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  1. Artikel: Community detection using unsupervised machine learning techniques on COVID-19 dataset.

    Chaudhary, Laxmi / Singh, Buddha

    Social network analysis and mining

    2021  Band 11, Heft 1, Seite(n) 28

    Abstract: COVID-19 has been considered to be the most destructive pandemic ever happened in the history of mankind. The worldwide research community has put a tenacious effort to carry out research on the COVID-19 to analyse its impact on economic, medical and ... ...

    Abstract COVID-19 has been considered to be the most destructive pandemic ever happened in the history of mankind. The worldwide research community has put a tenacious effort to carry out research on the COVID-19 to analyse its impact on economic, medical and sociolgoical fields. They are trying to solve many crucial issues related to this disease and derive strategies to deal with this global pandemic. In this paper, we have analysed the trend, countries affected regionally and the variation of cases at the country level on COVID-19 dataset. We have used the Principal component analysis on the COVID-19 dataset variables to reduce the dimensionality and find the most significant variables. Further, we have unveiled the hidden community structure of countries by applying the unsupervised clustering approach, K-means. We have compared the results with the K-means method. The communities achieved after applying the PCA are more precise. The resulted communities can be beneficial to researchers, scientists, sociologists, different policy makers and managers of health sector.
    Sprache Englisch
    Erscheinungsdatum 2021-03-10
    Erscheinungsland Germany
    Dokumenttyp Journal Article ; Review
    ZDB-ID 2595306-0
    ISSN 1869-5469 ; 1869-5450
    ISSN (online) 1869-5469
    ISSN 1869-5450
    DOI 10.1007/s13278-021-00734-2
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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  2. Artikel ; Online: AE-LGBM: Sequence-based novel approach to detect interacting protein pairs via ensemble of autoencoder and LightGBM.

    Sharma, Abhibhav / Singh, Buddha

    Computers in biology and medicine

    2020  Band 125, Seite(n) 103964

    Abstract: Protein-protein interactions (PPIs) play a crucial role in biological processes of living organisms. Correct prediction of PPI can prove to be extremely valuable in probing protein functions which can aid in the development of new and powerful therapies ... ...

    Abstract Protein-protein interactions (PPIs) play a crucial role in biological processes of living organisms. Correct prediction of PPI can prove to be extremely valuable in probing protein functions which can aid in the development of new and powerful therapies for disease prevention. Many experimental studies have been previously performed to investigate PPIs. However, in-vitro techniques to investigate PPIs are resource-extensive and time-consuming. Although various in-silico approaches to predict PPI have been developed in recent years, they could be fallible in terms of accuracy and false-positive rate. To overcome these shortcomings, we propose a novel approach, AE-LGBM to predict the PPIs more accurately. It employs LightGBM classifier and utilizes the Autoencoder, which is an artificial neural network, to efficiently produce lower-dimensional, discriminative, and noise-free features. We incorporate conjoint triad (CT) and Composition-Transition-Distribution (CTD) features into the AE-LGBM framework. On performing ten-fold cross-validation, the prediction accuracies obtained by AE-LGBM for Human and Yeast datasets are 98.7% and 95.4% respectively. AE-LGBM was further evaluated on independent datasets and has achieved excellent accuracies of 100%, 100%, 99.9%, 99.3%, 99.2% on E. coli, M. musculus, C. elegans, H. pylori and H. sapiens respectively. AE-LGBM has also obtained the best accuracy when tested over three important PPI networks namely single-core network (CD9), the multiple-core network (The Ras/Raf/MEK/ERK pathway) and the cross-connection network (Wnt Network). The outstanding generalization ability of AE-LGBM makes it a versatile, efficient, and robust PPIs prediction model.
    Mesh-Begriff(e) Animals ; Caenorhabditis elegans ; Computational Biology ; Escherichia coli ; Humans ; Neural Networks, Computer ; Protein Interaction Mapping ; Saccharomyces cerevisiae
    Sprache Englisch
    Erscheinungsdatum 2020-08-19
    Erscheinungsland United States
    Dokumenttyp Journal Article
    ZDB-ID 127557-4
    ISSN 1879-0534 ; 0010-4825
    ISSN (online) 1879-0534
    ISSN 0010-4825
    DOI 10.1016/j.compbiomed.2020.103964
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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