{"text_format":"2","workbook":"xls","n_sheets":1,"sheets":["report2"],"not_rendered":{"charts":0,"images":0,"macros":0,"external_links":1,"defined_names":1,"comments":0,"pivot_tables":0},"unopened":[],"bounds":{"document_bytes":16777216,"sheet_cells":100000,"run_cost":"one","rows_first":2000,"rows_last":500,"rows_between":2000,"halvings":0,"cell_chars":16384,"cell_head_chars":4096},"cells_cut":0,"cells_raw":0,"read":"whole","date_system":"1900","formulas":"cached_only"}

{"sheet":"report2","index":0,"n_rows":27,"n_cols":5,"rows_kept":27,"cut":false,"hidden":false,"merged":[[0,2,0,3],[0,4,0,5],[1,2,1,3],[1,4,1,5],[2,2,2,3],[2,4,2,5],[3,2,3,3],[4,2,4,3],[11,2,11,3],[12,2,12,3],[13,2,13,3],[14,2,14,3],[15,2,15,3],[17,2,17,3],[18,2,18,3],[22,2,22,3]],"flags":["formulas_cached_only"]}
[null,"1.","Record Nr.",null,"UNINA9910826809503321"]
[null,null,"Autore",null,"Zhang Yan-Qing"]
[null,null,"Titolo",null,"Machine learning in bioinformatics / / edited by Yan-Qing Zhang, Jagath C. Rajapakse"]
[null,null,"Pubbl/distr/stampa",null,"Hoboken, N.J., : Wiley, c2009"]
[null,null,"ISBN",null,"9786612030703"]
[{"$nulls":4},"9781282030701"]
[{"$nulls":4},"1282030701"]
[{"$nulls":4},"9780470397428"]
[{"$nulls":4},"047039742X"]
[{"$nulls":4},"9780470397411"]
[{"$nulls":4},"0470397411"]
[null,null,"Edizione",null,"[1st ed.]"]
[null,null,"Descrizione fisica",null,"1 online resource (476 p.)"]
[null,null,"Collana",null,"Wiley series on bioinformatics"]
[null,null,"Disciplina",null,"572.80285/61"]
[null,null,"Soggetti",null,"Bioinformatics"]
[{"$nulls":4},"Machine learning"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Note generali","Description based upon print version of record."]
[null,null,null,"Nota di bibliografia","Includes bibliographical references and index."]
[null,null,"Nota di contenuto",null,"MACHINE LEARNING IN BIOINFORMATICS; CONTENTS; Foreword; Preface; Contributors; 1 Feature Selection for Genomic and Proteomic Data Mining; 2 Comparing and Visualizing Gene Selection and Classification Methods for Microarray Data; 3 Adaptive Kernel Classifiers Via Matrix Decomposition Updating for Biological Data Analysis; 4 Bootstrapping Consistency Method for Optimal Gene Selection from Microarray Gene Expression Data for Classification Problems; 5 Fuzzy Gene Mining: A Fuzzy-Based Framework for Cancer Microarray Data Analysis; 6 Feature Selection for Ensemble Learning and Its Application"]
[{"$nulls":4},"7 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 Combination"]
[{"$nulls":4},"15 Feature Characterization and Testing of Bidirectional Promoters in "]
[{"$nulls":4},"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; Index"]
[null,null,null,"Sommario/riassunto","An 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 b"]