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  1. Article: Applications and Challenges of Machine Learning Methods in Alzheimer's Disease Multi-Source Data Analysis.

    Li, Xiong / Qiu, Yangping / Zhou, Juan / Xie, Ziruo

    Current genomics

    2022  Volume 22, Issue 8, Page(s) 564–582

    Abstract: Background: Recent development in neuroimaging and genetic testing technologies have made it possible to measure pathological features associated with Alzheimer's disease (AD) : Objective: To introduce and summarize the applications and challenges of ...

    Abstract Background: Recent development in neuroimaging and genetic testing technologies have made it possible to measure pathological features associated with Alzheimer's disease (AD)
    Objective: To introduce and summarize the applications and challenges of machine learning methods in Alzheimer's disease multi-source data analysis.
    Methods: The literature selected in the review is obtained from Google Scholar, PubMed, and Web of Science. The keywords of literature retrieval include Alzheimer's disease, bioinformatics, image genetics, genome-wide association research, molecular interaction network, multi-omics data integration, and so on.
    Conclusion: This study comprehensively introduces machine learning-based processing techniques for AD neuroimaging data and then shows the progress of computational analysis methods in omics data, such as the genome, proteome, and so on. Subsequently, machine learning methods for AD imaging analysis are also summarized. Finally, we elaborate on the current emerging technology of multi-modal neuroimaging, multi-omics data joint analysis, and present some outstanding issues and future research directions.
    Language English
    Publishing date 2022-04-07
    Publishing country United Arab Emirates
    Document type Journal Article ; Review
    ZDB-ID 2033677-9
    ISSN 1875-5488 ; 1389-2029
    ISSN (online) 1875-5488
    ISSN 1389-2029
    DOI 10.2174/1389202923666211216163049
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article ; Online: Annotating whole genome variants and constructing a multi-classifier based on samples of ADNI.

    Zhou, Juan / Qiu, Yangping / Liu, Xiangyu / Xie, Ziruo / Lv, Shanguo / Peng, Yuanyuan / Li, Xiong

    Frontiers in bioscience (Landmark edition)

    2022  Volume 27, Issue 1, Page(s) 37

    Abstract: Introduction: Alzheimer's disease (AD) is the most common progressive neurodegenerative disorder in the elderly, which will eventually lead to dementia without an effective precaution and treatment. As a typical complex disease, the mechanism of AD's ... ...

    Abstract Introduction: Alzheimer's disease (AD) is the most common progressive neurodegenerative disorder in the elderly, which will eventually lead to dementia without an effective precaution and treatment. As a typical complex disease, the mechanism of AD's occurrence and development still lacks sufficient understanding.
    Research design and methods: In this study, we aim to directly analyze the relationship between DNA variants and phenotypes based on the whole genome sequencing data. Firstly, to enhance the biological meanings of our study, we annotate the deleterious variants and mapped them to nearest protein coding genes. Then, to eliminate the redundant features and reduce the burden of downstream analysis, a multi-objective evaluation strategy based on entropy theory is applied for ranking all candidate genes. Finally, we use multi-classifier XGBoost for classifying unbalanced data composed with 46 AD samples, 483 mild cognitive impairment (MCI) samples and 279 cognitive normal (CN) samples.
    Results: The experimental results on real whole genome sequencing data from Alzheimer's Disease Neuroimaging Initiative (ADNI) show that our method not only has satisfactory classification performance but also finds significance correlation between AD and
    Conclusions: From the experimental results, we demonstrated that the efficacy of our proposed method has practical significance.
    MeSH term(s) Aged ; Alzheimer Disease/genetics ; Brain ; Cognitive Dysfunction ; Humans ; Magnetic Resonance Imaging/methods ; Neuroimaging/methods
    Language English
    Publishing date 2022-01-28
    Publishing country Singapore
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 2704569-9
    ISSN 2768-6698 ; 2768-6698
    ISSN (online) 2768-6698
    ISSN 2768-6698
    DOI 10.31083/j.fbl2701037
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Article ; Online: Optimal modeling of anti-breast cancer candidate drugs screening based on multi-model ensemble learning with imbalanced data.

    Zhou, Juan / Li, Xiong / Ma, Yuanting / Wu, Zejiu / Xie, Ziruo / Zhang, Yuqi / Wei, Yiming

    Mathematical biosciences and engineering : MBE

    2022  Volume 20, Issue 3, Page(s) 5117–5134

    Abstract: The imbalanced data makes the machine learning model seriously biased, which leads to false positive in screening of therapeutic drugs for breast cancer. In order to deal with this problem, a multi-model ensemble framework based on tree-model, linear ... ...

    Abstract The imbalanced data makes the machine learning model seriously biased, which leads to false positive in screening of therapeutic drugs for breast cancer. In order to deal with this problem, a multi-model ensemble framework based on tree-model, linear model and deep-learning model is proposed. Based on the methodology constructed in this study, we screened the 20 most critical molecular descriptors from 729 molecular descriptors of 1974 anti-breast cancer drug candidates and, in order to measure the pharmacokinetic properties and safety of the drug candidates, the screened molecular descriptors were used in this study for subsequent bioactivity, absorption, distribution metabolism, excretion, toxicity, and other prediction tasks. The results show that the method constructed in this study is superior and more stable than the individual models used in the ensemble approach.
    MeSH term(s) Humans ; Female ; Early Detection of Cancer ; Breast Neoplasms/drug therapy ; Machine Learning ; Linear Models
    Language English
    Publishing date 2022-12-29
    Publishing country United States
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 2265126-3
    ISSN 1551-0018 ; 1551-0018
    ISSN (online) 1551-0018
    ISSN 1551-0018
    DOI 10.3934/mbe.2023237
    Database MEDical Literature Analysis and Retrieval System OnLINE

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