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Article ; Online: SARS-CoV-2 Drug Discovery based on Intrinsically Disordered Regions.

Mudide, Anish / Alterovitz, Gil

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing

2021 Volume 26, Page(s) 131–142

Abstract: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), a close relative of SARS-CoV-1, causes coronavirus disease 2019 (COVID-19), which, at the time of writing, has spread to over 19.9 million people worldwide. In this work, we aim to discover ... ...

Abstract	Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), a close relative of SARS-CoV-1, causes coronavirus disease 2019 (COVID-19), which, at the time of writing, has spread to over 19.9 million people worldwide. In this work, we aim to discover drugs capable of inhibiting SARS-CoV-2 through interaction modeling and statistical methods. Currently, many drug discovery approaches follow the typical protein structure-function paradigm, designing drugs to bind to fixed three-dimensional structures. However, in recent years such approaches have failed to address drug resistance and limit the set of possible drug targets and candidates. For these reasons we instead focus on targeting protein regions that lack a stable structure, known as intrinsically disordered regions (IDRs). Such regions are essential to numerous biological pathways that contribute to the virulence of various viruses. In this work, we discover eleven new SARS-CoV-2 drug candidates targeting IDRs and provide further evidence for the involvement of IDRs in viral processes such as enzymatic peptide cleavage while demonstrating the efficacy of our unique docking approach.
MeSH term(s)	COVID-19 ; Computational Biology ; Drug Discovery ; Humans ; SARS-CoV-2
Language	English
Publishing date	2021-03-09
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ISSN	2335-6936
ISSN (online)	2335-6936
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: The effects of department of Veterans Affairs medical centers on socio-economic outcomes: Evidence from the Paycheck Protection Program.

Makridis, Christos A / Kelly, J D / Alterovitz, Gil

PloS one

2022 Volume 17, Issue 12, Page(s) e0269588

Abstract: Do medical facilities also help advance improvements in socio-economic outcomes? We focus on Veterans, a vulnerable group over the COVID-19 pandemic who have access to a comprehensive healthcare network, and the receipt of funds from the Paycheck ... ...

Abstract	Do medical facilities also help advance improvements in socio-economic outcomes? We focus on Veterans, a vulnerable group over the COVID-19 pandemic who have access to a comprehensive healthcare network, and the receipt of funds from the Paycheck Protection Program (PPP) between April and June as a source of variation. First, we find that Veterans received 3.5% more loans and 6.8% larger loans than their counterparts (p < 0.01), controlling for a wide array of zipcode characteristics. Second, we develop models to predict the number of PPP loans awarded to Veterans, finding that the inclusion of local VA medical center characteristics adds almost as much explanatory power as the industry and occupational composition in an area and even more than the education, race, and age distribution combined. Our results suggest that VA medical centers can play an important role in helping Veterans thrive even beyond addressing their direct medical needs.
Language	English
Publishing date	2022-12-22
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.0269588
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Article ; Online: netAE: semi-supervised dimensionality reduction of single-cell RNA sequencing to facilitate cell labeling.

Dong, Zhengyang / Alterovitz, Gil

Bioinformatics (Oxford, England)

2020 Volume 37, Issue 1, Page(s) 43–49

Abstract: Motivation: Single-cell RNA sequencing allows us to study cell heterogeneity at an unprecedented cell-level resolution and identify known and new cell populations. Current cell labeling pipeline uses unsupervised clustering and assigns labels to ... ...

Abstract	Motivation: Single-cell RNA sequencing allows us to study cell heterogeneity at an unprecedented cell-level resolution and identify known and new cell populations. Current cell labeling pipeline uses unsupervised clustering and assigns labels to clusters by manual inspection. However, this pipeline does not utilize available gold-standard labels because there are usually too few of them to be useful to most computational methods. This article aims to facilitate cell labeling with a semi-supervised method in an alternative pipeline, in which a few gold-standard labels are first identified and then extended to the rest of the cells computationally. Results: We built a semi-supervised dimensionality reduction method, a network-enhanced autoencoder (netAE). Tested on three public datasets, netAE outperforms various dimensionality reduction baselines and achieves satisfactory classification accuracy even when the labeled set is very small, without disrupting the similarity structure of the original space. Availability and implementation: The code of netAE is available on GitHub: https://github.com/LeoZDong/netAE. Supplementary information: Supplementary data are available at Bioinformatics online.
MeSH term(s)	Base Sequence ; Cluster Analysis ; Sequence Analysis, RNA ; Single-Cell Analysis ; Whole Exome Sequencing
Language	English
Publishing date	2020-07-29
Publishing country	England
Document type	Journal Article
ZDB-ID	1422668-6
ISSN	1367-4811 ; 1367-4803
ISSN (online)	1367-4811
ISSN	1367-4803
DOI	10.1093/bioinformatics/btaa669
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Zs.A 2374: Show issues

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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)

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Article ; Online: How Much Does the (Social) Environment Matter? Using Artificial Intelligence to Predict COVID-19 Outcomes with Socio-demographic Data.

Makridis, Christos A / Mudide, Anish / Alterovitz, Gil

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing

2021 Volume 26, Page(s) 328–335

Abstract: While the coronavirus pandemic has affected all demographic brackets and geographies, certain areas have been more adversely affected than others. This paper focuses on Veterans as a potentially vulnerable group that might be systematically more exposed ... ...

Abstract	While the coronavirus pandemic has affected all demographic brackets and geographies, certain areas have been more adversely affected than others. This paper focuses on Veterans as a potentially vulnerable group that might be systematically more exposed to infection than others because of their co-morbidities, i.e., greater incidence of physical and mental health challenges. Using data on 122 Veteran Healthcare Systems (HCS), this paper tests three machine learning models for predictive analysis. The combined LASSO and ridge regression with five-fold cross validation performs the best. We find that socio-demographic features are highly predictive of both cases and deaths-even more important than any hospital-specific characteristics. These results suggest that socio-demographic and social capital characteristics are important determinants of public health outcomes, especially for vulnerable groups, like Veterans, and they should be investigated further.
MeSH term(s)	Artificial Intelligence ; COVID-19 ; Computational Biology ; Demography ; Humans ; SARS-CoV-2
Language	English
Publishing date	2021-03-09
Publishing country	United States
Document type	Journal Article
ISSN	2335-6936
ISSN (online)	2335-6936
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Team Science in Precision Medicine: Study of Coleadership and Coauthorship Across Health Organizations.

An, Ning / Mattison, John / Chen, Xinyu / Alterovitz, Gil

Journal of medical Internet research

2021 Volume 23, Issue 6, Page(s) e17137

Abstract: Background: Interdisciplinary collaborations bring lots of benefits to researchers in multiple areas, including precision medicine.: Objective: This viewpoint aims at studying how cross-institution team science would affect the development of ... ...

Abstract	Background: Interdisciplinary collaborations bring lots of benefits to researchers in multiple areas, including precision medicine. Objective: This viewpoint aims at studying how cross-institution team science would affect the development of precision medicine. Methods: Publications of organizations on the eHealth Catalogue of Activities were collected in 2015 and 2017. The significance of the correlation between coleadership and coauthorship among different organizations was calculated using the Pearson chi-square test of independence. Other nonparametric tests examined whether organizations with coleaders publish more and better papers than organizations without coleaders. Results: A total of 374 publications from 69 organizations were analyzed in 2015, and 7064 papers from 87 organizations were analyzed in 2017. Organizations with coleadership published more papers (P<.001, 2015 and 2017), which received higher citations (Z=-13.547, P<.001, 2017), compared to those without coleadership. Organizations with coleaders tended to publish papers together (P<.001, 2015 and 2017). Conclusions: Our findings suggest that organizations in the field of precision medicine could greatly benefit from institutional-level team science. As a result, stronger collaboration is recommended.
MeSH term(s)	Humans ; Interdisciplinary Research ; Precision Medicine ; Publications ; Telemedicine
Language	English
Publishing date	2021-06-14
Publishing country	Canada
Document type	Journal Article
ZDB-ID	2028830-X
ISSN	1438-8871 ; 1439-4456
ISSN (online)	1438-8871
ISSN	1439-4456
DOI	10.2196/17137
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: Ethical Applications of Artificial Intelligence: Evidence From Health Research on Veterans.

Makridis, Christos / Hurley, Seth / Klote, Mary / Alterovitz, Gil

JMIR medical informatics

2021 Volume 9, Issue 6, Page(s) e28921

Abstract: Background: Despite widespread agreement that artificial intelligence (AI) offers significant benefits for individuals and society at large, there are also serious challenges to overcome with respect to its governance. Recent policymaking has focused on ...

Abstract	Background: Despite widespread agreement that artificial intelligence (AI) offers significant benefits for individuals and society at large, there are also serious challenges to overcome with respect to its governance. Recent policymaking has focused on establishing principles for the trustworthy use of AI. Adhering to these principles is especially important for ensuring that the development and application of AI raises economic and social welfare, including among vulnerable groups and veterans. Objective: We explore the newly developed principles around trustworthy AI and how they can be readily applied at scale to vulnerable groups that are potentially less likely to benefit from technological advances. Methods: Using the US Department of Veterans Affairs as a case study, we explore the principles of trustworthy AI that are of particular interest for vulnerable groups and veterans. Results: We focus on three principles: (1) designing, developing, acquiring, and using AI so that the benefits of its use significantly outweigh the risks and the risks are assessed and managed; (2) ensuring that the application of AI occurs in well-defined domains and is accurate, effective, and fit for the intended purposes; and (3) ensuring that the operations and outcomes of AI applications are sufficiently interpretable and understandable by all subject matter experts, users, and others. Conclusions: These principles and applications apply more generally to vulnerable groups, and adherence to them can allow the VA and other organizations to continue modernizing their technology governance, leveraging the gains of AI while simultaneously managing its risks.
Language	English
Publishing date	2021-06-02
Publishing country	Canada
Document type	Journal Article
ZDB-ID	2798261-0
ISSN	2291-9694
ISSN	2291-9694
DOI	10.2196/28921
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Article ; Online: ChatGPT: Increasing accessibility for natural language processing in healthcare quality measurement.

Wu, Julie Tsu-Yu / Shenoy, Erica S / Carey, Evan P / Alterovitz, Gil / Kim, Michael J / Branch-Elliman, Westyn

Infection control and hospital epidemiology

2023 Volume 45, Issue 1, Page(s) 9–10

MeSH term(s)	Natural Language Processing ; Quality Assurance, Health Care ; Artificial Intelligence
Language	English
Publishing date	2023-11-10
Publishing country	United States
Document type	Journal Article
ZDB-ID	639378-0
ISSN	1559-6834 ; 0195-9417 ; 0899-823X
ISSN (online)	1559-6834
ISSN	0195-9417 ; 0899-823X
DOI	10.1017/ice.2023.236
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Zs.A 1714: Show issues

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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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Article ; Online: Developing and Implementing Predictive Models in a Learning Healthcare System: Traditional and Artificial Intelligence Approaches in the Veterans Health Administration.

Atkins, David / Makridis, Christos A / Alterovitz, Gil / Ramoni, Rachel / Clancy, Carolyn

Annual review of biomedical data science

2022 Volume 5, Page(s) 393–413

Abstract: Predicting clinical risk is an important part of healthcare and can inform decisions about treatments, preventive interventions, and provision of extra services. The field of predictive models has been revolutionized over the past two decades by ... ...

Abstract	Predicting clinical risk is an important part of healthcare and can inform decisions about treatments, preventive interventions, and provision of extra services. The field of predictive models has been revolutionized over the past two decades by electronic health record data; the ability to link such data with other demographic, socioeconomic, and geographic information; the availability of high-capacity computing; and new machine learning and artificial intelligence methods for extracting insights from complex datasets. These advances have produced a new generation of computerized predictive models, but debate continues about their development, reporting, validation, evaluation, and implementation. In this review we reflect on more than 10 years of experience at the Veterans Health Administration, the largest integrated healthcare system in the United States, in developing, testing, and implementing such models at scale. We report lessons from the implementation of national risk prediction models and suggest an agenda for research.
MeSH term(s)	Artificial Intelligence ; Delivery of Health Care ; Learning Health System ; Machine Learning ; United States ; Veterans Health
Language	English
Publishing date	2022-05-24
Publishing country	United States
Document type	Journal Article ; Review
ISSN	2574-3414
ISSN (online)	2574-3414
DOI	10.1146/annurev-biodatasci-122220-110053
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: An explainable machine learning platform for pyrazinamide resistance prediction and genetic feature identification of Mycobacterium tuberculosis.

Zhang, Andrew / Teng, Ling / Alterovitz, Gil

Journal of the American Medical Informatics Association : JAMIA

2020 Volume 28, Issue 3, Page(s) 533–540

Abstract: Objective: Tuberculosis is the leading cause of death from a single infectious agent. The emergence of antimicrobial resistant Mycobacterium tuberculosis strains makes the problem more severe. Pyrazinamide (PZA) is an important component for short- ... ...

Abstract	Objective: Tuberculosis is the leading cause of death from a single infectious agent. The emergence of antimicrobial resistant Mycobacterium tuberculosis strains makes the problem more severe. Pyrazinamide (PZA) is an important component for short-course treatment regimens and first- and second-line treatment regimens. This research aims for fast diagnosis of M. tuberculosis resistance to PZA and identification of genetic features causing resistance. Materials and methods: We use clinically collected genomic data of M. tuberculosis that are resistant or susceptible to PZA. A machine learning platform is built to diagnose PZA resistance using the whole genome sequence data, and to identify resistance genes and mutations. The platform consists of a deep convolutional neural network (DCNN) model for resistance diagnosis and a support vector machine (SVM) model as a surrogate to identify resistance genes and mutations. Results: The DCNN model achieves a PZA resistance diagnosis accuracy of 93%. Each prediction takes less than a second. The SVM has revealed 2 novel genes, embB and gyrA, besides the well-known pncA gene, and 9 mutations that harbor PZA resistance. Discussion: The DCNN and SVM machine learning platform, if used together with the real-time genome sequencing machines, could allow for rapid PZA diagnosis, allowing for critical time to ensure good patient outcomes, and preventing outbreaks of deadly infections. Furthermore, identifying pertinent resistance genes and mutations will help researchers better understand the biological mechanisms behind resistance. Conclusions: Machine learning can be used to achieve high-accuracy resistance prediction, and identify genes and mutations causing the resistance.
MeSH term(s)	Antitubercular Agents/pharmacology ; Antitubercular Agents/therapeutic use ; Drug Resistance, Bacterial ; Humans ; Machine Learning ; Microbial Sensitivity Tests ; Mutation ; Mycobacterium tuberculosis/drug effects ; Mycobacterium tuberculosis/genetics ; Neural Networks, Computer ; Pyrazinamide/pharmacology ; Pyrazinamide/therapeutic use ; Support Vector Machine ; Tuberculosis/drug therapy ; Tuberculosis/microbiology
Chemical Substances	Antitubercular Agents ; Pyrazinamide (2KNI5N06TI)
Language	English
Publishing date	2020-11-20
Publishing country	England
Document type	Journal Article ; Research Support, N.I.H., Extramural
ZDB-ID	1205156-1
ISSN	1527-974X ; 1067-5027
ISSN (online)	1527-974X
ISSN	1067-5027
DOI	10.1093/jamia/ocaa233
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Zs.A 4128: 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.MO 312: Show issues

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Article ; Online: Designing COVID-19 mortality predictions to advance clinical outcomes: Evidence from the Department of Veterans Affairs.

Makridis, Christos A / Strebel, Tim / Marconi, Vincent / Alterovitz, Gil

BMJ health & care informatics

2021 Volume 28, Issue 1

Abstract: Using administrative data on all Veterans who enter Department of Veterans Affairs (VA) medical centres throughout the USA, this paper uses artificial intelligence (AI) to predict mortality rates for patients with COVID-19 between March and August 2020. ... ...

Abstract	Using administrative data on all Veterans who enter Department of Veterans Affairs (VA) medical centres throughout the USA, this paper uses artificial intelligence (AI) to predict mortality rates for patients with COVID-19 between March and August 2020. First, using comprehensive data on over 10 000 Veterans' medical history, demographics and lab results, we estimate five AI models. Our XGBoost model performs the best, producing an area under the receive operator characteristics curve (AUROC) and area under the precision-recall curve of 0.87 and 0.41, respectively. We show how focusing on the performance of the AUROC alone can lead to unreliable models. Second, through a unique collaboration with the Washington D.C. VA medical centre, we develop a dashboard that incorporates these risk factors and the contributing sources of risk, which we deploy across local VA medical centres throughout the country. Our results provide a concrete example of how AI recommendations can be made explainable and practical for clinicians and their interactions with patients.
MeSH term(s)	Artificial Intelligence ; COVID-19/mortality ; Data Display ; Humans ; Models, Statistical ; Risk Factors ; United States ; United States Department of Veterans Affairs ; Veterans
Language	English
Publishing date	2021-05-25
Publishing country	England
Document type	Journal Article
ISSN	2632-1009
ISSN (online)	2632-1009
DOI	10.1136/bmjhci-2020-100312
Database	MEDical Literature Analysis and Retrieval System OnLINE

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