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  1. Book ; Online ; E-Book: Translational biomedical informatics

    Shen, Bairong / Tang, Haixu / Jiang, Xiaoqian

    a precision medicine perspective

    (Advances in experimental medicine and biology ; 939)

    2016  

    Author's details Bairong Shen, Haixu Tang, Xiaoqian Jiang editors
    Series title Advances in experimental medicine and biology ; 939
    Collection
    Keywords Life sciences ; Molecular biology ; Bioinformatics
    Subject code 570.285
    Language English
    Size 1 Online-Ressource (vi, 332 Seiten), Illustrationen, Diagramme
    Publisher Springer
    Publishing place Singapore
    Publishing country Singapore
    Document type Book ; Online ; E-Book
    Remark Zugriff für angemeldete ZB MED-Nutzerinnen und -Nutzer
    HBZ-ID HT019555012
    ISBN 978-981-10-1503-8 ; 9789811015021 ; 981-10-1503-1 ; 9811015023
    DOI 10.1007/978-981-10-1503-8
    Database ZB MED Catalogue: Medicine, Health, Nutrition, Environment, Agriculture

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  2. Article ; Online: Federated generalized linear mixed models for collaborative genome-wide association studies

    Wentao Li / Han Chen / Xiaoqian Jiang / Arif Harmanci

    iScience, Vol 26, Iss 8, Pp 107227- (2023)

    2023  

    Abstract: Summary: Federated association testing is a powerful approach to conduct large-scale association studies where sites share intermediate statistics through a central server. There are, however, several standing challenges. Confounding factors like ... ...

    Abstract Summary: Federated association testing is a powerful approach to conduct large-scale association studies where sites share intermediate statistics through a central server. There are, however, several standing challenges. Confounding factors like population stratification should be carefully modeled across sites. In addition, it is crucial to consider disease etiology using flexible models to prevent biases. Privacy protections for participants pose another significant challenge. Here, we propose distributed Mixed Effects Genome-wide Association study (dMEGA), a method that enables federated generalized linear mixed model-based association testing across multiple sites without explicitly sharing genotype and phenotype data. dMEGA employs a reference projection to correct for population-stratification and utilizes efficient local-gradient updates among sites, incorporating both fixed and random effects. The accuracy and efficiency of dMEGA are demonstrated through simulated and real datasets. dMEGA is publicly available at https://github.com/Li-Wentao/dMEGA.
    Keywords Health sciences ; Clinical genetics ; Human genetics ; Genomics ; Science ; Q
    Subject code 310
    Language English
    Publishing date 2023-08-01T00:00:00Z
    Publisher Elsevier
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  3. Article ; Online: Privacy-preserving logistic regression with secret sharing

    Ali Reza Ghavamipour / Fatih Turkmen / Xiaoqian Jiang

    BMC Medical Informatics and Decision Making, Vol 22, Iss 1, Pp 1-

    2022  Volume 11

    Abstract: Abstract Background Logistic regression (LR) is a widely used classification method for modeling binary outcomes in many medical data classification tasks. Researchers that collect and combine datasets from various data custodians and jurisdictions can ... ...

    Abstract Abstract Background Logistic regression (LR) is a widely used classification method for modeling binary outcomes in many medical data classification tasks. Researchers that collect and combine datasets from various data custodians and jurisdictions can greatly benefit from the increased statistical power to support their analysis goals. However, combining data from different sources creates serious privacy concerns that need to be addressed. Methods In this paper, we propose two privacy-preserving protocols for performing logistic regression with the Newton–Raphson method in the estimation of parameters. Our proposals are based on secure Multi-Party Computation (MPC) and tailored to the honest majority and dishonest majority security settings. Results The proposed protocols are evaluated against both synthetic and real-world datasets in terms of efficiency and accuracy, and a comparison is made with the ordinary logistic regression. The experimental results demonstrate that the proposed protocols are highly efficient and accurate. Conclusions Our work introduces two iterative algorithms to enable the distributed training of a logistic regression model in a privacy-preserving manner. The implementation results show that our algorithms can handle large datasets from multiple sources.
    Keywords Logistic regression ; Secret sharing ; Multi-party computation ; Privacy-preserving ; Newton–Raphson ; Computer applications to medicine. Medical informatics ; R858-859.7
    Subject code 005
    Language English
    Publishing date 2022-04-01T00:00:00Z
    Publisher BMC
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  4. Article ; Online: Evaluation of vicinity-based hidden Markov models for genotype imputation

    Su Wang / Miran Kim / Xiaoqian Jiang / Arif Ozgun Harmanci

    BMC Bioinformatics, Vol 23, Iss 1, Pp 1-

    2022  Volume 26

    Abstract: Abstract Background The decreasing cost of DNA sequencing has led to a great increase in our knowledge about genetic variation. While population-scale projects bring important insight into genotype–phenotype relationships, the cost of performing whole- ... ...

    Abstract Abstract Background The decreasing cost of DNA sequencing has led to a great increase in our knowledge about genetic variation. While population-scale projects bring important insight into genotype–phenotype relationships, the cost of performing whole-genome sequencing on large samples is still prohibitive. In-silico genotype imputation coupled with genotyping-by-arrays is a cost-effective and accurate alternative for genotyping of common and uncommon variants. Imputation methods compare the genotypes of the typed variants with the large population-specific reference panels and estimate the genotypes of untyped variants by making use of the linkage disequilibrium patterns. Most accurate imputation methods are based on the Li–Stephens hidden Markov model, HMM, that treats the sequence of each chromosome as a mosaic of the haplotypes from the reference panel. Results Here we assess the accuracy of vicinity-based HMMs, where each untyped variant is imputed using the typed variants in a small window around itself (as small as 1 centimorgan). Locality-based imputation is used recently by machine learning-based genotype imputation approaches. We assess how the parameters of the vicinity-based HMMs impact the imputation accuracy in a comprehensive set of benchmarks and show that vicinity-based HMMs can accurately impute common and uncommon variants. Conclusions Our results indicate that locality-based imputation models can be effectively used for genotype imputation. The parameter settings that we identified can be used in future methods and vicinity-based HMMs can be used for re-structuring and parallelizing new imputation methods. The source code for the vicinity-based HMM implementations is publicly available at https://github.com/harmancilab/LoHaMMer .
    Keywords Genotype imputation ; Hidden Markov models ; Forward–Backward algorithm ; Viterbi algorithm ; Computer applications to medicine. Medical informatics ; R858-859.7 ; Biology (General) ; QH301-705.5
    Subject code 006
    Language English
    Publishing date 2022-08-01T00:00:00Z
    Publisher BMC
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  5. Article ; Online: Predicting multiple sclerosis severity with multimodal deep neural networks

    Kai Zhang / John A. Lincoln / Xiaoqian Jiang / Elmer V. Bernstam / Shayan Shams

    BMC Medical Informatics and Decision Making, Vol 23, Iss 1, Pp 1-

    2023  Volume 17

    Abstract: Abstract Multiple Sclerosis (MS) is a chronic disease developed in the human brain and spinal cord, which can cause permanent damage or deterioration of the nerves. The severity of MS disease is monitored by the Expanded Disability Status Scale, composed ...

    Abstract Abstract Multiple Sclerosis (MS) is a chronic disease developed in the human brain and spinal cord, which can cause permanent damage or deterioration of the nerves. The severity of MS disease is monitored by the Expanded Disability Status Scale, composed of several functional sub-scores. Early and accurate classification of MS disease severity is critical for slowing down or preventing disease progression via applying early therapeutic intervention strategies. Recent advances in deep learning and the wide use of Electronic Health Records (EHR) create opportunities to apply data-driven and predictive modeling tools for this goal. Previous studies focusing on using single-modal machine learning and deep learning algorithms were limited in terms of prediction accuracy due to data insufficiency or model simplicity. In this paper, we proposed the idea of using patients’ multimodal longitudinal and longitudinal EHR data to predict multiple sclerosis disease severity in the future. Our contribution has two main facets. First, we describe a pioneering effort to integrate structured EHR data, neuroimaging data and clinical notes to build a multi-modal deep learning framework to predict patient’s MS severity. The proposed pipeline demonstrates up to 19% increase in terms of the area under the Area Under the Receiver Operating Characteristic curve (AUROC) compared to models using single-modal data. Second, the study also provides valuable insights regarding the amount useful signal embedded in each data modality with respect to MS disease prediction, which may improve data collection processes.
    Keywords Multimodal deep learning ; Multiple sclerosis ; Expanded disability status scale ; Computer applications to medicine. Medical informatics ; R858-859.7
    Subject code 006
    Language English
    Publishing date 2023-11-01T00:00:00Z
    Publisher BMC
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  6. Article ; Online: Multiple imputation for analysis of incomplete data in distributed health data networks

    Changgee Chang / Yi Deng / Xiaoqian Jiang / Qi Long

    Nature Communications, Vol 11, Iss 1, Pp 1-

    2020  Volume 11

    Abstract: Distributed health data networks (DHDNs) leverage data from multiple healthcare systems, but often face major analytical challenges in the presence of missing data. This paper develops distributed multiple imputation methods that do not require sharing ... ...

    Abstract Distributed health data networks (DHDNs) leverage data from multiple healthcare systems, but often face major analytical challenges in the presence of missing data. This paper develops distributed multiple imputation methods that do not require sharing subject-level data across health systems.
    Keywords Science ; Q
    Language English
    Publishing date 2020-10-01T00:00:00Z
    Publisher Nature Publishing Group
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  7. Article ; Online: Multiple imputation for analysis of incomplete data in distributed health data networks

    Changgee Chang / Yi Deng / Xiaoqian Jiang / Qi Long

    Nature Communications, Vol 11, Iss 1, Pp 1-

    2020  Volume 11

    Abstract: Distributed health data networks (DHDNs) leverage data from multiple healthcare systems, but often face major analytical challenges in the presence of missing data. This paper develops distributed multiple imputation methods that do not require sharing ... ...

    Abstract Distributed health data networks (DHDNs) leverage data from multiple healthcare systems, but often face major analytical challenges in the presence of missing data. This paper develops distributed multiple imputation methods that do not require sharing subject-level data across health systems.
    Keywords Science ; Q
    Language English
    Publishing date 2020-10-01T00:00:00Z
    Publisher Nature Portfolio
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  8. Article ; Online: Computational medication regimen for Parkinson’s disease using reinforcement learning

    Yejin Kim / Jessika Suescun / Mya C. Schiess / Xiaoqian Jiang

    Scientific Reports, Vol 11, Iss 1, Pp 1-

    2021  Volume 9

    Abstract: Abstract Our objective is to derive a sequential decision-making rule on the combination of medications to minimize motor symptoms using reinforcement learning (RL). Using an observational longitudinal cohort of Parkinson’s disease patients, the ... ...

    Abstract Abstract Our objective is to derive a sequential decision-making rule on the combination of medications to minimize motor symptoms using reinforcement learning (RL). Using an observational longitudinal cohort of Parkinson’s disease patients, the Parkinson’s Progression Markers Initiative database, we derived clinically relevant disease states and an optimal combination of medications for each of them by using policy iteration of the Markov decision process (MDP). We focused on 8 combinations of medications, i.e., Levodopa, a dopamine agonist, and other PD medications, as possible actions and motor symptom severity, based on the Unified Parkinson Disease Rating Scale (UPDRS) section III, as reward/penalty of decision. We analyzed a total of 5077 visits from 431 PD patients with 55.5 months follow-up. We excluded patients without UPDRS III scores or medication records. We derived a medication regimen that is comparable to a clinician’s decision. The RL model achieved a lower level of motor symptom severity scores than what clinicians did, whereas the clinicians’ medication rules were more consistent than the RL model. The RL model followed the clinician’s medication rules in most cases but also suggested some changes, which leads to the difference in lowering symptoms severity. This is the first study to investigate RL to improve the pharmacological approach of PD patients. Our results contribute to the development of an interactive machine-physician ecosystem that relies on evidence-based medicine and can potentially enhance PD management.
    Keywords Medicine ; R ; Science ; Q
    Subject code 006
    Language English
    Publishing date 2021-04-01T00:00:00Z
    Publisher Nature Portfolio
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  9. Article ; Online: Counterfactual analysis of differential comorbidity risk factors in Alzheimer's disease and related dementias.

    Yejin Kim / Kai Zhang / Sean I Savitz / Luyao Chen / Paul E Schulz / Xiaoqian Jiang

    PLOS Digital Health, Vol 1, Iss 3, p e

    2022  Volume 0000018

    Abstract: Alzheimer's disease and related dementias (ADRD) is a multifactorial disease that involves several different etiologic mechanisms with various comorbidities. There is also significant heterogeneity in the prevalence of ADRD across diverse demographics ... ...

    Abstract Alzheimer's disease and related dementias (ADRD) is a multifactorial disease that involves several different etiologic mechanisms with various comorbidities. There is also significant heterogeneity in the prevalence of ADRD across diverse demographics groups. Association studies on such heterogeneous comorbidity risk factors are limited in their ability to determine causation. We aim to compare counterfactual treatment effects of various comorbidity in ADRD in different racial groups (African Americans and Caucasians). We used 138,026 ADRD and 1:1 matched older adults without ADRD from nationwide electronic health records, which extensively cover a large population's long medical history in breadth. We matched African Americans and Caucasians based on age, sex, and high-risk comorbidities (hypertension, diabetes, obesity, vascular disease, heart disease, and head injury) to build two comparable cohorts. We derived a Bayesian network of 100 comorbidities and selected comorbidities with potential causal effect to ADRD. We estimated the average treatment effect (ATE) of the selected comorbidities on ADRD using inverse probability of treatment weighting. Late effects of cerebrovascular disease significantly predisposed older African Americans (ATE = 0.2715) to ADRD, but not in the Caucasian counterparts; depression significantly predisposed older Caucasian counterparts (ATE = 0.1560) to ADRD, but not in the African Americans. Our extensive counterfactual analysis using a nationwide EHR discovered different comorbidities that predispose older African Americans to ADRD compared to Caucasian counterparts. Despite the noisy and incomplete nature of the real-world data, the counterfactual analysis on the comorbidity risk factors can be a valuable tool to support the risk factor exposure studies.
    Keywords Computer applications to medicine. Medical informatics ; R858-859.7
    Subject code 610
    Language English
    Publishing date 2022-03-01T00:00:00Z
    Publisher Public Library of Science (PLoS)
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  10. Article ; Online: A multitask deep learning approach for pulmonary embolism detection and identification

    Xiaotian Ma / Emma C. Ferguson / Xiaoqian Jiang / Sean I. Savitz / Shayan Shams

    Scientific Reports, Vol 12, Iss 1, Pp 1-

    2022  Volume 11

    Abstract: Abstract Pulmonary embolism (PE) is a blood clot traveling to the lungs and is associated with substantial morbidity and mortality. Therefore, rapid diagnoses and treatments are essential. Chest computed tomographic pulmonary angiogram (CTPA) is the gold ...

    Abstract Abstract Pulmonary embolism (PE) is a blood clot traveling to the lungs and is associated with substantial morbidity and mortality. Therefore, rapid diagnoses and treatments are essential. Chest computed tomographic pulmonary angiogram (CTPA) is the gold standard for PE diagnoses. Deep learning can enhance the radiologists’workflow by identifying PE using CTPA, which helps to prioritize important cases and hasten the diagnoses for at-risk patients. In this study, we propose a two-phase multitask learning method that can recognize the presence of PE and its properties such as the position, whether acute or chronic, and the corresponding right-to-left ventricle diameter (RV/LV) ratio, thereby reducing false-negative diagnoses. Trained on the RSNA-STR Pulmonary Embolism CT Dataset, our model demonstrates promising PE detection performances on the hold-out test set with the window-level AUROC achieving 0.93 and the sensitivity being 0.86 with a specificity of 0.85, which is competitive with the radiologists’sensitivities ranging from 0.67 to 0.87 with specificities of 0.89–0.99. In addition, our model provides interpretability through attention weight heatmaps and gradient-weighted class activation mapping (Grad-CAM). Our proposed deep learning model could predict PE existence and other properties of existing cases, which could be applied to practical assistance for PE diagnosis.
    Keywords Medicine ; R ; Science ; Q
    Subject code 610
    Language English
    Publishing date 2022-07-01T00:00:00Z
    Publisher Nature Portfolio
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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