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  1. Article: Ag

    Gao, Yubing / Zhou, Weirong / Wang, Yong / Gao, Yuan / Han, Jiayin / Kong, Dehao / Lu, Geyu

    Nanomaterials (Basel, Switzerland)

    2024  Volume 14, Issue 5

    Abstract: N-butanol ( ... ...

    Abstract N-butanol (C
    Language English
    Publishing date 2024-02-21
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2662255-5
    ISSN 2079-4991
    ISSN 2079-4991
    DOI 10.3390/nano14050394
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  2. Article ; Online: Using clinical and genetic risk factors for risk prediction of 8 cancers in the UK Biobank.

    Hu, Jiaqi / Ye, Yixuan / Zhou, Geyu / Zhao, Hongyu

    JNCI cancer spectrum

    2024  Volume 8, Issue 2

    Abstract: Background: Models with polygenic risk scores and clinical factors to predict risk of different cancers have been developed, but these models have been limited by the polygenic risk score-derivation methods and the incomplete selection of clinical ... ...

    Abstract Background: Models with polygenic risk scores and clinical factors to predict risk of different cancers have been developed, but these models have been limited by the polygenic risk score-derivation methods and the incomplete selection of clinical variables.
    Methods: We used UK Biobank to train the best polygenic risk scores for 8 cancers (bladder, breast, colorectal, kidney, lung, ovarian, pancreatic, and prostate cancers) and select relevant clinical variables from 733 baseline traits through extreme gradient boosting (XGBoost). Combining polygenic risk scores and clinical variables, we developed Cox proportional hazards models for risk prediction in these cancers.
    Results: Our models achieved high prediction accuracy for 8 cancers, with areas under the curve ranging from 0.618 (95% confidence interval = 0.581 to 0.655) for ovarian cancer to 0.831 (95% confidence interval = 0.817 to 0.845) for lung cancer. Additionally, our models could identify individuals at a high risk for developing cancer. For example, the risk of breast cancer for individuals in the top 5% score quantile was nearly 13 times greater than for individuals in the lowest 10%. Furthermore, we observed a higher proportion of individuals with high polygenic risk scores in the early-onset group but a higher proportion of individuals at high clinical risk in the late-onset group.
    Conclusion: Our models demonstrated the potential to predict cancer risk and identify high-risk individuals with great generalizability to different cancers. Our findings suggested that the polygenic risk score model is more predictive for the cancer risk of early-onset patients than for late-onset patients, while the clinical risk model is more predictive for late-onset patients. Meanwhile, combining polygenic risk scores and clinical risk factors has overall better predictive performance than using polygenic risk scores or clinical risk factors alone.
    MeSH term(s) Male ; Humans ; UK Biobank ; Biological Specimen Banks ; Risk Factors ; Prostatic Neoplasms/diagnosis ; Prostatic Neoplasms/epidemiology ; Prostatic Neoplasms/genetics ; Breast Neoplasms/diagnosis ; Breast Neoplasms/epidemiology ; Breast Neoplasms/genetics
    Language English
    Publishing date 2024-02-16
    Publishing country England
    Document type Journal Article
    ISSN 2515-5091
    ISSN (online) 2515-5091
    DOI 10.1093/jncics/pkae008
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  3. Article: A Bayesian Approach to Correcting the Attenuation Bias of Regression Using Polygenic Risk Score.

    Zhou, Geyu / Qie, Xinyue / Zhao, Hongyu

    bioRxiv : the preprint server for biology

    2023  

    Abstract: Polygenic risk score (PRS) has become increasingly popular for predicting the value of complex traits. In many settings, PRS is used as a covariate in regression analysis to study the association between different phenotypes. However, measurement error ... ...

    Abstract Polygenic risk score (PRS) has become increasingly popular for predicting the value of complex traits. In many settings, PRS is used as a covariate in regression analysis to study the association between different phenotypes. However, measurement error in PRS causes attenuation bias in the estimation of regression coefficients. In this paper, we employ a Bayesian approach to accounting for the measurement error of PRS and correcting the attenuation bias in linear and logistic regression. Through simulation, we show that our approach is able to obtain approximately unbiased estimation of coefficients and credible intervals with correct coverage probability. We also empirically compare our Bayesian measurement error model to the conventional regression model by analyzing real traits in the UK Biobank. The results demonstrate the effectiveness of our approach as it significantly reduces the error in coefficient estimates.
    Language English
    Publishing date 2023-11-28
    Publishing country United States
    Document type Preprint
    DOI 10.1101/2023.11.27.568907
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  4. Article ; Online: Ppb-level unsymmetrical dimethylhydrazine detection based on In

    Bu, Weiyi / Zhou, You / Huang, Dan / Liu, Na / Zhang, Yan / Han, Wenjiang / Chuai, Xiaohong / Zhou, Zhijie / Hu, Changhua / Lu, Geyu

    Journal of hazardous materials

    2024  Volume 472, Page(s) 134508

    Abstract: As one of main high-energy fuels for rocket launching, unsymmetrical dimethylhydrazine (UDMH) and its decomposition products do harm to environment and human health. It is significant to develop a device to monitor its leakage. In this work, a UDMH gas ... ...

    Abstract As one of main high-energy fuels for rocket launching, unsymmetrical dimethylhydrazine (UDMH) and its decomposition products do harm to environment and human health. It is significant to develop a device to monitor its leakage. In this work, a UDMH gas sensor based on In
    Language English
    Publishing date 2024-05-04
    Publishing country Netherlands
    Document type Journal Article
    ZDB-ID 1491302-1
    ISSN 1873-3336 ; 0304-3894
    ISSN (online) 1873-3336
    ISSN 0304-3894
    DOI 10.1016/j.jhazmat.2024.134508
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  5. Article ; Online: Statistical methods for assessing the effects of de novo variants on birth defects.

    Xie, Yuhan / Wu, Ruoxuan / Li, Hongyu / Dong, Weilai / Zhou, Geyu / Zhao, Hongyu

    Human genomics

    2024  Volume 18, Issue 1, Page(s) 25

    Abstract: With the development of next-generation sequencing technology, de novo variants (DNVs) with deleterious effects can be identified and investigated for their effects on birth defects such as congenital heart disease (CHD). However, statistical power is ... ...

    Abstract With the development of next-generation sequencing technology, de novo variants (DNVs) with deleterious effects can be identified and investigated for their effects on birth defects such as congenital heart disease (CHD). However, statistical power is still limited for such studies because of the small sample size due to the high cost of recruiting and sequencing samples and the low occurrence of DNVs. DNV analysis is further complicated by genetic heterogeneity across diseased individuals. Therefore, it is critical to jointly analyze DNVs with other types of genomic/biological information to improve statistical power to identify genes associated with birth defects. In this review, we discuss the general workflow, recent developments in statistical methods, and future directions for DNV analysis.
    MeSH term(s) Humans ; Genetic Heterogeneity ; Genomics ; High-Throughput Nucleotide Sequencing ; Sample Size ; Workflow
    Language English
    Publishing date 2024-03-14
    Publishing country England
    Document type Journal Article ; Review
    ZDB-ID 2147618-4
    ISSN 1479-7364 ; 1479-7364
    ISSN (online) 1479-7364
    ISSN 1479-7364
    DOI 10.1186/s40246-024-00590-z
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  6. Article: Leveraging genetic correlations and multiple populations to improve genetic risk prediction for non-European populations.

    Xu, Leqi / Zhou, Geyu / Jiang, Wei / Guan, Leying / Zhao, Hongyu

    Research square

    2023  

    Abstract: The disparity in genetic risk prediction accuracy between European and non-European individuals highlights a critical challenge in health inequality. To bridge this gap, we introduce JointPRS, a novel method that models multiple populations jointly to ... ...

    Abstract The disparity in genetic risk prediction accuracy between European and non-European individuals highlights a critical challenge in health inequality. To bridge this gap, we introduce JointPRS, a novel method that models multiple populations jointly to improve genetic risk predictions for non-European individuals. JointPRS has three key features. First, it encompasses all diverse populations to improve prediction accuracy, rather than relying solely on the target population with a singular auxiliary European group. Second, it autonomously estimates and leverages chromosome-wise cross-population genetic correlations to infer the effect sizes of genetic variants. Lastly, it provides an auto version that has comparable performance to the tuning version to accommodate the situation with no validation dataset. Through extensive simulations and real data applications to 22 quantitative traits and four binary traits in East Asian populations, nine quantitative traits and one binary trait in African populations, and four quantitative traits in South Asian populations, we demonstrate that JointPRS outperforms state-of-art methods, improving the prediction accuracy for both quantitative and binary traits in non-European populations.
    Language English
    Publishing date 2023-12-25
    Publishing country United States
    Document type Preprint
    DOI 10.21203/rs.3.rs-3741763/v1
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  7. Article: Leveraging genetic correlations and multiple populations to improve genetic risk prediction for non-European populations.

    Xu, Leqi / Zhou, Geyu / Jiang, Wei / Guan, Leying / Zhao, Hongyu

    bioRxiv : the preprint server for biology

    2023  

    Abstract: The disparity in genetic risk prediction accuracy between European and non-European individuals highlights a critical challenge in health inequality. To bridge this gap, we introduce JointPRS, a novel method that models multiple populations jointly to ... ...

    Abstract The disparity in genetic risk prediction accuracy between European and non-European individuals highlights a critical challenge in health inequality. To bridge this gap, we introduce JointPRS, a novel method that models multiple populations jointly to improve genetic risk predictions for non-European individuals. JointPRS has three key features. First, it encompasses all diverse populations to improve prediction accuracy, rather than relying solely on the target population with a singular auxiliary European group. Second, it autonomously estimates and leverages chromosome-wise cross-population genetic correlations to infer the effect sizes of genetic variants. Lastly, it provides an auto version that has comparable performance to the tuning version to accommodate the situation with no validation dataset. Through extensive simulations and real data applications to 22 quantitative traits and four binary traits in East Asian populations, nine quantitative traits and one binary trait in African populations, and four quantitative traits in South Asian populations, we demonstrate that JointPRS outperforms state-of-art methods, improving the prediction accuracy for both quantitative and binary traits in non-European populations.
    Language English
    Publishing date 2023-12-12
    Publishing country United States
    Document type Preprint
    DOI 10.1101/2023.10.29.564615
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  8. Article ; Online: A fast and robust Bayesian nonparametric method for prediction of complex traits using summary statistics.

    Zhou, Geyu / Zhao, Hongyu

    PLoS genetics

    2021  Volume 17, Issue 7, Page(s) e1009697

    Abstract: Genetic prediction of complex traits has great promise for disease prevention, monitoring, and treatment. The development of accurate risk prediction models is hindered by the wide diversity of genetic architecture across different traits, limited access ...

    Abstract Genetic prediction of complex traits has great promise for disease prevention, monitoring, and treatment. The development of accurate risk prediction models is hindered by the wide diversity of genetic architecture across different traits, limited access to individual level data for training and parameter tuning, and the demand for computational resources. To overcome the limitations of the most existing methods that make explicit assumptions on the underlying genetic architecture and need a separate validation data set for parameter tuning, we develop a summary statistics-based nonparametric method that does not rely on validation datasets to tune parameters. In our implementation, we refine the commonly used likelihood assumption to deal with the discrepancy between summary statistics and external reference panel. We also leverage the block structure of the reference linkage disequilibrium matrix for implementation of a parallel algorithm. Through simulations and applications to twelve traits, we show that our method is adaptive to different genetic architectures, statistically robust, and computationally efficient. Our method is available at https://github.com/eldronzhou/SDPR.
    MeSH term(s) Algorithms ; Bayes Theorem ; Forecasting/methods ; Genetic Predisposition to Disease/genetics ; Genetic Testing/methods ; Genome-Wide Association Study/methods ; Genotype ; Humans ; Linkage Disequilibrium/genetics ; Multifactorial Inheritance/genetics ; Multifactorial Inheritance/physiology ; Phenotype ; Polymorphism, Single Nucleotide/genetics ; Risk Factors ; Statistics, Nonparametric
    Language English
    Publishing date 2021-07-26
    Publishing country United States
    Document type Journal Article ; Research Support, N.I.H., Extramural
    ZDB-ID 2186725-2
    ISSN 1553-7404 ; 1553-7390
    ISSN (online) 1553-7404
    ISSN 1553-7390
    DOI 10.1371/journal.pgen.1009697
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  9. Article ; Online: Imparting Chemiresistor with Humidity-Independent Sensitivity toward Trace-Level Formaldehyde via Substitutional Doping Platinum Single Atom.

    Wang, Ningyi / Liu, Zihe / Zhou, Yun / Zhao, Liupeng / Kou, Xueying / Wang, Tianshuang / Wang, Yanchao / Sun, Peng / Lu, Geyu

    Small (Weinheim an der Bergstrasse, Germany)

    2024  , Page(s) e2310465

    Abstract: The modification of metal oxides with noble metals is one of the most effective means of improving gas-sensing performance of chemiresistors, but it is often accompanied by unintended side effects such as sensor resistance increases up to unmeasurable ... ...

    Abstract The modification of metal oxides with noble metals is one of the most effective means of improving gas-sensing performance of chemiresistors, but it is often accompanied by unintended side effects such as sensor resistance increases up to unmeasurable levels. Herein, a carbonization-oxidation method is demonstrated using ultrasonic spray pyrolysis technique to realize platinum (Pt) single atom (SA) substitutional doping into SnO
    Language English
    Publishing date 2024-02-16
    Publishing country Germany
    Document type Journal Article
    ZDB-ID 2168935-0
    ISSN 1613-6829 ; 1613-6810
    ISSN (online) 1613-6829
    ISSN 1613-6810
    DOI 10.1002/smll.202310465
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  10. Article ; Online: Corrigendum to "Ultrabright organic fluorescent probe for quantifying the dynamics of cytosolic/nuclear lipid droplets" [Biosens. Bioelectron. 241 (2023) 115707].

    Liu, Guannan / Zheng, Huanlong / Zhou, Ri / Li, Huaiyu / Dai, Jianan / Wei, Jinbei / Li, Di / Meng, Xing / Wang, Chenguang / Lu, Geyu

    Biosensors & bioelectronics

    2023  Volume 246, Page(s) 115908

    Language English
    Publishing date 2023-12-04
    Publishing country England
    Document type Published Erratum
    ZDB-ID 1011023-9
    ISSN 1873-4235 ; 0956-5663
    ISSN (online) 1873-4235
    ISSN 0956-5663
    DOI 10.1016/j.bios.2023.115908
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