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  1. Article ; Online: Open Data and transparency in artificial intelligence and machine learning: A new era of research.

    Rodgers, Caellin M / Ellingson, Sally R / Chatterjee, Parag

    F1000Research

    2023  Volume 12, Page(s) 387

    Abstract: Artificial Intelligence (AI) and machine learning are the current forefront of computer science and technology. AI and related sub-disciplines, including machine learning, are essential technologies which have enabled the widespread use ... ...

    Abstract Artificial Intelligence (AI) and machine learning are the current forefront of computer science and technology. AI and related sub-disciplines, including machine learning, are essential technologies which have enabled the widespread use of
    MeSH term(s) Humans ; Artificial Intelligence ; Reproducibility of Results ; Machine Learning ; Health Facilities ; Industry
    Language English
    Publishing date 2023-04-12
    Publishing country England
    Document type Editorial
    ZDB-ID 2699932-8
    ISSN 2046-1402 ; 2046-1402
    ISSN (online) 2046-1402
    ISSN 2046-1402
    DOI 10.12688/f1000research.133019.1
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  2. Article ; Online: Artesunate acts through cytochrome c to inhibit growth of pediatric AML cells.

    Hill, Kristen S / Schuler, Erin E / Ellingson, Sally R / Kolesar, Jill M

    Scientific reports

    2023  Volume 13, Issue 1, Page(s) 22383

    Abstract: Artesunate is a derivative of artemisinin, an active compound isolated from Artemisia annua which has been used in Traditional Chinese Medicine and to treat malaria worldwide. Artemisinin derivatives have exhibited anti-cancer activity against both solid ...

    Abstract Artesunate is a derivative of artemisinin, an active compound isolated from Artemisia annua which has been used in Traditional Chinese Medicine and to treat malaria worldwide. Artemisinin derivatives have exhibited anti-cancer activity against both solid tumors and leukemia. The direct target(s) of artesunate are controversial; although, heme-bound proteins in the mitochondria have been implicated. We utilized computational modeling to calculate the predicted binding score of artesunate with heme-bound mitochondrial proteins and identified cytochrome c as potential artesunate target. UV-visible spectroscopy showed changes in the absorbance spectrum, and thus protein structure, when cytochrome c was incubated with artesunate. Artesunate induces apoptosis, disrupts mitochondrial membrane potential, and is antagonized by methazolamide in pediatric AML cells indicating a probable mechanism of action involving cytochrome c. We utilized a multi-disciplinary approach to show that artesunate can interact with and is dependent on cytochrome c release to induce cell death in pediatric AML cell lines.
    MeSH term(s) Child ; Humans ; Artesunate/pharmacology ; Antimalarials/pharmacology ; Antimalarials/therapeutic use ; Cytochromes c ; Artemisinins/pharmacology ; Heme ; Leukemia, Myeloid, Acute/drug therapy
    Chemical Substances Artesunate (60W3249T9M) ; Antimalarials ; Cytochromes c (9007-43-6) ; Artemisinins ; Heme (42VZT0U6YR)
    Language English
    Publishing date 2023-12-16
    Publishing country England
    Document type Journal Article
    ZDB-ID 2615211-3
    ISSN 2045-2322 ; 2045-2322
    ISSN (online) 2045-2322
    ISSN 2045-2322
    DOI 10.1038/s41598-023-49928-y
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  3. Article: Machine learning and ligand binding predictions: A review of data, methods, and obstacles

    Ellingson, Sally R / Davis, Brian / Allen, Jonathan

    Elsevier B.V. Biochimica et biophysica acta. 2020 June, v. 1864, no. 6

    2020  

    Abstract: Computational predictions of ligand binding is a difficult problem, with more accurate methods being extremely computationally expensive. The use of machine learning for drug binding predictions could possibly leverage the use of biomedical big data in ... ...

    Abstract Computational predictions of ligand binding is a difficult problem, with more accurate methods being extremely computationally expensive. The use of machine learning for drug binding predictions could possibly leverage the use of biomedical big data in exchange for time-intensive simulations. This paper reviews current trends in the use of machine learning for drug binding predictions, data sources to develop machine learning algorithms, and potential problems that may lead to overfitting and ungeneralizable models. A few popular datasets that can be used to develop virtual high-throughput screening models are characterized using spatial statistics to quantify potential biases. We can see from evaluating some common benchmarks that good performance correlates with models with high-predicted bias scores and models with low bias scores do not have much predictive power. A better understanding of the limits of available data sources and how to fix them will lead to more generalizable models that will lead to novel drug discovery.
    Keywords algorithms ; artificial intelligence ; data collection ; drugs ; high-throughput screening methods ; ligands ; models ; prediction ; statistics
    Language English
    Dates of publication 2020-06
    Publishing place Elsevier B.V.
    Document type Article
    ZDB-ID 840755-1
    ISSN 0304-4165
    ISSN 0304-4165
    DOI 10.1016/j.bbagen.2020.129545
    Database NAL-Catalogue (AGRICOLA)

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    In stock of ZB MED Cologne/Königswinter

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  4. Article ; Online: Machine learning and ligand binding predictions: A review of data, methods, and obstacles.

    Ellingson, Sally R / Davis, Brian / Allen, Jonathan

    Biochimica et biophysica acta. General subjects

    2020  Volume 1864, Issue 6, Page(s) 129545

    Abstract: Computational predictions of ligand binding is a difficult problem, with more accurate methods being extremely computationally expensive. The use of machine learning for drug binding predictions could possibly leverage the use of biomedical big data in ... ...

    Abstract Computational predictions of ligand binding is a difficult problem, with more accurate methods being extremely computationally expensive. The use of machine learning for drug binding predictions could possibly leverage the use of biomedical big data in exchange for time-intensive simulations. This paper reviews current trends in the use of machine learning for drug binding predictions, data sources to develop machine learning algorithms, and potential problems that may lead to overfitting and ungeneralizable models. A few popular datasets that can be used to develop virtual high-throughput screening models are characterized using spatial statistics to quantify potential biases. We can see from evaluating some common benchmarks that good performance correlates with models with high-predicted bias scores and models with low bias scores do not have much predictive power. A better understanding of the limits of available data sources and how to fix them will lead to more generalizable models that will lead to novel drug discovery.
    MeSH term(s) Algorithms ; Big Data ; Computational Biology ; Drug Discovery ; Humans ; Ligands ; Machine Learning ; Protein Binding/drug effects ; Protein Binding/genetics
    Chemical Substances Ligands
    Language English
    Publishing date 2020-02-10
    Publishing country Netherlands
    Document type Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
    ZDB-ID 60-7
    ISSN 1872-8006 ; 1879-2596 ; 1879-260X ; 1879-2642 ; 1879-2618 ; 1879-2650 ; 0006-3002 ; 0005-2728 ; 0005-2736 ; 0304-4165 ; 0167-4838 ; 1388-1981 ; 0167-4889 ; 0167-4781 ; 0304-419X ; 1570-9639 ; 0925-4439 ; 1874-9399
    ISSN (online) 1872-8006 ; 1879-2596 ; 1879-260X ; 1879-2642 ; 1879-2618 ; 1879-2650
    ISSN 0006-3002 ; 0005-2728 ; 0005-2736 ; 0304-4165 ; 0167-4838 ; 1388-1981 ; 0167-4889 ; 0167-4781 ; 0304-419X ; 1570-9639 ; 0925-4439 ; 1874-9399
    DOI 10.1016/j.bbagen.2020.129545
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    In stock of ZB MED Cologne/Königswinter

    Uc I Zs.247: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 2021: Bestellungen von Artikeln über das Online-Bestellformular
    ab Jg. 2022: Lesesaal (EG)

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  5. Article: Automated quality control for genome wide association studies.

    Ellingson, Sally R / Fardo, David W

    F1000Research

    2016  Volume 5, Page(s) 1889

    Abstract: This paper provides details on the necessary steps to assess and control data in genome wide association studies (GWAS) using genotype information on a large number of genetic markers for large number of individuals. Due to varied study designs and ... ...

    Abstract This paper provides details on the necessary steps to assess and control data in genome wide association studies (GWAS) using genotype information on a large number of genetic markers for large number of individuals. Due to varied study designs and genotyping platforms between multiple sites/projects as well as potential genotyping errors, it is important to ensure high quality data. Scripts and directions are provided to facilitate others in this process.
    Language English
    Publishing date 2016
    Publishing country England
    Document type Journal Article
    ZDB-ID 2699932-8
    ISSN 2046-1402
    ISSN 2046-1402
    DOI 10.12688/f1000research.9271.1
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  6. Book ; Online: Split Optimization for Protein/Ligand Binding Models

    Davis, Brian / Mcloughlin, Kevin / Allen, Jonathan / Ellingson, Sally

    2020  

    Abstract: In this paper, we investigate potential biases in datasets used to make drug binding predictions using machine learning. We investigate a recently published metric called the Asymmetric Validation Embedding (AVE) bias which is used to quantify this bias ... ...

    Abstract In this paper, we investigate potential biases in datasets used to make drug binding predictions using machine learning. We investigate a recently published metric called the Asymmetric Validation Embedding (AVE) bias which is used to quantify this bias and detect overfitting. We compare it to a slightly revised version and introduce a new weighted metric. We find that the new metrics allow to quantify overfitting while not overly limiting training data and produce models with greater predictive value.
    Keywords Quantitative Biology - Biomolecules
    Publishing date 2020-01-09
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  7. Article ; Online: Ensemble Docking in Drug Discovery: How Many Protein Configurations from Molecular Dynamics Simulations are Needed To Reproduce Known Ligand Binding?

    Evangelista Falcon, Wilfredo / Ellingson, Sally R / Smith, Jeremy C / Baudry, Jerome

    The journal of physical chemistry. B

    2019  Volume 123, Issue 25, Page(s) 5189–5195

    Abstract: Ensemble docking in drug discovery or chemical biology uses dynamical simulations of target proteins to generate binding site conformations for docking campaigns. We show that 600 ns molecular dynamics simulations of four G-protein-coupled receptors in ... ...

    Abstract Ensemble docking in drug discovery or chemical biology uses dynamical simulations of target proteins to generate binding site conformations for docking campaigns. We show that 600 ns molecular dynamics simulations of four G-protein-coupled receptors in their membrane environments generate ensembles of protein configurations that, collectively, are selected by 70?99% of the known ligands of these proteins. Therefore, the process of ligand recognition by conformational selection can be reproduced by combining molecular dynamics and docking calculations. Clustering of the molecular dynamics trajectories, however, does not necessarily identify the protein conformations that are most often selected by the ligands.
    MeSH term(s) Binding Sites ; Drug Discovery ; Humans ; Ligands ; Molecular Docking Simulation ; Molecular Dynamics Simulation ; Protein Binding ; Protein Conformation ; Receptor, Adenosine A2A/chemistry ; Receptor, Adenosine A2A/metabolism ; Receptors, G-Protein-Coupled/chemistry ; Receptors, G-Protein-Coupled/metabolism
    Chemical Substances ADORA2A protein, human ; Ligands ; Receptor, Adenosine A2A ; Receptors, G-Protein-Coupled
    Language English
    Publishing date 2019-02-12
    Publishing country United States
    Document type Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't ; Research Support, U.S. Gov't, Non-P.H.S.
    ISSN 1520-5207
    ISSN (online) 1520-5207
    DOI 10.1021/acs.jpcb.8b11491
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  8. Article: Ensemble Docking in Drug Discovery: How Many Protein Configurations from Molecular Dynamics Simulations are Needed To Reproduce Known Ligand Binding?

    Evangelista Falcon, Wilfredo / Baudry, Jerome / Ellingson, Sally R / Smith, Jeremy C

    Journal of physical chemistry. 2019 Jan. 29, v. 123, no. 25

    2019  

    Abstract: Ensemble docking in drug discovery or chemical biology uses dynamical simulations of target proteins to generate binding site conformations for docking campaigns. We show that 600 ns molecular dynamics simulations of four G-protein-coupled receptors in ... ...

    Abstract Ensemble docking in drug discovery or chemical biology uses dynamical simulations of target proteins to generate binding site conformations for docking campaigns. We show that 600 ns molecular dynamics simulations of four G-protein-coupled receptors in their membrane environments generate ensembles of protein configurations that, collectively, are selected by 70–99% of the known ligands of these proteins. Therefore, the process of ligand recognition by conformational selection can be reproduced by combining molecular dynamics and docking calculations. Clustering of the molecular dynamics trajectories, however, does not necessarily identify the protein conformations that are most often selected by the ligands.
    Keywords binding sites ; drugs ; G-protein coupled receptors ; ligands ; molecular dynamics ; simulation models
    Language English
    Dates of publication 2019-0129
    Size p. 5189-5195.
    Publishing place American Chemical Society
    Document type Article
    ISSN 1520-5207
    DOI 10.1021/acs.jpcb.8b11491
    Database NAL-Catalogue (AGRICOLA)

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  9. Article ; Online: Quantifying Overfitting Potential in Drug Binding Datasets

    Davis, Brian / Mcloughlin, Kevin / Allen, Jonathan / Ellingson, Sally R.

    Computational Science - ICCS 2020

    Abstract: In this paper, we investigate potential biases in datasets used to make drug binding predictions using machine learning. We investigate a recently published metric called the Asymmetric Validation Embedding (AVE) bias which is used to quantify this bias ... ...

    Abstract In this paper, we investigate potential biases in datasets used to make drug binding predictions using machine learning. We investigate a recently published metric called the Asymmetric Validation Embedding (AVE) bias which is used to quantify this bias and detect overfitting. We compare it to a slightly revised version and introduce a new weighted metric. We find that the new metrics allow to quantify overfitting while not overly limiting training data and produce models with greater predictive value.
    Keywords covid19
    Publisher PMC
    Document type Article ; Online
    DOI 10.1007/978-3-030-50420-5_44
    Database COVID19

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  10. Article: KEAP1 Is Required for Artesunate Anticancer Activity in Non-Small-Cell Lung Cancer.

    Hill, Kristen S / McDowell, Anthony / McCorkle, J Robert / Schuler, Erin / Ellingson, Sally R / Plattner, Rina / Kolesar, Jill M

    Cancers

    2021  Volume 13, Issue 8

    Abstract: Artesunate is the most common treatment for malaria throughout the world. Artesunate has anticancer activity likely through the induction of reactive oxygen species, the same mechanism of action utilized ... ...

    Abstract Artesunate is the most common treatment for malaria throughout the world. Artesunate has anticancer activity likely through the induction of reactive oxygen species, the same mechanism of action utilized in
    Language English
    Publishing date 2021-04-14
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2527080-1
    ISSN 2072-6694
    ISSN 2072-6694
    DOI 10.3390/cancers13081885
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