Life Science Data Mining PDF Download
Are you looking for read ebook online? Search for your book and save it on your Kindle device, PC, phones or tablets. Download Life Science Data Mining PDF full book. Access full book title Life Science Data Mining.
Author | : Jens Dörpinghaus |
Publisher | : Springer Nature |
Total Pages | : 593 |
Release | : 2023-03-04 |
Genre | : Computers |
ISBN | : 303108411X |
Download Computational Life Sciences Book in PDF, ePub and Kindle
This book broadly covers the given spectrum of disciplines in Computational Life Sciences, transforming it into a strong helping hand for teachers, students, practitioners and researchers. In Life Sciences, problem-solving and data analysis often depend on biological expertise combined with technical skills in order to generate, manage and efficiently analyse big data. These technical skills can easily be enhanced by good theoretical foundations, developed from well-chosen practical examples and inspiring new strategies. This is the innovative approach of Computational Life Sciences-Data Engineering and Data Mining for Life Sciences: We present basic concepts, advanced topics and emerging technologies, introduce algorithm design and programming principles, address data mining and knowledge discovery as well as applications arising from real projects. Chapters are largely independent and often flanked by illustrative examples and practical advise.
Author | : Chung-sheng Li |
Publisher | : World Scientific |
Total Pages | : 390 |
Release | : 2006-12-29 |
Genre | : Science |
ISBN | : 981447682X |
Download Life Science Data Mining Book in PDF, ePub and Kindle
This timely book identifies and highlights the latest data mining paradigms to analyze, combine, integrate, model and simulate vast amounts of heterogeneous multi-modal, multi-scale data for emerging real-world applications in life science.The cutting-edge topics presented include bio-surveillance, disease outbreak detection, high throughput bioimaging, drug screening, predictive toxicology, biosensors, and the integration of macro-scale bio-surveillance and environmental data with micro-scale biological data for personalized medicine. This collection of works from leading researchers in the field offers readers an exceptional start in these areas.
Author | : Oliviero Carugo |
Publisher | : Humana |
Total Pages | : 407 |
Release | : 2016-08-23 |
Genre | : Science |
ISBN | : 9781493956883 |
Download Data Mining Techniques for the Life Sciences Book in PDF, ePub and Kindle
Most life science researchers will agree that biology is not a truly theoretical branch of science. The hype around computational biology and bioinformatics beginning in the nineties of the 20th century was to be short lived (1, 2). When almost no value of practical importance such as the optimal dose of a drug or the three-dimensional structure of an orphan protein can be computed from fundamental principles, it is still more straightforward to determine them experimentally. Thus, experiments and observationsdogeneratetheoverwhelmingpartofinsightsintobiologyandmedicine. The extrapolation depth and the prediction power of the theoretical argument in life sciences still have a long way to go. Yet, two trends have qualitatively changed the way how biological research is done today. The number of researchers has dramatically grown and they, armed with the same protocols, have produced lots of similarly structured data. Finally, high-throu- put technologies such as DNA sequencing or array-based expression profiling have been around for just a decade. Nevertheless, with their high level of uniform data generation, they reach the threshold of totally describing a living organism at the biomolecular level for the first time in human history. Whereas getting exact data about living systems and the sophistication of experimental procedures have primarily absorbed the minds of researchers previously, the weight increasingly shifts to the problem of interpreting accumulated data in terms of biological function and bio- lecular mechanisms.
Author | : Oliviero Carugo |
Publisher | : Humana Press |
Total Pages | : 408 |
Release | : 2011-03-04 |
Genre | : Science |
ISBN | : 9781607614869 |
Download Data Mining Techniques for the Life Sciences Book in PDF, ePub and Kindle
Most life science researchers will agree that biology is not a truly theoretical branch of science. The hype around computational biology and bioinformatics beginning in the nineties of the 20th century was to be short lived (1, 2). When almost no value of practical importance such as the optimal dose of a drug or the three-dimensional structure of an orphan protein can be computed from fundamental principles, it is still more straightforward to determine them experimentally. Thus, experiments and observationsdogeneratetheoverwhelmingpartofinsightsintobiologyandmedicine. The extrapolation depth and the prediction power of the theoretical argument in life sciences still have a long way to go. Yet, two trends have qualitatively changed the way how biological research is done today. The number of researchers has dramatically grown and they, armed with the same protocols, have produced lots of similarly structured data. Finally, high-throu- put technologies such as DNA sequencing or array-based expression profiling have been around for just a decade. Nevertheless, with their high level of uniform data generation, they reach the threshold of totally describing a living organism at the biomolecular level for the first time in human history. Whereas getting exact data about living systems and the sophistication of experimental procedures have primarily absorbed the minds of researchers previously, the weight increasingly shifts to the problem of interpreting accumulated data in terms of biological function and bio- lecular mechanisms.
Author | : Rob Sullivan |
Publisher | : Springer Science & Business Media |
Total Pages | : 644 |
Release | : 2012-01-07 |
Genre | : Science |
ISBN | : 1597452904 |
Download Introduction to Data Mining for the Life Sciences Book in PDF, ePub and Kindle
Data mining provides a set of new techniques to integrate, synthesize, and analyze tdata, uncovering the hidden patterns that exist within. Traditionally, techniques such as kernel learning methods, pattern recognition, and data mining, have been the domain of researchers in areas such as artificial intelligence, but leveraging these tools, techniques, and concepts against your data asset to identify problems early, understand interactions that exist and highlight previously unrealized relationships through the combination of these different disciplines can provide significant value for the investigator and her organization.
Author | : Stephen T. C. Wong |
Publisher | : World Scientific Publishing Company |
Total Pages | : 392 |
Release | : 2006 |
Genre | : Computers |
ISBN | : |
Download Life Science Data Mining Book in PDF, ePub and Kindle
This timely book identifies and highlights the latest data mining paradigms to analyze, combine, integrate, model and simulate vast amounts of heterogeneous multi-modal, multi-scale data for emerging real-world applications in life science.The cutting-edge topics presented include bio-surveillance, disease outbreak detection, high throughput bioimaging, drug screening, predictive toxicology, biosensors, and the integration of macro-scale bio-surveillance and environmental data with micro-scale biological data for personalized medicine. This collection of works from leading researchers in the field offers readers an exceptional start in these areas.
Author | : Xiaoli Li |
Publisher | : World Scientific |
Total Pages | : 437 |
Release | : 2013-11-28 |
Genre | : Science |
ISBN | : 9814551023 |
Download Biological Data Mining And Its Applications In Healthcare Book in PDF, ePub and Kindle
Biologists are stepping up their efforts in understanding the biological processes that underlie disease pathways in the clinical contexts. This has resulted in a flood of biological and clinical data from genomic and protein sequences, DNA microarrays, protein interactions, biomedical images, to disease pathways and electronic health records. To exploit these data for discovering new knowledge that can be translated into clinical applications, there are fundamental data analysis difficulties that have to be overcome. Practical issues such as handling noisy and incomplete data, processing compute-intensive tasks, and integrating various data sources, are new challenges faced by biologists in the post-genome era. This book will cover the fundamentals of state-of-the-art data mining techniques which have been designed to handle such challenging data analysis problems, and demonstrate with real applications how biologists and clinical scientists can employ data mining to enable them to make meaningful observations and discoveries from a wide array of heterogeneous data from molecular biology to pharmaceutical and clinical domains.
Author | : |
Publisher | : |
Total Pages | : 656 |
Release | : 2012-01-01 |
Genre | : |
ISBN | : 9781617795251 |
Download Introduction to Data Mining for the Life Sciences Book in PDF, ePub and Kindle
Author | : Rafael A. Irizarry |
Publisher | : CRC Press |
Total Pages | : 537 |
Release | : 2016-10-04 |
Genre | : Mathematics |
ISBN | : 1498775861 |
Download Data Analysis for the Life Sciences with R Book in PDF, ePub and Kindle
This book covers several of the statistical concepts and data analytic skills needed to succeed in data-driven life science research. The authors proceed from relatively basic concepts related to computed p-values to advanced topics related to analyzing highthroughput data. They include the R code that performs this analysis and connect the lines of code to the statistical and mathematical concepts explained.
Author | : Li, Xiao-Li |
Publisher | : IGI Global |
Total Pages | : 450 |
Release | : 2009-05-31 |
Genre | : Technology & Engineering |
ISBN | : 1605663999 |
Download Biological Data Mining in Protein Interaction Networks Book in PDF, ePub and Kindle
"The goal of this book is to disseminate research results and best practices from cross-disciplinary researchers and practitioners interested in, and working on bioinformatics, data mining, and proteomics"--Provided by publisher.