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Author: Nataša Pržulj
Publisher: Cambridge University Press
ISBN: 1108432239
Category : Language Arts & Disciplines
Languages : en
Pages : 647
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Book Description
Introduces biological concepts and biotechnologies producing the data, graph and network theory, cluster analysis and machine learning, using real-world biological and medical examples.
Author: Nataša Pržulj
Publisher: Cambridge University Press
ISBN: 1108432239
Category : Language Arts & Disciplines
Languages : en
Pages : 647
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Book Description
Introduces biological concepts and biotechnologies producing the data, graph and network theory, cluster analysis and machine learning, using real-world biological and medical examples.
Author: José María Barreiro
Publisher: Springer
ISBN: 3540305475
Category : Medical
Languages : en
Pages : 519
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Book Description
Thisyear,the5thInternationalSymposiumonMedicalDataAnalysishasexperimented an apparently slight modi?cation. The word "biological" has been added to the title of the conferences. The motivation for this shift goes beyond the wish to attract a diff- ent kind of professional. It is linked to recent trends to produce a shift within various biomedical areas towards genomics-based research and practice. For instance, medical informaticsandbioinformaticsarebeinglinkedina synergicareadenominatedbiom- ical informatics.Similarly,patient careis beingimproved,leadingto conceptsandareas such as molecular medicine, genomic medicine or personalized healthcare. The resultsfromdifferentgenomeprojects,the advancesin systemsbiologyand the integrative approaches to physiology would not be possible without new approaches in data and information processing. Within this scenario, novel methodologies and tools will beneededtolinkclinicalandgenomicinformation,forinstance,forgeneticclinical trials, integrated data mining of genetic clinical records and clinical databases, or gene expression studies, among others. Genomic medicine presents a series of challenges that need to be addressed by researchers and practitioners. In this sense, this ISBMDA conference aimed to become a place where researchers involved in biomedical research could meet and discuss. For this conference, the classical contents of former ISMDA conferences were updated to incorporate various issues from the biological ?elds. Similarly to the incorporation of these new topics of the conference, data analysts will face, in this world of genomic medicine and related areas, signi?cant challenges in research, education and practice.
Author: Nicos Maglaveras
Publisher: Springer
ISBN: 3540680659
Category : Medical
Languages : en
Pages : 507
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Book Description
This book constitutes the refereed proceedings of the 7th International Symposium on Biological and Medical Data Analysis, ISBMDA 2006, held in Thessaloniki, Greece, December 2006. Coverage in this volume includes functional genomics, sequence analysis, biomedical models, information modeling, biomedical signal processing, biomedical image analysis, biomedical data analysis, as well as decision support systems and diagnostic tools.
Author: Michael C. Whitlock
Publisher: Macmillan Higher Education
ISBN: 1319226299
Category : Mathematics
Languages : en
Pages : 2074
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Book Description
The Analysis of Biological Data provides students with a practical foundation of statistics for biology students. Every chapter has several biological or medical examples of key concepts, and each example is prefaced by a substantial description of the biological setting. The emphasis on real and interesting examples carries into the problem sets where students have dozens of practice problems based on real data. The third edition features over 200 new examples and problems. These include new calculation practice problems, which guide the student step by step through the methods, and a greater number of examples and topics come from medical and human health research. Every chapter has been carefully edited for even greater clarity and ease of use. All the data sets, R scripts for all worked examples in the book, as well as many other teaching resources, are available to qualified instructors (see below).
Author: Victor Bloomfield
Publisher: Springer Science & Business Media
ISBN: 1441900837
Category : Science
Languages : en
Pages : 325
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Book Description
This book provides an introduction to two important aspects of modern bioch- istry, molecular biology, and biophysics: computer simulation and data analysis. My aim is to introduce the tools that will enable students to learn and use some f- damental methods to construct quantitative models of biological mechanisms, both deterministicandwithsomeelementsofrandomness;tolearnhowconceptsofpr- ability can help to understand important features of DNA sequences; and to apply a useful set of statistical methods to analysis of experimental data. The availability of very capable but inexpensive personal computers and software makes it possible to do such work at a much higher level, but in a much easier way, than ever before. TheExecutiveSummaryofthein?uential2003reportfromtheNationalAcademy of Sciences, “BIO 2010: Transforming Undergraduate Education for Future - search Biologists” [12], begins The interplay of the recombinant DNA, instrumentation, and digital revolutions has p- foundly transformed biological research. The con?uence of these three innovations has led to important discoveries, such as the mapping of the human genome. How biologists design, perform, and analyze experiments is changing swiftly. Biological concepts and models are becoming more quantitative, and biological research has become critically dependent on concepts and methods drawn from other scienti?c disciplines. The connections between the biological sciences and the physical sciences, mathematics, and computer science are rapidly becoming deeper and more extensive.
Author: Gregg Hartvigsen
Publisher: Columbia University Press
ISBN: 0231537042
Category : Education
Languages : en
Pages : 245
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Book Description
R is the most widely used open-source statistical and programming environment for the analysis and visualization of biological data. Drawing on Gregg Hartvigsen's extensive experience teaching biostatistics and modeling biological systems, this text is an engaging, practical, and lab-oriented introduction to R for students in the life sciences. Underscoring the importance of R and RStudio in organizing, computing, and visualizing biological statistics and data, Hartvigsen guides readers through the processes of entering data into R, working with data in R, and using R to visualize data using histograms, boxplots, barplots, scatterplots, and other common graph types. He covers testing data for normality, defining and identifying outliers, and working with non-normal data. Students are introduced to common one- and two-sample tests as well as one- and two-way analysis of variance (ANOVA), correlation, and linear and nonlinear regression analyses. This volume also includes a section on advanced procedures and a chapter introducing algorithms and the art of programming using R.
Author: Wang, Baoying
Publisher: IGI Global
ISBN: 1466666129
Category : Computers
Languages : en
Pages : 552
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Book Description
As technology evolves and electronic data becomes more complex, digital medical record management and analysis becomes a challenge. In order to discover patterns and make relevant predictions based on large data sets, researchers and medical professionals must find new methods to analyze and extract relevant health information. Big Data Analytics in Bioinformatics and Healthcare merges the fields of biology, technology, and medicine in order to present a comprehensive study on the emerging information processing applications necessary in the field of electronic medical record management. Complete with interdisciplinary research resources, this publication is an essential reference source for researchers, practitioners, and students interested in the fields of biological computation, database management, and health information technology, with a special focus on the methodologies and tools to manage massive and complex electronic information.
Author: Information Resources Management Association
Publisher: Medical Information Science Reference
ISBN: 9781799812043
Category :
Languages : en
Pages : 2250
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Book Description
""This book examines practical applications of healthcare analytics for improved patient care, resource allocation, and medical performance, as well as for diagnosing, predicting, and identifying at-risk populations"--
Author: Firas Kobeissy
Publisher: Academic Press
ISBN: 012809561X
Category : Medical
Languages : en
Pages : 228
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Book Description
Leveraging Biomedical and Healthcare Data: Semantics, Analytics and Knowledge provides an overview of the approaches used in semantic systems biology, introduces novel areas of its application, and describes step-wise protocols for transforming heterogeneous data into useful knowledge that can influence healthcare and biomedical research. Given the astronomical increase in the number of published reports, papers, and datasets over the last few decades, the ability to curate this data has become a new field of biomedical and healthcare research. This book discusses big data text-based mining to better understand the molecular architecture of diseases and to guide health care decision. It will be a valuable resource for bioinformaticians and members of several areas of the biomedical field who are interested in understanding more about how to process and apply great amounts of data to improve their research. Includes at each section resource pages containing a list of available curated raw and processed data that can be used by researchers in the field Provides demonstrative and relevant examples that serve as a general tutorial Presents a list of algorithm names and computational tools available for basic and clinical researchers
Author: Rabinarayan Satpathy
Publisher: John Wiley & Sons
ISBN: 1119785618
Category : Computers
Languages : en
Pages : 544
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Book Description
Machine learning techniques are increasingly being used to address problems in computational biology and bioinformatics. Novel machine learning 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. Machine Learning in Bioinformatics compiles recent approaches in machine learning methods and their applications in addressing contemporary problems in bioinformatics approximating classification and prediction of disease, feature selection, dimensionality reduction, gene selection and classification of microarray data and many more.
