KNIME platformasida hisob yaratish va dasturiy muhitni sozlash
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KNIME Analytics Platform##common.commaListSeparator## ma'lumotlar tahlili##common.commaListSeparator## mashina o‘rganishi##common.commaListSeparator## workflow##common.commaListSeparator## vizual dasturlash##common.commaListSeparator## ochiq kodli platforma##common.commaListSeparator## KNIME Hub##common.commaListSeparator## Python integratsiyasiAnnotatsiya
Ushbu maqolada KNIME Analytics Platform - ochiq kodli ma'lumotlar tahlili va mashina o‘rganish platformasida foydalanuvchi hisobini yaratish va dasturiy muhitni sozlash jarayoni batafsil ko‘rib chiqilgan. Maqolada KNIME platformasining asosiy xususiyatlari, tizim talablari, o‘rnatish bosqichlari, KNIME Hub da ro‘yxatdan o‘tish, dasturiy muhit interfeysi bilan tanishish va muhim sozlamalarni amalga oshirish jarayonlari keltirilgan. Shuningdek, Python va R tillarini integratsiya qilish, kengaytmalar o‘rnatish va birinchi workflow yaratish bo‘yicha amaliy ko‘rsatmalar berilgan
##submission.citations##
1. Berthold, M. R., Cebron, N., Dill, F., Gabriel, T. R., Kötter, T., Meinl, T., ... & Wiswedel, B. (2009). KNIME - the Konstanz information miner: version 2.0 and beyond. ACM SIGKDD explorations Newsletter, 11(1), 26-31.
2. Fillbrunn, A., Dietz, C., Pfeuffer, J., Rahn, R., Landrum, G. A., & Berthold, M. R. (2017). KNIME for reproducible cross-domain analysis of life science data. Journal of biotechnology, 261, 149-156.
3. Mazanetz, M. P., Marmon, R. J., Reisser, C. B., & Morao, I. (2012). Drug discovery applications for KNIME: an open source data mining platform. Current topics in medicinal chemistry, 12(18), 1965-1979.
4. Warr, W. A. (2012). Scientific workflow systems: Pipeline Pilot and KNIME. Journal of computer-aided molecular design, 26(7), 801-804.
5. KNIME AG. (2024). KNIME Analytics Platform: User Guide. Retrieved from https://docs.knime.com/
6. Jagadish, H. V., Gehrke, J., Labrinidis, A., Papakonstantinou, Y., Patel, J. M., Ramakrishnan, R., & Shahabi, C. (2014). Big data and its technical challenges. Communications of the ACM, 57(7), 86-94.
7. Beisken, S., Eiden, M., & Salek, R. M. (2015). Getting the right answers: understanding metabolomics challenges. Expert review of molecular diagnostics, 15(1), 97-109.
8. Dietz, C., & Berthold, M. R. (2016). KNIME for Open-Source Bioimage Analysis: A Tutorial. In Focus on Bio-Image Informatics (pp. 179-197). Springer, Cham.
9. Amat, F., Höckendorf, B., Wan, Y., Lemon, W. C., McDole, K., & Keller, P. J. (2015). Efficient processing and analysis of large-scale light-sheet microscopy data. Nature protocols, 10(11), 1679-1696.
10. Wickham, H., & Grolemund, G. (2016). R for data science: import, tidy, transform, visualize, and model data. O‘Reilly Media, Inc.
11. McInnes, L., Healy, J., & Melville, J. (2018). UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction. arXiv preprint arXiv:1802.03426.
12. Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., ... & Duchesnay, É. (2011). Scikit-learn: Machine learning in Python. Journal of machine learning research, 12, 2825-2830
