Institutional Repository
Technical University of Crete
Technical University of Crete
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EL
| URI | http://purl.tuc.gr/dl/dias/C76E2EA1-D864-4740-A172-88F7AD7E1258 | - |
| Identifier | http://ebooks.iospress.nl/publication/5105 | - |
| Identifier | https://doi.org/10.3233/978-1-60750-010-0-69 | - |
| Language | en | - |
| Extent | 12 pages | en |
| Title | Pattern recognition methods for Acute Myeloid Leukemia (AML) induction-treatment analysis | en |
| Creator | Manikis Georgios | en |
| Creator | Μανικης Γεωργιος | el |
| Creator | Kounelakis Michail | en |
| Creator | Κουνελακης Μιχαηλ | el |
| Creator | Zervakis Michail | en |
| Creator | Ζερβακης Μιχαηλ | el |
| Publisher | IOS Press | en |
| Content Summary | Acute Myeloid Leukemia is a neoplastic malignancy, which originate from the myeloid line of the cells of hematopoietic system. Although it is a relatively rare cancer and despite of improvement in prognosis due to recent radio-chemotherapy regimens, it is still a severe disease and only a minority of patients are cured. In the present work we propose a methodological approach, evaluating and benchmarking different supervised learning techniques for the prediction of response to induction treatment in Acute Myeloid Leukemia (AML). Our research also focuses on the examination of the most significant indicators that contribute to the improvement of diagnosis. | en |
| Type of Item | Peer-Reviewed Journal Publication | en |
| Type of Item | Δημοσίευση σε Περιοδικό με Κριτές | el |
| License | http://creativecommons.org/licenses/by/4.0/ | en |
| Date of Item | 2018-05-17 | - |
| Date of Publication | 2009 | - |
| Subject | AML | en |
| Subject | Acute Myeloid Leukemia | en |
| Subject | Feature selection | en |
| Subject | Supervised learning | en |
| Subject | Support vector machines | en |
| Bibliographic Citation | G. C. Manikis, M. G. Kounelakis and M. E. Zervakis, "Pattern recognition methods for acute myeloid Leukemia (AML) induction-treatment analysis," Fr. Art. Int., vol. 196, no. 1, pp. 69-80, 2009. doi: 10.3233/978-1-60750-010-0-69 | en |
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