data mining for genomics and proteomics analysis of gene and protein expression data wiley series on methods and applications in data mining

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Data Mining For Genomics And Proteomics

Author : Darius M. Dziuda
ISBN : 0470593407
Genre : Computers
File Size : 53. 85 MB
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Data Mining for Genomics and Proteomics uses pragmatic examples and a complete case study to demonstrate step-by-step how biomedical studies can be used to maximize the chance of extracting new and useful biomedical knowledge from data. It is an excellent resource for students and professionals involved with gene or protein expression data in a variety of settings.

Data Mining And Predictive Analytics

Author : Daniel T. Larose
ISBN : 9781118868706
Genre : Computers
File Size : 76. 15 MB
Format : PDF, ePub
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Learn methods of data analysis and their application to real-world data sets This updated second edition serves as an introduction to data mining methods and models, including association rules, clustering, neural networks, logistic regression, and multivariate analysis. The authors apply a unified “white box” approach to data mining methods and models. This approach is designed to walk readers through the operations and nuances of the various methods, using small data sets, so readers can gain an insight into the inner workings of the method under review. Chapters provide readers with hands-on analysis problems, representing an opportunity for readers to apply their newly-acquired data mining expertise to solving real problems using large, real-world data sets. Data Mining and Predictive Analytics: Offers comprehensive coverage of association rules, clustering, neural networks, logistic regression, multivariate analysis, and R statistical programming language Features over 750 chapter exercises, allowing readers to assess their understanding of the new material Provides a detailed case study that brings together the lessons learned in the book Includes access to the companion website, www.dataminingconsultant, with exclusive password-protected instructor content Data Mining and Predictive Analytics will appeal to computer science and statistic students, as well as students in MBA programs, and chief executives.

Data Mining And Learning Analytics

Author : Samira ElAtia
ISBN : 9781118998212
Genre : Computers
File Size : 56. 64 MB
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Addresses the impacts of data mining on education and reviews applications in educational research teaching, and learning This book discusses the insights, challenges, issues, expectations, and practical implementation of data mining (DM) within educational mandates. Initial series of chapters offer a general overview of DM, Learning Analytics (LA), and data collection models in the context of educational research, while also defining and discussing data mining’s four guiding principles— prediction, clustering, rule association, and outlier detection. The next series of chapters showcase the pedagogical applications of Educational Data Mining (EDM) and feature case studies drawn from Business, Humanities, Health Sciences, Linguistics, and Physical Sciences education that serve to highlight the successes and some of the limitations of data mining research applications in educational settings. The remaining chapters focus exclusively on EDM’s emerging role in helping to advance educational research—from identifying at-risk students and closing socioeconomic gaps in achievement to aiding in teacher evaluation and facilitating peer conferencing. This book features contributions from international experts in a variety of fields. Includes case studies where data mining techniques have been effectively applied to advance teaching and learning Addresses applications of data mining in educational research, including: social networking and education; policy and legislation in the classroom; and identification of at-risk students Explores Massive Open Online Courses (MOOCs) to study the effectiveness of online networks in promoting learning and understanding the communication patterns among users and students Features supplementary resources including a primer on foundational aspects of educational mining and learning analytics Data Mining and Learning Analytics: Applications in Educational Research is written for both scientists in EDM and educators interested in using and integrating DM and LA to improve education and advance educational research.

Knowledge Discovery In Bioinformatics

Author : Xiaohua Hu
ISBN : 0470124636
Genre : Technology & Engineering
File Size : 59. 30 MB
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The purpose of this edited book is to bring together the ideas and findings of data mining researchers and bioinformaticians by discussing cutting-edge research topics such as, gene expressions, protein/RNA structure prediction, phylogenetics, sequence and structural motifs, genomics and proteomics, gene findings, drug design, RNAi and microRNA analysis, text mining in bioinformatics, modelling of biochemical pathways, biomedical ontologies, system biology and pathways, and biological database management.

Microbial Functional Genomics

Author : Jizhong Zhou
ISBN : 0471071900
Genre : Medical
File Size : 56. 1 MB
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Genomics: toward a genome-level understanding of the structure, functions, and evolution of bioloical systems; Microbial diversity and genomics. Computational genome annotation. Microbial evolution from a genomics perspective. Computational methods for functional prediction of genes. DNA microarray technology. Microarray gene expression data analysis. Mutagenesis as a genomic tool for studying gene function. Mass spectrometry. Identification of protein-ligand interactions. The functional genomics of model organisms: addressing old questions from a new perspective. Functional genomic analysis of bacterial pathogens and environmentally significant microorganisms. The impact of genomics on antimicrobial drug discovery and toxicology. Application of microarray-based genomic technology to mutation analysis and microbial detection. Future perspectives: genomics beyond single cells.

Machine Learning In Bioinformatics

Author : Yanqing Zhang
ISBN : 0470397411
Genre : Computers
File Size : 54. 70 MB
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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 because of their capabilities in handling randomness and uncertainty of data noise and in generalization. From an internationally recognized panel of prominent researchers in the field, Machine Learning in Bioinformatics compiles recent approaches in machine learning methods and their applications in addressing contemporary problems in bioinformatics. Coverage includes: feature selection for genomic and proteomic data mining; comparing variable selection methods in gene selection and classification of microarray data; fuzzy gene mining; sequence-based prediction of residue-level properties in proteins; probabilistic methods for long-range features in biosequences; and much more. Machine Learning in Bioinformatics is an indispensable resource for computer scientists, engineers, biologists, mathematicians, researchers, clinicians, physicians, and medical informaticists. It is also a valuable reference text for computer science, engineering, and biology courses at the upper undergraduate and graduate levels.

Data Analysis And Visualization In Genomics And Proteomics

Author : Francisco Azuaje
ISBN : 9780470094402
Genre : Science
File Size : 36. 53 MB
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Data Analysis and Visualization in Genomics and Proteomics is the first book addressing integrative data analysis and visualization in this field. It addresses important techniques for the interpretation of data originating from multiple sources, encoded in different formats or protocols, and processed by multiple systems. One of the first systematic overviews of the problem of biological data integration using computational approaches This book provides scientists and students with the basis for the development and application of integrative computational methods to analyse biological data on a systemic scale Places emphasis on the processing of multiple data and knowledge resources, and the combination of different models and systems

Integration Of Omics Approaches And Systems Biology For Clinical Applications

Author : Antonia Vlahou
ISBN : 9781119181149
Genre : Science
File Size : 53. 15 MB
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Introduces readers to the state of the art of omics platforms and all aspects of omics approaches for clinical applications This book presents different high throughput omics platforms used to analyze tissue, plasma, and urine. The reader is introduced to state of the art analytical approaches (sample preparation and instrumentation) related to proteomics, peptidomics, transcriptomics, and metabolomics. In addition, the book highlights innovative approaches using bioinformatics, urine miRNAs, and MALDI tissue imaging in the context of clinical applications. Particular emphasis is put on integration of data generated from these different platforms in order to uncover the molecular landscape of diseases. The relevance of each approach to the clinical setting is explained and future applications for patient monitoring or treatment are discussed. Integration of omics Approaches and Systems Biology for Clinical Applications presents an overview of state of the art omics techniques. These methods are employed in order to obtain the comprehensive molecular profile of biological specimens. In addition, computational tools are used for organizing and integrating these multi-source data towards developing molecular models that reflect the pathophysiology of diseases. Investigation of chronic kidney disease (CKD) and bladder cancer are used as test cases. These represent multi-factorial, highly heterogeneous diseases, and are among the most significant health issues in developed countries with a rapidly aging population. The book presents novel insights on CKD and bladder cancer obtained by omics data integration as an example of the application of systems biology in the clinical setting. Describes a range of state of the art omics analytical platforms Covers all aspects of the systems biology approach—from sample preparation to data integration and bioinformatics analysis Contains specific examples of omics methods applied in the investigation of human diseases (Chronic Kidney Disease, Bladder Cancer) Integration of omics Approaches and Systems Biology for Clinical Applications will appeal to a wide spectrum of scientists including biologists, biotechnologists, biochemists, biophysicists, and bioinformaticians working on the different molecular platforms. It is also an excellent text for students interested in these fields.

The Urinary Proteome

Author : Alex J. Rai
ISBN : STANFORD:36105211740936
Genre : Science
File Size : 45. 32 MB
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This book is intended for scientifc researchers, clinical laboratorians, clinical and translational scientists, and others interested in proteomics and biomarker discovery. Urine is one of the most easily accessible biological samples, and it provides a treasure trove of molecules important in clinical diagnostics. In this book, we review briefly the classical urine tests that are performed in the clinical laboratory and then delve into the state-of-the-art methods for proteomic analysis using urine specimens. The most recent advances are discussed with regard to sample preparation, data analysis, and fnally methods and applications. A multitude of examples are provided including procedural details for the identifcation and characterization of urine biomarkers that hold potential for the diagnosis and treatment of many different disease conditions. The text is arranged so as to read systematically: introduction, sample preparation methods, applications, and data analysis. However, it does not necessarily require the reader to read it from start to fnish. Each chapter is organized such that it can be read individually without requiring knowledge from other chapters. I would like to thank the many individuals who made this book possible. These include the many authors who contributed to each of the individual chapters, the corresponding authors who took responsibility in providing the complete and fnished versions solicited for the peer review process, and the many scientifc reviewers who provided their valuable input and guidance.

Bioinformatics And Biomarker Discovery

Author : Francisco Azuaje
ISBN : 9781119964308
Genre : Computers
File Size : 27. 94 MB
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This book is designed to introduce biologists, clinicians and computational researchers to fundamental data analysis principles, techniques and tools for supporting the discovery of biomarkers and the implementation of diagnostic/prognostic systems. The focus of the book is on how fundamental statistical and data mining approaches can support biomarker discovery and evaluation, emphasising applications based on different types of "omic" data. The book also discusses design factors, requirements and techniques for disease screening, diagnostic and prognostic applications. Readers are provided with the knowledge needed to assess the requirements, computational approaches and outputs in disease biomarker research. Commentaries from guest experts are also included, containing detailed discussions of methodologies and applications based on specific types of "omic" data, as well as their integration. Covers the main range of data sources currently used for biomarker discovery Covers the main range of data sources currently used for biomarker discovery Puts emphasis on concepts, design principles and methodologies that can be extended or tailored to more specific applications Offers principles and methods for assessing the bioinformatic/biostatistic limitations, strengths and challenges in biomarker discovery studies Discusses systems biology approaches and applications Includes expert chapter commentaries to further discuss relevance of techniques, summarize biological/clinical implications and provide alternative interpretations

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