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  1. Book: Clinical Applications of Artificial Intelligence in Real-World Data

    Asselbergs, Folkert W. / Moore, Jason H. / Oberski, Daniel L. / Denaxas, Spiros

    2023  

    Author's details Dr Folkert Asselbergs is a clinical cardiologist at Amsterdam Heart Center, Prof of Precision medicine at the Institute of Health Informatics, University College London, director and founder of the BRC Clinical Research Informatics Unit and the recently initiated Nudging Unit at University College London Hospital, chair of the data infrastructure of the Dutch Cardiovascular Alliance, and associate editor of European Heart Journal for digital health and innovation. His research program focuses on translational data science using existing health data such as electronic health records and clinical registries enriched with novel modalities such as -omics and sensor data for knowledge discovery, drug target validation and precision medicine in cardiovascular disease. §Dr Spiros Denaxas is a Professor in Biomedical Informatics based at the Institute of Health Informatics at University College London and Associate Director leading phenomics at the British Heart Foundation Data Science Cent
    Keywords Bighealthdata ; ArtificialIntelligence ; machinelearning ; DeepLearning ; Biomedicalontologies ; electronichealthrecords ; Big health data ; Artificial intelligence ; Machine learning ; Deep learning ; Biomedical ontologies ; Electronic Health Records
    Language English
    Size 292 p.
    Edition 1
    Publisher Springer International Publishing
    Document type Book
    Note PDA Manuell_25
    Format 183 x 260 x 21
    ISBN 9783031366772 ; 3031366778
    Database PDA

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  2. Book: Epistasis

    Moore, Jason H. / Williams, Scott M.

    methods and protocols

    (Methods in molecular biology ; 1253 ; Springer protocols)

    2015  

    Author's details ed. by Jason H. Moore ; Scott M. Williams
    Series title Methods in molecular biology ; 1253
    Springer protocols
    Collection
    Language English
    Size X, 350 S. : Ill., graph. Darst.
    Publisher Humana Press
    Publishing place New York u.a.
    Publishing country United States
    Document type Book
    HBZ-ID HT018509578
    ISBN 978-1-4939-2154-6 ; 9781493921553 ; 1-4939-2154-1 ; 149392155X
    Database Catalogue ZB MED Medicine, Health

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    In stock of ZB MED Cologne/Königswinter

    Shelf markLoan statusLocation
    Zs A 7042 -1253- verfügbar in Königswinter Königswinter

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  3. Article: Empowering the data science scientist.

    Moore, Jason H

    BioData mining

    2021  Volume 14, Issue 1, Page(s) 8

    Language English
    Publishing date 2021-01-23
    Publishing country England
    Document type Editorial
    ZDB-ID 2438773-3
    ISSN 1756-0381
    ISSN 1756-0381
    DOI 10.1186/s13040-021-00246-x
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  4. Article: Genetics and precision health: the ecological fallacy and artificial intelligence solutions.

    Williams, Scott M / Moore, Jason H

    BioData mining

    2023  Volume 16, Issue 1, Page(s) 9

    Language English
    Publishing date 2023-03-13
    Publishing country England
    Document type Editorial
    ZDB-ID 2438773-3
    ISSN 1756-0381
    ISSN 1756-0381
    DOI 10.1186/s13040-023-00327-z
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  5. Book ; Online: Genetic Programming Theory and Practice

    Sipper, Moshe / Moore, Jason H.

    A Fifteen-Year Trajectory

    2024  

    Abstract: The GPTP workshop series, which began in 2003, has served over the years as a focal meeting for genetic programming (GP) researchers. As such, we think it provides an excellent source for studying the development of GP over the past fifteen years. We ... ...

    Abstract The GPTP workshop series, which began in 2003, has served over the years as a focal meeting for genetic programming (GP) researchers. As such, we think it provides an excellent source for studying the development of GP over the past fifteen years. We thus present herein a trajectory of the thematic developments in the field of GP.
    Keywords Computer Science - Neural and Evolutionary Computing
    Publishing date 2024-02-01
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

    Full text online

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  6. Book: Computational methods for genetics of complex traits

    Moore, Jason H. / Dunlap, Jay C.

    (Advances in genetics ; 72)

    2010  

    Author's details ed. by Jay C. Dunlap ; Jason H. Moore
    Series title Advances in genetics ; 72
    Collection
    Language English
    Size X, 200 S. : Ill., graph. Darst.
    Edition 1. ed.
    Publisher Elsevier
    Publishing place Amsterdam u.a.
    Publishing country Netherlands
    Document type Book
    HBZ-ID HT016595534
    ISBN 978-0-12-380862-2 ; 0-12-380862-6
    Database Catalogue ZB MED Medicine, Health

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    In stock of ZB MED Cologne/Königswinter

    Shelf markLoan statusLocation
    Se 836 -72- verfügbar in Königswinter Königswinter

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  7. Article: STAR_outliers: a python package that separates univariate outliers from non-normal distributions.

    Gregg, John T / Moore, Jason H

    BioData mining

    2023  Volume 16, Issue 1, Page(s) 25

    Abstract: There are not currently any univariate outlier detection algorithms that transform and model arbitrarily shaped distributions to remove univariate outliers. Some algorithms model skew, even fewer model kurtosis, and none of them model bimodality and ... ...

    Abstract There are not currently any univariate outlier detection algorithms that transform and model arbitrarily shaped distributions to remove univariate outliers. Some algorithms model skew, even fewer model kurtosis, and none of them model bimodality and monotonicity. To overcome these challenges, we have implemented an algorithm for Skew and Tail-heaviness Adjusted Removal of Outliers (STAR_outliers) that robustly removes univariate outliers from distributions with many different shape profiles, including extreme skew, extreme kurtosis, bimodality, and monotonicity. We show that STAR_outliers removes simulated outliers with greater recall and precision than several general algorithms, and it also models the outlier bounds of real data distributions with greater accuracy.Background Reliably removing univariate outliers from arbitrarily shaped distributions is a difficult task. Incorrectly assuming unimodality or overestimating tail heaviness fails to remove outliers, while underestimating tail heaviness incorrectly removes regular data from the tails. Skew often produces one heavy tail and one light tail, and we show that several sophisticated outlier removal algorithms often fail to remove outliers from the light tail. Multivariate outlier detection algorithms have recently become popular, but having tested PyOD's multivariate outlier removal algorithms, we found them to be inadequate for univariate outlier removal. They usually do not allow for univariate input, and they do not fit their distributions of outliership scores with a model on which an outlier threshold can be accurately established. Thus, there is a need for a flexible outlier removal algorithm that can model arbitrarily shaped univariate distributions.Results In order to effectively model arbitrarily shaped univariate distributions, we have combined several well-established algorithms into a new algorithm called STAR_outliers. STAR_outliers removes more simulated true outliers and fewer non-outliers than several other univariate algorithms. These include several normality-assuming outlier removal methods, PyOD's isolation forest (IF) outlier removal algorithm (ACM Transactions on Knowledge Discovery from Data (TKDD) 6:3, 2012) with default settings, and an IQR based algorithm by Verardi and Vermandele that removes outliers while accounting for skew and kurtosis (Verardi and Vermandele, Journal de la Société Française de Statistique 157:90-114, 2016). Since the IF algorithm's default model poorly fit the outliership scores, we also compared the isolation forest algorithm with a model that entails removing as many datapoints as STAR_outliers does in order of decreasing outliership scores. We also compared these algorithms on the publicly available 2018 National Health and Nutrition Examination Survey (NHANES) data by setting the outlier threshold to keep values falling within the main 99.3 percent of the fitted model's domain. We show that our STAR_outliers algorithm removes significantly closer to 0.7 percent of values from these features than other outlier removal methods on average.Conclusions STAR_outliers is an easily implemented python package for removing outliers that outperforms multiple commonly used methods of univariate outlier removal.
    Language English
    Publishing date 2023-09-04
    Publishing country England
    Document type Journal Article
    ZDB-ID 2438773-3
    ISSN 1756-0381
    ISSN 1756-0381
    DOI 10.1186/s13040-023-00342-0
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  8. Article: Ten important roles for academic leaders in data science.

    Moore, Jason H

    BioData mining

    2020  Volume 13, Page(s) 18

    Abstract: Data science has emerged as an important discipline in the era of big data and biological and biomedical data mining. As such, we have seen a rapid increase in the number of data science departments, research centers, and schools. We review here ten ... ...

    Abstract Data science has emerged as an important discipline in the era of big data and biological and biomedical data mining. As such, we have seen a rapid increase in the number of data science departments, research centers, and schools. We review here ten important leadership roles for a successful academic data science chair, director, or dean. These roles include the visionary, executive, cheerleader, manager, enforcer, subordinate, educator, entrepreneur, mentor, and communicator. Examples specific to leadership in data science are given for each role.
    Language English
    Publishing date 2020-10-26
    Publishing country England
    Document type Editorial
    ZDB-ID 2438773-3
    ISSN 1756-0381
    ISSN 1756-0381
    DOI 10.1186/s13040-020-00228-5
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  9. Article ; Online: Empowering the data science scientist

    Jason H. Moore

    BioData Mining, Vol 14, Iss 1, Pp 1-

    2021  Volume 3

    Keywords Computer applications to medicine. Medical informatics ; R858-859.7 ; Analysis ; QA299.6-433
    Language English
    Publishing date 2021-01-01T00:00:00Z
    Publisher BMC
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

    Full text online

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  10. Article ; Online: Generative and reproducible benchmarks for comprehensive evaluation of machine learning classifiers.

    Orzechowski, Patryk / Moore, Jason H

    Science advances

    2022  Volume 8, Issue 47, Page(s) eabl4747

    Abstract: Understanding the strengths and weaknesses of machine learning (ML) algorithms is crucial to determine their scope of application. Here, we introduce the Diverse and Generative ML Benchmark (DIGEN), a collection of synthetic datasets for comprehensive, ... ...

    Abstract Understanding the strengths and weaknesses of machine learning (ML) algorithms is crucial to determine their scope of application. Here, we introduce the Diverse and Generative ML Benchmark (DIGEN), a collection of synthetic datasets for comprehensive, reproducible, and interpretable benchmarking of ML algorithms for classification of binary outcomes. The DIGEN resource consists of 40 mathematical functions that map continuous features to binary targets for creating synthetic datasets. These 40 functions were found using a heuristic algorithm designed to maximize the diversity of performance among multiple popular ML algorithms, thus providing a useful test suite for evaluating and comparing new methods. Access to the generative functions facilitates understanding of why a method performs poorly compared to other algorithms, thus providing ideas for improvement.
    Language English
    Publishing date 2022-11-23
    Publishing country United States
    Document type Journal Article
    ZDB-ID 2810933-8
    ISSN 2375-2548 ; 2375-2548
    ISSN (online) 2375-2548
    ISSN 2375-2548
    DOI 10.1126/sciadv.abl4747
    Database MEDical Literature Analysis and Retrieval System OnLINE

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