01123cam0 22002891 450 SOBE0002363020120316134902.020120316d1927 |||||ita|0103 baitaIT<<Il >>Malmantile racquistatoLorenzo Lippicolle note di vari scelte da Luigi Portirelli e con appunti biografici e critici per cura di Lodovico CorioMilanoCasa Editrice Sonzogno1927398 p.18 cmBiblioteca classica economica94001LAEC000195592001 *Biblioteca classica economica94Lippi, LorenzoSOBA00003277070393075Portirelli, LuigiSOBA00003288070Corio, LodovicoSOBA00003206070ITUNISOB20120316RICAUNISOBUNISOB000|Coll|58|K60699SOBE00023630M 102 Monografia moderna SBNM000|Coll|58|K000039SI60699acquistoIvittoriniUNISOBUNISOB20120316133909.020120321125800.0vittoriniMalmantile racquistato559737UNISOB04155nam 2200709 a 450 991082680950332120200520144314.09786612030703978128203070112820307019780470397428047039742X97804703974110470397411(CKB)1000000000719486(EBL)427638(SSID)ssj0000195100(PQKBManifestationID)11178701(PQKBTitleCode)TC0000195100(PQKBWorkID)10241797(PQKB)11282379(Au-PeEL)EBL427638(CaPaEBR)ebr10296669(CaONFJC)MIL203070(OCoLC)352745595(MiAaPQ)EBC427638(Perlego)2770724(EXLCZ)99100000000071948620080415d2009 uy 0engur|n|---|||||txtccrMachine learning in bioinformatics /edited by Yan-Qing Zhang, Jagath C. Rajapakse1st ed.Hoboken, N.J. Wileyc20091 online resource (476 p.)Wiley series on bioinformaticsDescription based upon print version of record.9780470116623 0470116625 Includes bibliographical references and index.MACHINE LEARNING IN BIOINFORMATICS; CONTENTS; Foreword; Preface; Contributors; 1 Feature Selection for Genomic and Proteomic Data Mining; 2 Comparing and Visualizing Gene Selection and Classification Methods for Microarray Data; 3 Adaptive Kernel Classifiers Via Matrix Decomposition Updating for Biological Data Analysis; 4 Bootstrapping Consistency Method for Optimal Gene Selection from Microarray Gene Expression Data for Classification Problems; 5 Fuzzy Gene Mining: A Fuzzy-Based Framework for Cancer Microarray Data Analysis; 6 Feature Selection for Ensemble Learning and Its Application7 Sequence-Based Prediction of Residue-Level Properties in Proteins8 Consensus Approaches to Protein Structure Prediction; 9 Kernel Methods in Protein Structure Prediction; 10 Evolutionary Granular Kernel Trees for Protein Subcellular Location Prediction; 11 Probabilistic Models for Long-Range Features in Biosequences; 12 Neighborhood Profile Search for Motif Refinement; 13 Markov/Neural Model for Eukaryotic Promoter Recognition; 14 Eukaryotic Promoter Detection Based on Word and Sequence Feature Selection and Combination15 Feature Characterization and Testing of Bidirectional Promoters in the Human Genome-Significance and Applications in Human Genome Research16 Supervised Learning Methods for MicroRNA Studies; 17 Machine Learning for Computational Haplotype Analysis; 18 Machine Learning Applications in SNP-Disease Association Study; 19 Nanopore Cheminformatics-Based Studies of Individual Molecular Interactions; 20 An Information Fusion Framework for Biomedical Informatics; IndexAn introduction to machine learning methods and their applications to problems in bioinformatics Machine learning techniques are increasingly being used to address problems in computational biology and bioinformatics. Novel computational techniques to analyze high throughput data in the form of sequences, gene and protein expressions, pathways, and images are becoming vital for understanding diseases and future drug discovery. Machine learning techniques such as Markov models, support vector machines, neural networks, and graphical models have been successful in analyzing life science data bWiley series on bioinformatics.BioinformaticsMachine learningBioinformatics.Machine learning.572.80285/61Zhang Yan-Qing1678833Rajapakse Jagath Chandana1678834MiAaPQMiAaPQMiAaPQBOOK9910826809503321Machine learning in bioinformatics4046726UNINA