File:Elmap breastcancer wiki.png
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Summary
| Description |
Визуализация набора данных по экспрессии генов в раке молочной железы с использованием упругих карт (b) и метода главных компонент (c). Классы точек показаны с использованием размера (ER - статуc эстроген-рецептора), формы (GROUP - группа А - появление метастаз в течение 5 лет после лечения, группа B - нет рецидива) и цвета (TYPE - молекулярный тип опухоли). На панели (a) показана конфигурация узлов двумерной упругой карты в проекции на первые три главные компоненты. Сравнивая (b) и (c), можно заметить, что базальный тип опухоли как кластер лучше отделен на нелинейной проекции (b). Visualization of breast cancer microarray data[1] using nonlinear pricipal manifolds produced by the elastic maps algorithm[2]. Ab initio classifications are shown using points size (ER), shape (GROUP) and color (TYPE): a) configuration of nodes in the three-dimensional principal linear manifold. One clear feature is that the dataset is curved such that it can not be mapped adequately on a two-dimensional principal plane; b) the distribution of points in the internal non-linear manifold coordinates (ELMap2D) is shown together with an estimation of the two-dimensional density of points; c) the same as b), but for the linear two-dimensional PCA manifold (PCA2D). One can notice that the “basal” breast cancer subtype is visualized more adequately with ELMap2D and some features of the distribution become better resolved in comparison to PCA2D. |
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| Source | http://ru.wikipedia.org/wiki/File:Elmap_breastcancer_wiki.png | ||
| Author | self-made, Андрей Зиновьев=Andrei Zinovyev | ||
| Permission (Reusing this file) |
self-made, released by the author into Public Domain | ||
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Moved from ru.wikipedia
Licensing
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I, the copyright holder of this work, release this work into the public domain. This applies worldwide. In some countries this may not be legally possible; if so: I grant anyone the right to use this work for any purpose, without any conditions, unless such conditions are required by law. |
- ↑ Wang, Y., Klijn, J.G., Zhang, Y., Sieuwerts, A.M., Look, M.P., Yang, F., Talantov, D., Timmermans, M., Meijer-van Gelder, M.E., Yu, J. et al.: Geneexpression profiles to predict distant metastasis of lymph-node-negative primary breast cancer. Lancet 365, 671-679 (2005); Data online
- ↑ A. N. Gorban, A. Y. Zinovyev, Principal Graphs and Manifolds, In: Handbook of Research on Machine Learning Applications and Trends: Algorithms, Methods and Techniques, Olivas E.S. et al Eds. Information Science Reference, IGI Global: Hershey, PA, USA, 2009. 28-59.
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| Date/Time | Thumbnail | Dimensions | User | Comment | |
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| current | 18:13, 12 August 2010 | | 561 × 661 (133 KB) | Agor153 | {{Information |Description=Визуализация набора данных по экспрессии генов в раке молочной железы с использованием упругих карт (b) и метода главных ком� |
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