دانلود مقاله انگلیسی رایگان:چارچوب های یادگیری ماشین و داده کاوی برای پیش بینی پاسخ به دارو در سرطان: یک مرور کلی و رمان در فرآیند غربالگری سیلیکون بر اساس قاعده قاچاق انجمن - 2019
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  • Machine learning and data mining frameworks for predicting drug response in cancer: An overview and a novel in silico screening process based on association rule mining Machine learning and data mining frameworks for predicting drug response in cancer: An overview and a novel in silico screening process based on association rule mining
    Machine learning and data mining frameworks for predicting drug response in cancer: An overview and a novel in silico screening process based on association rule mining

    سال انتشار:

    2019


    عنوان انگلیسی مقاله:

    Machine learning and data mining frameworks for predicting drug response in cancer: An overview and a novel in silico screening process based on association rule mining


    ترجمه فارسی عنوان مقاله:

    چارچوب های یادگیری ماشین و داده کاوی برای پیش بینی پاسخ به دارو در سرطان: یک مرور کلی و رمان در فرآیند غربالگری سیلیکون بر اساس قاعده قاچاق انجمن


    منبع:

    Sciencedirect - Elsevier - Pharmacology & Therapeutics 203 (2019) 107395


    نویسنده:

    Konstantinos Vougas a,b,1,2, Theodore Sakellaropoulos c,d,1, Athanassios Kotsinas b,1, George-Romanos P. Foukas b, Andreas Ntargaras b, Filippos Koinis b, Alexander Polyzos e, Vassilios Myrianthopoulos b,f, Hua Zhou g, Sonali Narang c,d, Vassilis Georgoulias h, Leonidas Alexopoulos i, Iannis Aifantis c,d, Paul A. Townsend j, Petros Sfikakis k,l, Rebecca Fitzgerald m, Dimitris Thanos a, Jiri Bartek n,o,p, Russell Petty q, Aristotelis Tsirigos c,d,g,⁎,2, Vassilis G. Gorgoulis


    چکیده انگلیسی:

    A major challenge in cancer treatment is predicting the clinical response to anti-cancer drugs on a personalized basis. The success of such a task largely depends on the ability to develop computational resources that integrate big “omic” data into effective drug-response models. Machine learning is both an expanding and an evolving computational field that holds promise to cover such needs. Here we provide a focused overview of: 1) the various supervised and unsupervised algorithms used specifically in drug response prediction applications, 2) the strategies employed to develop these algorithms into applicable models, 3) data resources that are fed into these frameworks and 4) pitfalls and challenges tomaximizemodel performance. In this contextwe also describe a novel in silico screening process, based on Association RuleMining, for identifying genes as candidate drivers of drug response and compare it with relevant data mining frameworks, for which we generated a web application freely available at: https://compbio.nyumc.org/drugs/. This pipeline explores with high efficiency large samplespaces, while is able to detect low frequency events and evaluate statistical significance even in the multidimensional space, presenting the results in the form of easily interpretable rules. We conclude with future prospects and challenges of applying machine learning based drug response prediction in precision medicine.
    Key words: Drug Response Prediction | Precision Medicine | Data mining | Machine Learning | Association Rule Mining


    سطح: متوسط
    تعداد صفحات فایل pdf انگلیسی: 31
    حجم فایل: 6735 کیلوبایت

    قیمت: رایگان


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