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  1. Article ; Online: Uncovering the complexities of biological structures with network-based learning: An application in SARS-CoV-2.

    Faghri, Faraz / Nalls, Mike A

    Patterns (New York, N.Y.)

    2021  Volume 2, Issue 5, Page(s) 100259

    Abstract: Network-based learning enables the identification of possible undiscovered interactions in biological systems. In this issue ... ...

    Abstract Network-based learning enables the identification of possible undiscovered interactions in biological systems. In this issue of
    Language English
    Publishing date 2021-04-15
    Publishing country United States
    Document type News
    ISSN 2666-3899
    ISSN (online) 2666-3899
    DOI 10.1016/j.patter.2021.100259
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article: Unraveling the genetic complexity of Alzheimer disease with Mendelian Randomization.

    Bandres-Ciga, Sara / Faghri, Faraz

    Neurology. Genetics

    2019  Volume 5, Issue 2, Page(s) e313

    Language English
    Publishing date 2019-03-07
    Publishing country United States
    Document type Editorial
    ZDB-ID 2818607-2
    ISSN 2376-7839
    ISSN 2376-7839
    DOI 10.1212/NXG.0000000000000313
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Article: Federated Learning for multi-omics: a performance evaluation in Parkinson's disease.

    Danek, Benjamin / Makarious, Mary B / Dadu, Anant / Vitale, Dan / Lee, Paul Suhwan / Nalls, Mike A / Sun, Jimeng / Faghri, Faraz

    bioRxiv : the preprint server for biology

    2024  

    Abstract: While machine learning (ML) research has recently grown more in popularity, its application in the omics domain is constrained by access to sufficiently large, high-quality datasets needed to train ML models. Federated Learning (FL) represents an ... ...

    Abstract While machine learning (ML) research has recently grown more in popularity, its application in the omics domain is constrained by access to sufficiently large, high-quality datasets needed to train ML models. Federated Learning (FL) represents an opportunity to enable collaborative curation of such datasets among participating institutions. We compare the simulated performance of several models trained using FL against classically trained ML models on the task of multi-omics Parkinson's Disease prediction. We find that FL model performance tracks centrally trained ML models, where the most performant FL model achieves an AUC-PR of 0.876 ± 0.009, 0.014 ± 0.003 less than its centrally trained variation. We also determine that the dispersion of samples within a federation plays a meaningful role in model performance. Our study implements several open source FL frameworks and aims to highlight some of the challenges and opportunities when applying these collaborative methods in multi-omics studies.
    Language English
    Publishing date 2024-02-12
    Publishing country United States
    Document type Preprint
    DOI 10.1101/2023.10.04.560604
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  4. Article ; Online: Federated learning for multi-omics: A performance evaluation in Parkinson's disease.

    Danek, Benjamin P / Makarious, Mary B / Dadu, Anant / Vitale, Dan / Lee, Paul Suhwan / Singleton, Andrew B / Nalls, Mike A / Sun, Jimeng / Faghri, Faraz

    Patterns (New York, N.Y.)

    2024  Volume 5, Issue 3, Page(s) 100945

    Abstract: While machine learning (ML) research has recently grown more in popularity, its application in the omics domain is constrained by access to sufficiently large, high-quality datasets needed to train ML models. Federated learning (FL) represents an ... ...

    Abstract While machine learning (ML) research has recently grown more in popularity, its application in the omics domain is constrained by access to sufficiently large, high-quality datasets needed to train ML models. Federated learning (FL) represents an opportunity to enable collaborative curation of such datasets among participating institutions. We compare the simulated performance of several models trained using FL against classically trained ML models on the task of multi-omics Parkinson's disease prediction. We find that FL model performance tracks centrally trained ML models, where the most performant FL model achieves an AUC-PR of 0.876 ± 0.009, 0.014 ± 0.003 less than its centrally trained variation. We also determine that the dispersion of samples within a federation plays a meaningful role in model performance. Our study implements several open-source FL frameworks and aims to highlight some of the challenges and opportunities when applying these collaborative methods in multi-omics studies.
    Language English
    Publishing date 2024-03-01
    Publishing country United States
    Document type Journal Article
    ISSN 2666-3899
    ISSN (online) 2666-3899
    DOI 10.1016/j.patter.2024.100945
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  5. Article: GenoTools: An Open-Source Python Package for Efficient Genotype Data Quality Control and Analysis.

    Vitale, Dan / Koretsky, Mathew / Kuznetsov, Nicole / Hong, Samantha / Martin, Jessica / James, Mikayla / Makarious, Mary B / Leonard, Hampton / Iwaki, Hirotaka / Faghri, Faraz / Blauwendraat, Cornelis / Singleton, Andrew B / Song, Yeajin / Levine, Kristin / Kumar Sreelatha, Ashwin Ashok / Fang, Zih-Hua / Nalls, Mike

    bioRxiv : the preprint server for biology

    2024  

    Abstract: GenoTools, a Python package, streamlines population genetics research by integrating ancestry estimation, quality control (QC), and genome-wide association studies (GWAS) capabilities into efficient pipelines. By tracking samples, variants, and quality- ... ...

    Abstract GenoTools, a Python package, streamlines population genetics research by integrating ancestry estimation, quality control (QC), and genome-wide association studies (GWAS) capabilities into efficient pipelines. By tracking samples, variants, and quality-specific measures throughout fully customizable pipelines, users can easily manage genetics data for large and small studies. GenoTools' "Ancestry" module renders highly accurate predictions, allowing for high-quality ancestry-specific studies, and enables custom ancestry model training and serialization, specified to the user's genotyping or sequencing platform. As the genotype processing engine that powers several large initiatives including the NIH's Center for Alzheimer's and Related Dementias (CARD) and the Global Parkinson's Genetics Program (GP2). GenoTools was used to process and analyze the UK Biobank and major Alzheimer's Disease (AD) and Parkinson's Disease (PD) datasets with over 400,000 genotypes from arrays and 5000 sequences and has led to novel discoveries in diverse populations. It has provided replicable ancestry predictions, implemented rigorous QC, and conducted genetic ancestry-specific GWAS to identify systematic errors or biases through a single command. GenoTools is a customizable tool that enables users to efficiently analyze and scale genotype data with reproducible and scalable ancestry, QC, and GWAS pipelines.
    Language English
    Publishing date 2024-03-29
    Publishing country United States
    Document type Preprint
    DOI 10.1101/2024.03.26.586362
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  6. Article: omicSynth: an Open Multi-omic Community Resource for Identifying Druggable Targets across Neurodegenerative Diseases.

    Alvarado, Chelsea X / Makarious, Mary B / Weller, Cory A / Vitale, Dan / Koretsky, Mathew J / Bandres-Ciga, Sara / Iwaki, Hirotaka / Levine, Kristin / Singleton, Andrew / Faghri, Faraz / Nalls, Mike A / Leonard, Hampton L

    medRxiv : the preprint server for health sciences

    2023  

    Abstract: Treatments for neurodegenerative disorders remain rare, although recent FDA approvals, such as Lecanemab and Aducanumab for Alzheimer's Disease, highlight the importance of the underlying biological mechanisms in driving discovery and creating disease ... ...

    Abstract Treatments for neurodegenerative disorders remain rare, although recent FDA approvals, such as Lecanemab and Aducanumab for Alzheimer's Disease, highlight the importance of the underlying biological mechanisms in driving discovery and creating disease modifying therapies. The global population is aging, driving an urgent need for therapeutics that stop disease progression and eliminate symptoms. In this study, we create an open framework and resource for evidence-based identification of therapeutic targets for neurodegenerative disease. We use Summary-data-based Mendelian Randomization to identify genetic targets for drug discovery and repurposing. In parallel, we provide mechanistic insights into disease processes and potential network-level consequences of gene-based therapeutics. We identify 116 Alzheimer's disease, 3 amyotrophic lateral sclerosis, 5 Lewy body dementia, 46 Parkinson's disease, and 9 Progressive supranuclear palsy target genes passing multiple test corrections (p
    Language English
    Publishing date 2023-07-14
    Publishing country United States
    Document type Preprint
    DOI 10.1101/2023.04.06.23288266
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  7. Article ; Online: omicSynth: An open multi-omic community resource for identifying druggable targets across neurodegenerative diseases.

    Alvarado, Chelsea X / Makarious, Mary B / Weller, Cory A / Vitale, Dan / Koretsky, Mathew J / Bandres-Ciga, Sara / Iwaki, Hirotaka / Levine, Kristin / Singleton, Andrew / Faghri, Faraz / Nalls, Mike A / Leonard, Hampton L

    American journal of human genetics

    2023  Volume 111, Issue 1, Page(s) 150–164

    Abstract: Treatments for neurodegenerative disorders remain rare, but recent FDA approvals, such as lecanemab and aducanumab for Alzheimer disease (MIM: 607822), highlight the importance of the underlying biological mechanisms in driving discovery and creating ... ...

    Abstract Treatments for neurodegenerative disorders remain rare, but recent FDA approvals, such as lecanemab and aducanumab for Alzheimer disease (MIM: 607822), highlight the importance of the underlying biological mechanisms in driving discovery and creating disease modifying therapies. The global population is aging, driving an urgent need for therapeutics that stop disease progression and eliminate symptoms. In this study, we create an open framework and resource for evidence-based identification of therapeutic targets for neurodegenerative disease. We use summary-data-based Mendelian randomization to identify genetic targets for drug discovery and repurposing. In parallel, we provide mechanistic insights into disease processes and potential network-level consequences of gene-based therapeutics. We identify 116 Alzheimer disease, 3 amyotrophic lateral sclerosis (MIM: 105400), 5 Lewy body dementia (MIM: 127750), 46 Parkinson disease (MIM: 605909), and 9 progressive supranuclear palsy (MIM: 601104) target genes passing multiple test corrections (p
    MeSH term(s) Humans ; Alzheimer Disease/drug therapy ; Alzheimer Disease/genetics ; Community Resources ; Multiomics ; Neurodegenerative Diseases/drug therapy ; Neurodegenerative Diseases/genetics ; Parkinson Disease ; Mendelian Randomization Analysis
    Language English
    Publishing date 2023-12-29
    Publishing country United States
    Document type Journal Article
    ZDB-ID 219384-x
    ISSN 1537-6605 ; 0002-9297
    ISSN (online) 1537-6605
    ISSN 0002-9297
    DOI 10.1016/j.ajhg.2023.12.006
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

    Zs.A 107: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
    Jg. 1995 - 2021: Lesesall (1.OG)
    ab Jg. 2022: Lesesaal (EG)

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  8. Article ; Online: Virus exposure and neurodegenerative disease risk across national biobanks.

    Levine, Kristin S / Leonard, Hampton L / Blauwendraat, Cornelis / Iwaki, Hirotaka / Johnson, Nicholas / Bandres-Ciga, Sara / Ferrucci, Luigi / Faghri, Faraz / Singleton, Andrew B / Nalls, Mike A

    Neuron

    2023  Volume 111, Issue 7, Page(s) 1086–1093.e2

    Abstract: With recent findings connecting the Epstein-Barr virus to an increased risk of multiple sclerosis and growing concerns regarding the neurological impact of the coronavirus pandemic, we examined potential links between viral exposures and ... ...

    Abstract With recent findings connecting the Epstein-Barr virus to an increased risk of multiple sclerosis and growing concerns regarding the neurological impact of the coronavirus pandemic, we examined potential links between viral exposures and neurodegenerative disease risk. Using time series data from FinnGen for discovery and cross-sectional data from the UK Biobank for replication, we identified 45 viral exposures significantly associated with increased risk of neurodegenerative disease and replicated 22 of these associations. The largest effect association was between viral encephalitis exposure and Alzheimer's disease. Influenza with pneumonia was significantly associated with five of the six neurodegenerative diseases studied. We also replicated the Epstein-Barr/multiple sclerosis association. Some of these exposures were associated with an increased risk of neurodegeneration up to 15 years after infection. As vaccines are currently available for some of the associated viruses, vaccination may be a way to reduce some risk of neurodegenerative disease.
    MeSH term(s) Humans ; Neurodegenerative Diseases/epidemiology ; Cross-Sectional Studies ; Biological Specimen Banks ; Epstein-Barr Virus Infections ; Herpesvirus 4, Human ; Alzheimer Disease ; Multiple Sclerosis/epidemiology
    Language English
    Publishing date 2023-01-19
    Publishing country United States
    Document type Journal Article ; Research Support, N.I.H., Intramural
    ZDB-ID 808167-0
    ISSN 1097-4199 ; 0896-6273
    ISSN (online) 1097-4199
    ISSN 0896-6273
    DOI 10.1016/j.neuron.2022.12.029
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    Zs.A 2496: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
    Jg. 1995 - 2021: Lesesall (2.OG)
    ab Jg. 2022: Lesesaal (EG)
    Zs.MG 92: Show issues

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  9. Article ; Online: Application of Aligned-UMAP to longitudinal biomedical studies.

    Dadu, Anant / Satone, Vipul K / Kaur, Rachneet / Koretsky, Mathew J / Iwaki, Hirotaka / Qi, Yue A / Ramos, Daniel M / Avants, Brian / Hesterman, Jacob / Gunn, Roger / Cookson, Mark R / Ward, Michael E / Singleton, Andrew B / Campbell, Roy H / Nalls, Mike A / Faghri, Faraz

    Patterns (New York, N.Y.)

    2023  Volume 4, Issue 6, Page(s) 100741

    Abstract: High-dimensional data analysis starts with projecting the data to low dimensions to visualize and understand the underlying data structure. Several methods have been developed for dimensionality reduction, but they are limited to cross-sectional datasets. ...

    Abstract High-dimensional data analysis starts with projecting the data to low dimensions to visualize and understand the underlying data structure. Several methods have been developed for dimensionality reduction, but they are limited to cross-sectional datasets. The recently proposed Aligned-UMAP, an extension of the uniform manifold approximation and projection (UMAP) algorithm, can visualize high-dimensional longitudinal datasets. We demonstrated its utility for researchers to identify exciting patterns and trajectories within enormous datasets in biological sciences. We found that the algorithm parameters also play a crucial role and must be tuned carefully to utilize the algorithm's potential fully. We also discussed key points to remember and directions for future extensions of Aligned-UMAP. Further, we made our code open source to enhance the reproducibility and applicability of our work. We believe our benchmarking study becomes more important as more and more high-dimensional longitudinal data in biomedical research become available.
    Language English
    Publishing date 2023-05-08
    Publishing country United States
    Document type Journal Article
    ISSN 2666-3899
    ISSN (online) 2666-3899
    DOI 10.1016/j.patter.2023.100741
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  10. Article ; Online: Longitudinal risk factors for developing depressive symptoms in Parkinson's disease.

    Antar, Tarek / Morris, Huw R / Faghri, Faraz / Leonard, Hampton L / Nalls, Mike A / Singleton, Andrew B / Iwaki, Hirotaka

    Journal of the neurological sciences

    2021  Volume 429, Page(s) 117615

    Abstract: Background: Despite the established importance of identifying depression in Parkinson's disease, our understanding of the factors which place the Parkinson's disease patient at future risk of depression is limited.: Methods: Our sample consisted of ... ...

    Abstract Background: Despite the established importance of identifying depression in Parkinson's disease, our understanding of the factors which place the Parkinson's disease patient at future risk of depression is limited.
    Methods: Our sample consisted of 874 patients from two longitudinal cohorts, PPMI and PDBP, with median follow-up durations of 7 and 3 years respectively. Risk factors for depressive symptoms at baseline were determined using logistic regression. A Cox regression model was then used to identify baseline factors that predisposed the non-depressed patient to develop depressive symptoms that were sustained for at least one year, while adjusting for antidepressant use and cognitive impairment. Common predictors between the two cohorts were identified with a random-effects meta-analysis.
    Results: We found in our analyses that the majority of baseline non-depressed patients would develop sustained depressive symptoms at least once during the course of the study. Probable REM sleep behavior disorder (pRBD), age, duration of diagnosis, impairment in daily activities, mild constipation, and antidepressant use were among the baseline risk factors for depression in either cohort. Our Cox regression model indicated that pRBD, impairment in daily activities, hyposmia, and mild constipation could serve as longitudinal predictors of sustained depressive symptoms.
    Conclusions: We identified several potential risk factors to aid physicians in the early detection of depression in Parkinson's disease patients. Our findings also underline the importance of adjusting for multiple covariates when analyzing risk factors for depression.
    MeSH term(s) Cohort Studies ; Depression/epidemiology ; Depression/etiology ; Humans ; Parkinson Disease/complications ; Parkinson Disease/epidemiology ; REM Sleep Behavior Disorder ; Risk Factors
    Language English
    Publishing date 2021-08-12
    Publishing country Netherlands
    Document type Journal Article ; Meta-Analysis
    ZDB-ID 80160-4
    ISSN 1878-5883 ; 0022-510X ; 0374-8642
    ISSN (online) 1878-5883
    ISSN 0022-510X ; 0374-8642
    DOI 10.1016/j.jns.2021.117615
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    Zs.A 308: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
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