01040cam0 2200265 450 E60020002772220210514072111.020070619d1969 |||||ita|0103 baitaIT<<Il >>bilancio dell'impresa e le sue differenziazioni e la sua interpretazioneNapoleone RossiTorinoUtet1969XIV, 312 p. ;Università degli Studi di PaviaIstituto di Economia aziendale2001LAEC000236692001 *Università degli Studi di Pavia : Istituto di Economia aziendale2Rossi, NapoleoneA600200042279070108327ITUNISOB20210514RICAUNISOBUNISOB330122807E600200027722M 102 Monografia moderna SBNM330001600Si122807donopomicinoUNISOBUNISOB20070619075905.020210514072100.0AlfanoBilancio dell'impresa e le sue differenziazioni e la sua interpretazione1686071UNISOB04014nam 2200637 a 450 991014595700332120230721005143.01-282-03070-197866120307030-470-39742-X0-470-39741-1(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(EXLCZ)99100000000071948620080415d2009 uy 0engur|n|---|||||txtccrMachine learning in bioinformatics[electronic resource] /edited by Yan-Qing Zhang, Jagath C. RajapakseHoboken, N.J. Wileyc20091 online resource (476 p.)Wiley series on bioinformaticsDescription based upon print version of record.0-470-11662-5 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-Qing902087Rajapakse Jagath Chandana961203MiAaPQMiAaPQMiAaPQBOOK9910145957003321Machine learning in bioinformatics2179119UNINA