Zobrazeno 1 - 10
of 62
pro vyhledávání: '"Ilya Muchnik"'
Autor:
Victor Tkachev, Maxim Sorokin, Artem Mescheryakov, Alexander Simonov, Andrew Garazha, Anton Buzdin, Ilya Muchnik, Nicolas Borisov
Publikováno v:
Frontiers in Genetics, Vol 9 (2019)
Here, we propose a heuristic technique of data trimming for SVM termed FLOating Window Projective Separator (FloWPS), tailored for personalized predictions based on molecular data. This procedure can operate with high throughput genetic datasets like
Externí odkaz:
https://doaj.org/article/a9f04f9cb801495fb4d75d7ba1c5f01f
Publikováno v:
Greedy Algorithms
In this article, we have investigated monotone linkage functions defined on convex geometries, antimatroids, and semilattices in general. It has been shown that the class of functions defined as minimum values of monotone linkage functions has close
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5d067d9b2c4d94374f86496549fafc29
http://www.intechopen.com/articles/show/title/quasi-concave_functions_and_greedy_algorithms
http://www.intechopen.com/articles/show/title/quasi-concave_functions_and_greedy_algorithms
Autor:
Olga Kovalchuk, Anton Buzdin, Alex Zhavoronkov, Victor Tkachev, Maria Suntsova, Ilya Muchnik, Nicolas Borisov
Publikováno v:
Cell Cycle. 17:486-491
Personalized medicine implies that distinct treatment methods are prescribed to individual patients according several features that may be obtained from, e.g., gene expression profile. The majority of machine learning methods suffer from the deficien
Publikováno v:
Journal of Petroleum Science and Engineering. 195:107916
The article presents a method of statistical modeling and forecasting of oil production rate at a real field that has been in operation for a long time. Nowadays such fields form the basis of the oil production industry. The “aging” of oil produc
Autor:
Ilya Muchnik
Publikováno v:
Braverman Readings in Machine Learning. Key Ideas from Inception to Current State ISBN: 9783319994918
Braverman Readings in Machine Learning
Braverman Readings in Machine Learning
Braverman E.M. Certain problems in the design of machines which classify objects according to an identifying feature which is not specified a priori. Automation and Remote Control 21, 971–978 (1960). Braverman, E.M. Experiments on machine learning
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::88d38870b7b0bc6d18c2acbabe3f1713
https://doi.org/10.1007/978-3-319-99492-5_17
https://doi.org/10.1007/978-3-319-99492-5_17
Publikováno v:
Braverman Readings in Machine Learning. Key Ideas from Inception to Current State ISBN: 9783319994918
Braverman Readings in Machine Learning
ResearcherID
Braverman Readings in Machine Learning
ResearcherID
The paper represents a novel approach for individual medical treatment in oncology, based on machine learning with transferring gene expression data, obtained on cell lines, onto individual cancer patients for drug efficiency prediction. We give a de
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::57f2a4335e113cee5cb78e4030ce1eb2
https://doi.org/10.1007/978-3-319-99492-5_9
https://doi.org/10.1007/978-3-319-99492-5_9
This state-of-the-art survey is dedicated to the memory of Emmanuil Markovich Braverman (1931-1977), a pioneer in developing machine learning theory.The 12 revised full papers and 4 short papers included in this volume were presented at the conferen
Publikováno v:
ICCBB
Development of individual predictors of clinical drug efficiency becomes the mainstream in modern oncology. According to this approach, for a given patient with known type of cancer and a chosen drug, we should be able to estimate the treatment effec
Publikováno v:
International Journal of Computer Mathematics. 84:1285-1297
In object recognition tasks, where images are represented as constellations of image patches, often many patches correspond to the cluttered background. In this paper, we present a two-stage method for selecting the image patches which characterize t
Publikováno v:
IEEE/ACM Transactions on Computational Biology and Bioinformatics. 4:17-27
We present a method for automatically extracting groups of orthologous genes from a large set of genomes by a new clustering algorithm on a weighted multipartite graph. The method assigns a score to an arbitrary subset of genes from multiple genomes