LIVIVO - Search results -

Search results

Result 1 - 10 of total 38

Search options

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

Order via subito

This service is chargeable due to the Delivery terms set by subito. Orders including an article and supplementary material will be classified as separate orders. In these cases, fees will be demanded for each order.

Inter-library loan at ZB MED

Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

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

Order via subito

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

Inter-library loan at ZB MED

Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

Details ▾

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 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

Order via subito

Inter-library loan at ZB MED

Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

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

Inter-library loan at ZB MED

Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

Details ▾

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

Inter-library loan at ZB MED

Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

Details ▾

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

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

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)

Order via subito

Details ▾
- See ZB MED holdings
- Order with fees

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
Database	MEDical Literature Analysis and Retrieval System OnLINE

In stock of ZB MED Cologne/Königswinter

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

Order via subito

Details ▾
- See ZB MED holdings
- Order with fees

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

Full text online

Accessible to users with ZB MED library card

In stock of ZB MED Cologne/Königswinter

Zs.A 308: 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)

Order via subito

Details ▾

Article ; Online: Analysis and prediction of unplanned intensive care unit readmission using recurrent neural networks with long short-term memory.

Lin, Yu-Wei / Zhou, Yuqian / Faghri, Faraz / Shaw, Michael J / Campbell, Roy H

PloS one

2019 Volume 14, Issue 7, Page(s) e0218942

Abstract: Background: Unplanned readmission of a hospitalized patient is an indicator of patients' exposure to risk and an avoidable waste of medical resources. In addition to hospital readmission, intensive care unit (ICU) readmission brings further financial ... ...

Abstract	Background: Unplanned readmission of a hospitalized patient is an indicator of patients' exposure to risk and an avoidable waste of medical resources. In addition to hospital readmission, intensive care unit (ICU) readmission brings further financial risk, along with morbidity and mortality risks. Identification of high-risk patients who are likely to be readmitted can provide significant benefits for both patients and medical providers. The emergence of machine learning solutions to detect hidden patterns in complex, multi-dimensional datasets provides unparalleled opportunities for developing an efficient discharge decision-making support system for physicians and ICU specialists. Methods and findings: We used supervised machine learning approaches for ICU readmission prediction. We used machine learning methods on comprehensive, longitudinal clinical data from the MIMIC-III to predict the ICU readmission of patients within 30 days of their discharge. We incorporate multiple types of features including chart events, demographic, and ICD-9 embeddings. We have utilized recent machine learning techniques such as Recurrent Neural Networks (RNN) with Long Short-Term Memory (LSTM), by this we have been able to incorporate the multivariate features of EHRs and capture sudden fluctuations in chart event features (e.g. glucose and heart rate). We show that our LSTM-based solution can better capture high volatility and unstable status in ICU patients, an important factor in ICU readmission. Our machine learning models identify ICU readmissions at a higher sensitivity rate of 0.742 (95% CI, 0.718-0.766) and an improved Area Under the Curve of 0.791 (95% CI, 0.782-0.800) compared with traditional methods. We perform in-depth deep learning performance analysis, as well as the analysis of each feature contribution to the predictive model. Conclusion: Our manuscript highlights the ability of machine learning models to improve our ICU decision-making accuracy and is a real-world example of precision medicine in hospitals. These data-driven solutions hold the potential for substantial clinical impact by augmenting clinical decision-making for physicians and ICU specialists. We anticipate that machine learning models will improve patient counseling, hospital administration, allocation of healthcare resources and ultimately individualized clinical care.
MeSH term(s)	Databases, Genetic ; Female ; Humans ; Intensive Care Units/statistics & numerical data ; Machine Learning ; Male ; Memory, Long-Term/physiology ; Memory, Short-Term/physiology ; Middle Aged ; Neural Networks, Computer ; Oxygen/metabolism ; Patient Discharge ; Patient Readmission/statistics & numerical data
Chemical Substances	Oxygen (S88TT14065)
Language	English
Publishing date	2019-07-08
Publishing country	United States
Document type	Journal Article
ZDB-ID	2267670-3
ISSN	1932-6203 ; 1932-6203
ISSN (online)	1932-6203
ISSN	1932-6203
DOI	10.1371/journal.pone.0218942
Database	MEDical Literature Analysis and Retrieval System OnLINE

Order via subito

To top

More links

Kategorien

Order via subito

Inter-library loan at ZB MED

More links

Kategorien

Order via subito

More links

Kategorien

Inter-library loan at ZB MED

More links

Kategorien

Order via subito

Inter-library loan at ZB MED

More links

Kategorien

Inter-library loan at ZB MED

More links

Kategorien

Inter-library loan at ZB MED

More links

Kategorien

In stock of ZB MED Cologne/Königswinter

Order via subito

More links

Kategorien

In stock of ZB MED Cologne/Königswinter

Order via subito

Full text online

More links

Kategorien

In stock of ZB MED Cologne/Königswinter

Order via subito

More links

Kategorien

Order via subito