examine associations between mental health problems and socioeconomic factors. Prevalence rates of psychological distress, insomnia, and probable post-traumatic stress disorder were 11.9%, 35.2%, and 4.1%, respectively. Female gender (OR = 1.33, 95% CI = 1.13–1.57; OR = 1.21, 95% CI = 1.08–1.34; OR = 1.81, 95% CI = 1.41–2.32), public housing (OR = 2.14, 95% CI = 1.63–2.83; OR = 1.54, 95% CI = 1.26–1.88; OR = 2.41, 95% CI = 1.62–3.58), loneliness (OR = 9.08, 95% CI = 7.71–10.70; OR = 5.55, 95% CI = 4.90–6.30; OR = 3.52, 95% CI = 2.77–4.49), COVID-19-induced activity reduction (OR = 1.41, 95% CI = 1.19–1.66; OR = 1.86, 95% CI = 1.68–2.07; OR = 1.80, 95% CI = 1.40–2.31), and COVID-19-induced income reduction (OR = 1.33, 95% CI = 1.12–1.57; OR = 1.43, 95% CI = 1.28–1.59; OR = 1.92, 95% CI = 1.51–2.43) were significantly associated with mental health problems. These results suggest that gender, current housing, loneliness, and COVID-19 affected the survivors’ mental health during recovery.
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Affiliation(s)
- Ayako Ide-Okochi
- Graduate School of Health Sciences, Kumamoto University, Kumamoto City 862-0976, Japan; (Y.K.); (M.H.); (M.S.)
- Correspondence: ; Tel.: +81-96-373-5518
| | - Tomonori Samiso
- Health and Welfare Policy Division, Health and Welfare Bureau, Kumamoto City 860-0808, Japan;
| | - Yumie Kanamori
- Graduate School of Health Sciences, Kumamoto University, Kumamoto City 862-0976, Japan; (Y.K.); (M.H.); (M.S.)
| | - Mu He
- Graduate School of Health Sciences, Kumamoto University, Kumamoto City 862-0976, Japan; (Y.K.); (M.H.); (M.S.)
| | - Mika Sakaguchi
- Graduate School of Health Sciences, Kumamoto University, Kumamoto City 862-0976, Japan; (Y.K.); (M.H.); (M.S.)
| | - Kazumi Fujimura
- Department of Community Health Systems Nursing, Ehime University Graduate School of Medicine, Toon City 791-0295, Japan;
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de Faria Cardoso C, Ohe NT, Taba VL, Paiva TT, Baltatu OC, Campos LA. Cross-Cultural Adaptation, Reliability, and Validity of a Brazilian of Short Version of the Posttraumatic Diagnostic Scale. Front Psychol 2021; 12:614554. [PMID: 33967886 PMCID: PMC8102692 DOI: 10.3389/fpsyg.2021.614554] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2020] [Accepted: 03/23/2021] [Indexed: 12/03/2022] Open
Abstract
Background: A short version of the Posttraumatic Diagnostic Scale (PDS) comprising only re-experiencing symptom items has been recently validated on Japanese adults. This short-version-PDS had good psychometric properties among Japanese adults with and without posttraumatic stress disorder (PTSD). The aim of this study was to translate and culturally validate the short-version-PDS for the Brazilian sociolinguistic context. Methods: A translation of the short-version-PDS was performed based on established guidelines. We enrolled 53 patients with PTSD as a potential comorbidity. The translation and cross-cultural adaptation of the short-version-PDS included forward and back-translation by a Japanese Brazilian researcher and a certified translator; synthesis was achieved by consensus, backward translation, pilot test, and finalization. Content validity coefficient (CVC) was used to assess quality of adaptation. Internal consistency was calculated using Cronbach's alpha coefficient. Spearman correlations were between the new short-version-PDS and the Brazilian version of the posttraumatic Stress Disorder Checklist for DSM-5 (PCL-5), and a receiver operating characteristic (ROC) curve was used to determine the best cut-off values for the short-version-PDS. Results: The short-version-PDS was well accepted by all subjects, none of the questions were experienced as inappropriate, and all questions of the 3 items were judged important. Item 1 presented CVCt = 0.92; item 2 had a CVCt = 0.87 and item 3 had a CVCt = 0.95. The internal consistency of the final version as measured by Cronbach's alpha was 0.78. The short-version-PDS scale correlated positively with the DSM-5 scale with a Spearman rho of 0.64 (95%CI [0.4-0.8], p < 0.001). The receiver operating characteristic (ROC) curve value was 0.97 (95%CI [0.9-1.0], p < 0.001). The cut-off score for a maximum Youden Index of 0.8 to distinguish moderate from severe from slight PTSD was > 31.0 with sensitivity and specificity are 86.4 and 93.5%, respectively. Conclusions: This Brazilian Portuguese version of the short-version-PDS had good psychometric properties among Brazilian adults with and without PTSD. Transferability and generalizability of the cut-off scores should be further analyzed.
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Affiliation(s)
- Cláudia de Faria Cardoso
- Institute of Biomedical Engineering at Anhembi Morumbi University, Sao Jose dos Campos, Brazil.,Center of Innovation, Technology and Education (CITE) at Sao Jose dos Campos Technology Park, Sao Jose dos Campos, Brazil.,Hospital São Francisco de Assis, Jacareí, Brazil
| | - Natalia Tiemi Ohe
- Institute of Biomedical Engineering at Anhembi Morumbi University, Sao Jose dos Campos, Brazil
| | - Vera Lúcia Taba
- Institute of Biomedical Engineering at Anhembi Morumbi University, Sao Jose dos Campos, Brazil.,Center of Innovation, Technology and Education (CITE) at Sao Jose dos Campos Technology Park, Sao Jose dos Campos, Brazil
| | | | - Ovidiu Constantin Baltatu
- Institute of Biomedical Engineering at Anhembi Morumbi University, Sao Jose dos Campos, Brazil.,Center of Innovation, Technology and Education (CITE) at Sao Jose dos Campos Technology Park, Sao Jose dos Campos, Brazil.,College of Medicine and Health Sciences, Khalifa University, Abu Dhabi, United Arab Emirates
| | - Luciana Aparecida Campos
- Institute of Biomedical Engineering at Anhembi Morumbi University, Sao Jose dos Campos, Brazil.,Center of Innovation, Technology and Education (CITE) at Sao Jose dos Campos Technology Park, Sao Jose dos Campos, Brazil.,College of Health Sciences, Abu Dhabi University, Abu Dhabi, United Arab Emirates
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Saigusa D, Matsukawa N, Hishinuma E, Koshiba S. Identification of biomarkers to diagnose diseases and find adverse drug reactions by metabolomics. Drug Metab Pharmacokinet 2020; 37:100373. [PMID: 33631535 DOI: 10.1016/j.dmpk.2020.11.008] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2020] [Revised: 11/24/2020] [Accepted: 11/25/2020] [Indexed: 12/12/2022]
Abstract
Metabolomics has been widely used for investigating the biological functions of disease expression and has the potential to discover biomarkers in circulating biofluids or tissue extracts that reflect in phenotypic changes. Metabolic profiling has advantages because of the use of unbiased techniques, including multivariate analysis, and has been applied in pharmacological studies to predict therapeutic and adverse reactions of drugs, which is called pharmacometabolomics (PMx). Nuclear magnetic resonance (NMR)- and mass spectrometry (MS)-based metabolomics has contributed to the discovery of recent disease biomarkers; however, the optimal strategy for the study purpose must be selected from many established protocols, methodologies and analytical platforms. Additionally, information on molecular localization in tissue is essential for further functional analyses related to therapeutic and adverse effects of drugs in the process of drug development. MS imaging (MSI) is a promising technology that can visualize molecules on tissue surfaces without labeling and thus provide localized information. This review summarizes recent uses of MS-based global and wide-targeted metabolomics technologies and the advantages of the MSI approach for PMx and highlights the PMx technique for the biomarker discovery of adverse drug effects.
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Affiliation(s)
- Daisuke Saigusa
- Department of Integrative Genomics, Tohoku University Tohoku Medical Megabank Organization, 2-1 Seiryo-machi, Aoba-ku, Sendai, 980-8573, Japan; Medical Biochemistry, Tohoku University Graduate School of Medicine, 2-1 Seiryo-machi, Aoba-ku, Sendai, 980-8575, Japan.
| | - Naomi Matsukawa
- Department of Integrative Genomics, Tohoku University Tohoku Medical Megabank Organization, 2-1 Seiryo-machi, Aoba-ku, Sendai, 980-8573, Japan; Medical Biochemistry, Tohoku University Graduate School of Medicine, 2-1 Seiryo-machi, Aoba-ku, Sendai, 980-8575, Japan.
| | - Eiji Hishinuma
- Department of Integrative Genomics, Tohoku University Tohoku Medical Megabank Organization, 2-1 Seiryo-machi, Aoba-ku, Sendai, 980-8573, Japan; Advanced Research Center for Innovations in Next-Generation Medicine, Tohoku University, 2-1 Seiryo-machi, Aoba-ku, Sendai, 980-8573, Japan.
| | - Seizo Koshiba
- Department of Integrative Genomics, Tohoku University Tohoku Medical Megabank Organization, 2-1 Seiryo-machi, Aoba-ku, Sendai, 980-8573, Japan; Medical Biochemistry, Tohoku University Graduate School of Medicine, 2-1 Seiryo-machi, Aoba-ku, Sendai, 980-8575, Japan; Advanced Research Center for Innovations in Next-Generation Medicine, Tohoku University, 2-1 Seiryo-machi, Aoba-ku, Sendai, 980-8573, Japan.
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15
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Edison AS, Colonna M, Gouveia GJ, Holderman NR, Judge MT, Shen X, Zhang S. NMR: Unique Strengths That Enhance Modern Metabolomics Research. Anal Chem 2020; 93:478-499. [DOI: 10.1021/acs.analchem.0c04414] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
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16
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Narita A, Ueki M, Tamiya G. Artificial intelligence powered statistical genetics in biobanks. J Hum Genet 2020; 66:61-65. [PMID: 32782383 DOI: 10.1038/s10038-020-0822-y] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2020] [Revised: 07/15/2020] [Accepted: 07/26/2020] [Indexed: 12/19/2022]
Abstract
Large-scale, sometimes nationwide, prospective genomic cohorts biobanking rich biological specimens such as blood, urine and tissues, have been established and released their vast amount of data in several countries. These genetic and epidemiological resources are expected to allow investigators to disentangle genetic and environmental components conferring common complex diseases. There are, however, two major challenges to statistical genetics for this goal: small sample size-high dimensionality and multilayered-heterogenous endophenotypes. Rather counterintuitively, biobank data generally have small sample size relative to their data dimensionality consisting of genomic variation, lifestyle questionnaire, and sometimes their interaction. This is a widely acknowledged difficulty in data analysis, so-called "p»n problem" in statistics or "curse of dimensionality" in machine-learning field. On the other hand, we have too many measurements of individual health status, which are endophenotypes, such as health check-up data, images, psychological test scores in addition to metabolomics and proteomics data. These endophenotypes are rich but not so tractable because of their worsen dimensionality, and substantial correlation, sometimes confusing causation among them. We have tried to overcome the problems inherent to biobank data, using statistical machine-learning and deep-learning technologies.
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Affiliation(s)
- Akira Narita
- Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan
| | - Masao Ueki
- RIKEN Center for Advanced Intelligence Project, Tokyo, Japan
| | - Gen Tamiya
- Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan. .,RIKEN Center for Advanced Intelligence Project, Tokyo, Japan.
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