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  1. Book ; Online ; E-Book: Advances in artificial intelligence, computation, and data science

    Pham, Tuan D. / Yan, Hong / Ashraf, Muhammad W. / Sjöberg, Folke

    for medicine and life science

    (Computational biology ; 31)

    2021  

    Author's details Tuan D. Pham, Hong Yan, Muhammad W. Ashraf, Folke Sjöberg editors
    Series title Computational biology ; 31
    Collection
    Keywords Electronic books
    Language English
    Size 1 Online-Ressource (xiv, 369 Seiten), Illustrationen, Diagramme
    Publisher Springer
    Publishing place Cham
    Publishing country Switzerland
    Document type Book ; Online ; E-Book
    Remark Zugriff für angemeldete ZB MED-Nutzerinnen und -Nutzer
    HBZ-ID HT021084504
    ISBN 978-3-030-69951-2 ; 9783030699505 ; 3-030-69951-X ; 3030699501
    DOI 10.1007/978-3-030-69951-2
    Database ZB MED Catalogue: Medicine, Health, Nutrition, Environment, Agriculture

    Full text online

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  2. Book: Advances in Artificial Intelligence, Computation, and Data Science

    Pham, Tuan D. / Sjöberg, Folke / Ashraf, Muhammad W. / Yan, Hong

    For Medicine and Life Science

    (Computational Biology)

    2021  

    Author's details Tuan D. Pham is professor and founding director of the Center for Artificial Intelligence at Prince Mohammad Bin Fahd University, Saudi Arabia. His previous position was Professor of Biomedical Engineering at Linkoping University, Sweden. His current research focuses on AI and machine learning methods for image processing, time-series analysis, complex networks, and pattern recognition applied to medicine, biology, and mental health. In 2020, Dr. Pham was selected as Expert in Artificial Intelligence for consultation by the U.S. Food & Drug Administration (FDA) Center for Devices and Radiological Health (CDRH) Network of Digital Health Experts Program (NoDEx). Hong Yan is currently chair professor of computer engineering at City University of Hong Kong. His research interests include image processing, pattern recognition, and bioinformatics. He has over 600 journal and conference publications in these areas. Professor Yan is IEEE Fellow and IAPR Fellow. He received the 2016 Norb
    Series title Computational Biology
    Keywords Machine Learning ; Neural Networks ; fuzzy logic ; bio-inspired optimization ; Computational Intelligence ; Computational Algorithms ; data analytics ; Big Data ; Data Mining ; knowledge discovery ; Drug Discovery ; Biomaker Discovery ; Biomedicine ; Biology ; chemistry ; biochemistry ; Artificial Intelligence ; Fuzzy Logic ; Bio-inspired Optimization ; Data Analytics ; Data Science ; Knowledge Discovery ; Medicine ; Life Science ; Chemistry ; Biochemistry
    Language English
    Size 388 p.
    Edition 1
    Publisher Springer International Publishing
    Document type Book
    Note PDA Manuell_10
    Format 160 x 241 x 27
    ISBN 9783030699505 ; 3030699501
    Database PDA

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  3. Book ; Conference proceedings: Biomedical informatics and technology

    Pham, Tuan D.

    First International Conference, ACBIT 2013, Aizu-Wakamatsu, Japan, September 16-17, 2013. Revised selected papers

    (Communications in computer and information science ; 404)

    2014  

    Event/congress International Aizu Conference on Biomedical Informatics and Technology (1, 2013, Aizu-Wakamatsu)
    Author's details Tuan D. Pham ... (ed.)
    Series title Communications in computer and information science ; 404
    Collection
    Keywords Medizinische Informatik ; Bioinformatik ; Biomedizin
    Subject Medizininformatik
    Language English
    Size XI, 326 S. : Ill., graph. Darst., 235 mm x 155 mm
    Publisher Springer
    Publishing place Heidelberg u.a.
    Publishing country Germany
    Document type Book ; Conference proceedings
    HBZ-ID HT018240581
    ISBN 978-3-642-54120-9 ; 3-642-54120-8 ; 9783642541216 ; 3642541216
    Database Catalogue ZB MED Medicine, Health

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    In stock of ZB MED Cologne/Königswinter

    Shelf markLoan statusLocation
    2014 A 1713 available for loan (reading room) Lesesaal EG

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  4. Article ; Online: Classification of Caenorhabditis Elegans Locomotion Behaviors With Eigenfeature-Enhanced Long Short-Term Memory Networks.

    Pham, Tuan D

    IEEE/ACM transactions on computational biology and bioinformatics

    2023  Volume 20, Issue 1, Page(s) 206–216

    Abstract: The free-living nematode Caenorhabditis elegans is an ideal model for understanding behavior and networks of neurons. Experimental and quantitative analyses of neural circuits and behavior have led to system-level understanding of behavioral genetics and ...

    Abstract The free-living nematode Caenorhabditis elegans is an ideal model for understanding behavior and networks of neurons. Experimental and quantitative analyses of neural circuits and behavior have led to system-level understanding of behavioral genetics and process of transformation from sensory integration in stimulus environments to behavioral outcomes. The ability to differentiate locomotion behavior between wild-type and mutant Caenorhabditis elegans strains allows precise inference on and gaining insights into genetic and environmental influences on behaviors. This paper presents an eigenfeature-enhanced deep-learning method for classifying the dynamics of locomotion behavior of wild-type and mutant Caenorhabditis elegans. Classification results obtained from public benchmark time-series data of eigenworms illustrate the superior performance of the new method over several existing classifiers. The proposed method has potential as a useful artificial-intelligence tool for automated identification of the nematode worm behavioral patterns aiming at elucidating molecular and genetic mechanisms that control the nervous system.
    MeSH term(s) Animals ; Caenorhabditis elegans/genetics ; Behavior, Animal/physiology ; Memory, Short-Term ; Caenorhabditis elegans Proteins ; Locomotion/genetics
    Chemical Substances Caenorhabditis elegans Proteins
    Language English
    Publishing date 2023-02-03
    Publishing country United States
    Document type Journal Article
    ISSN 1557-9964
    ISSN (online) 1557-9964
    DOI 10.1109/TCBB.2022.3153668
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  5. Article ; Online: Prediction of Five-Year Survival Rate for Rectal Cancer Using Markov Models of Convolutional Features of RhoB Expression on Tissue Microarray.

    Pham, Tuan D

    IEEE/ACM transactions on computational biology and bioinformatics

    2023  Volume 20, Issue 5, Page(s) 3195–3204

    Abstract: The ability to predict survival in cancer is clinically important because the finding can help patients and physicians make optimal treatment decisions. Artificial intelligence in the context of deep learning has been increasingly realized by the ... ...

    Abstract The ability to predict survival in cancer is clinically important because the finding can help patients and physicians make optimal treatment decisions. Artificial intelligence in the context of deep learning has been increasingly realized by the informatics-oriented medical community as a powerful machine-learning technology for cancer research, diagnosis, prediction, and treatment. This paper presents the combination of deep learning, data coding, and probabilistic modeling for predicting five-year survival in a cohort of patients with rectal cancer using images of RhoB expression on biopsies. Using about one-third of the patients' data for testing, the proposed approach achieved 90% prediction accuracy, which is much higher than the direct use of the best pretrained convolutional neural network (70%) and the best coupling of a pretrained model and support vector machines (70%).
    MeSH term(s) Humans ; Artificial Intelligence ; Survival Rate ; Neural Networks, Computer ; Machine Learning ; Rectal Neoplasms/genetics
    Language English
    Publishing date 2023-10-09
    Publishing country United States
    Document type Journal Article
    ISSN 1557-9964
    ISSN (online) 1557-9964
    DOI 10.1109/TCBB.2023.3274211
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  6. Book: Knowledge based systems in biomedicine and computational life science

    Pham, Tuan D.

    (Studies in computational intelligence ; 450)

    2013  

    Title variant Knowledge-based systems in biomedicine and computational life science
    Author's details Tuan D. Pham ... (ed.)
    Series title Studies in computational intelligence ; 450
    Collection
    Keywords Wissensbasiertes System ; Biomedizin
    Subject Wissenssystem ; Knowledge system ; Knowledge based system ; Knowledge-based system
    Language English
    Size XII, 213 S. : Ill., graph. Darst., 235 mm x 155 mm
    Publisher Springer
    Publishing place Berlin u.a.
    Publishing country Germany
    Document type Book
    HBZ-ID HT017513456
    ISBN 978-3-642-33014-8 ; 3-642-33014-2 ; 9783642330155 ; 3642330150
    Database Catalogue ZB MED Medicine, Health

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    In stock of ZB MED Cologne/Königswinter

    Shelf markLoan statusLocation
    2012 A 4842 order for loan Magazin

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  7. Article ; Online: Classification of Motor-Imagery Tasks using a Large EEG Dataset by Fusing Classifiers Learning on Wavelet-Scattering Features.

    Pham, Tuan D

    IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society

    2023  Volume PP

    Abstract: Brain-computer or brain-machine interface technology allows humans to control machines using their thoughts via brain signals. In particular, these interfaces can assist people with neurological diseases for speech understanding or physical disabilities ... ...

    Abstract Brain-computer or brain-machine interface technology allows humans to control machines using their thoughts via brain signals. In particular, these interfaces can assist people with neurological diseases for speech understanding or physical disabilities for operating devices such as wheelchairs. Motor-imagery tasks play a basic role in brain-computer interfaces. This study introduces an approach for classifying motor-imagery tasks in a brain-computer interface environment, which remains a challenge for rehabilitation technology using electroencephalogram sensors. Methods used and developed for addressing the classification include wavelet time and image scattering networks, fuzzy recurrence plots, support vector machines, and classifier fusion. The rationale for combining outputs from two classifiers learning on wavelet-time and wavelet-image scattering features of brain signals, respectively, is that they are complementary and can be effectively fused using a novel fuzzy rule-based system. A large-scale challenging electroencephalogram dataset of motor imagery-based brain-computer interface was used to test the efficacy of the proposed approach. Experimental results obtained from within-session classification show the potential application of the new model that achieves an improvement of 7% in classification accuracy over the best existing classifier using state-of-the-art artificial intelligence (76% versus 69%, respectively). For the cross-session experiment, which imposes a more challenging and practical classification task, the proposed fusion model improves the accuracy by 11% (54% versus 65%). The technical novelty presented herein and its further exploration are promising for developing a reliable sensor-based intervention for assisting people with neurodisability to improve their quality of life.
    Language English
    Publishing date 2023-01-31
    Publishing country United States
    Document type Journal Article
    ZDB-ID 1166307-8
    ISSN 1558-0210 ; 1063-6528 ; 1534-4320
    ISSN (online) 1558-0210
    ISSN 1063-6528 ; 1534-4320
    DOI 10.1109/TNSRE.2023.3241241
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

    Zs.A 4726: 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)

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  8. Article ; Online: Fuzzy Recurrence Exponents of Subcellular-Nanostructure Dynamics in Time-Lapse Confocal Imaging.

    Pham, Tuan D

    IEEE transactions on nanobioscience

    2021  Volume 20, Issue 4, Page(s) 497–506

    Abstract: Studying the dynamics of nanostructures in the intracellular space is important because it allows gaining insights into the mechanism of complex biological functions of organelles. Understanding such dynamical processes can contribute to the development ... ...

    Abstract Studying the dynamics of nanostructures in the intracellular space is important because it allows gaining insights into the mechanism of complex biological functions of organelles. Understanding such dynamical processes can contribute to the development of nanomedicine for the diagnosis and treatment of many diseases caused by the interaction of multiple genes and environmental factors. Here a quantitative measure of spatial-temporal dynamics of nanostructures within a cell line in the context of nonlinear dynamics is introduced, where early endosomes, late endosomes, and lysosomes recorded by time-lapse confocal imaging are examined. The mathematical derivation of the proposed technique is based on the concept of recurrence dynamics and sequential rate of change over time. The quantification introduced as fuzzy recurrence exponents can be generalized for characterizing the dynamics of experimental evolutions in other nanostructures of living cells captured under the optical microscope.
    MeSH term(s) Endosomes ; Lysosomes ; Nanostructures ; Nonlinear Dynamics ; Time-Lapse Imaging
    Language English
    Publishing date 2021-09-30
    Publishing country United States
    Document type Journal Article
    ISSN 1558-2639
    ISSN (online) 1558-2639
    DOI 10.1109/TNB.2021.3105533
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  9. Article ; Online: Time-frequency time-space LSTM for robust classification of physiological signals.

    Pham, Tuan D

    Scientific reports

    2021  Volume 11, Issue 1, Page(s) 6936

    Abstract: Automated analysis of physiological time series is utilized for many clinical applications in medicine and life sciences. Long short-term memory (LSTM) is a deep recurrent neural network architecture used for classification of time-series data. Here time- ...

    Abstract Automated analysis of physiological time series is utilized for many clinical applications in medicine and life sciences. Long short-term memory (LSTM) is a deep recurrent neural network architecture used for classification of time-series data. Here time-frequency and time-space properties of time series are introduced as a robust tool for LSTM processing of long sequential data in physiology. Based on classification results obtained from two databases of sensor-induced physiological signals, the proposed approach has the potential for (1) achieving very high classification accuracy, (2) saving tremendous time for data learning, and (3) being cost-effective and user-comfortable for clinical trials by reducing multiple wearable sensors for data recording.
    MeSH term(s) Electrocardiography ; Gait Analysis ; Humans ; Neural Networks, Computer ; Parkinson Disease/physiopathology ; Physiology/methods ; Software
    Language English
    Publishing date 2021-03-25
    Publishing country England
    Document type Journal Article ; Validation Study
    ZDB-ID 2615211-3
    ISSN 2045-2322 ; 2045-2322
    ISSN (online) 2045-2322
    ISSN 2045-2322
    DOI 10.1038/s41598-021-86432-7
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  10. Book: Advanced computational methods dor biocomputing and bioimaging

    Pham, Tuan D.

    2007  

    Author's details Tuan D. Pham ... ed
    Keywords Computational Biology / methods ; Genomics / methods ; Gene Expression Profiling / methods ; Oligonucleotide Array Sequence Analysis
    Language English
    Size IX, 215 S. : Ill., graph. Darst.
    Publisher Nova Science Publ
    Publishing place New York
    Publishing country United States
    Document type Book
    HBZ-ID HT015262484
    ISBN 978-1-600-21278-9 ; 1-600-21278-6
    Database Catalogue ZB MED Medicine, Health

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    In stock of ZB MED Cologne/Königswinter

    Shelf markLoan statusLocation
    2007 A 6031 order for loan Magazin

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