Statistical Modelling and Machine Learning Principles for Bioinformatics Techniques, Tools, and Applications -

Statistical Modelling and Machine Learning Principles for Bioinformatics Techniques, Tools, and Applications (eBook)

eBook Download: PDF
2020 | 1st ed. 2020
XII, 317 Seiten
Springer Singapore (Verlag)
978-981-15-2445-5 (ISBN)
Systemvoraussetzungen
181,89 inkl. MwSt
  • Download sofort lieferbar
  • Zahlungsarten anzeigen

This book discusses topics related to bioinformatics, statistics, and machine learning, presenting the latest research in various areas of bioinformatics. It also highlights the role of computing and machine learning in knowledge extraction from biological data, and how this knowledge can be applied in fields such as drug design, health supplements, gene therapy, proteomics and agriculture.

 




Siddesh G. M. is currently working as an Associate Professor at the Department of Information Science & Engineering, Ramaiah Institute of Technology, Bangalore. He received Bachelors and Masters Degrees in Computer Science and Engineering from the Visvesvaraya Technological University in 2003 and 2005, respectively, and his Ph.D. in Computer Science and Engineering from Jawaharlal Nehru Technological University, Hyderabad, in 2014. He is a member of IEEE, ISTE, and IETE. He was the recipient of Seed Money to Young Scientist for Research (SMYSR) for 2014-15 from the Government of Karnataka's Vision Group on Science and Technology (VGST). He has published numerous research papers in international journals and conferences. His research interests include distributed computing, grid/cloud computing, and IoT.   

S. R. Mani Sekhar received his M.Tech. degree from Bharathidasan University, Tiruchirappalli, and B.E. degree from Rajiv Gandhi Proudyogiki Vishwavidyalaya, Bhopal. He is currently an Assistant Professor at the Department of Information Science & Engineering, Ramaiah Institute of Technology, Bangalore. He is a member of ISTE. He has published a numerous research papers and book chapters. His research interests include data science, data analytics, and software engineering & bioinformatics. 

Srinivasa K G was awarded a Ph.D. in Computer Science and Engineering from Bangalore University in 2007. He has received various awards, including the All India Council for Technical Education - Career Award for Young Teachers; Indian Society of Technical Education - ISGITS National Award for Best Research Work Done by Young Teachers; Institution of Engineers (India) - IEI Young Engineer Award in Computer Engineering; the ISTE's Rajarambapu Patil National Award for Promising Engineering Teachers in 2012; and a Visiting Scientist Fellowship Award from IMS Singapore. He has published more than 100 research papers in international journals and conferences, and has authored three textbooks: File Structures using C++, Soft Computing for Data Mining Applications and Guide to High Performance Computing. He has also edited research books in the area of cyber-physical systems and energy-aware computing. He has been awarded a BOYSCAST Fellowship by the DST to conduct collaborative research with the Clouds Laboratory at the University of Melbourne. He is the Principal Investigator for several AICTE, UGC, DRDO, and DST funded projects. He is a senior member of IEEE and ACM. His research areas include data mining, machine learning, and cloud computing.

 


This book discusses topics related to bioinformatics, statistics, and machine learning, presenting the latest research in various areas of bioinformatics. It also highlights the role of computing and machine learning in knowledge extraction from biological data, and how this knowledge can be applied in fields such as drug design, health supplements, gene therapy, proteomics and agriculture. 
Erscheint lt. Verlag 30.1.2020
Reihe/Serie Algorithms for Intelligent Systems
Algorithms for Intelligent Systems
Zusatzinfo XII, 317 p.
Sprache englisch
Themenwelt Informatik Theorie / Studium Künstliche Intelligenz / Robotik
Naturwissenschaften Biologie Genetik / Molekularbiologie
Technik
Schlagworte Bioinformatics • data analytics • genomics • machine learning • Proteomics • Statistical Modelling • Structural Predition
ISBN-10 981-15-2445-9 / 9811524459
ISBN-13 978-981-15-2445-5 / 9789811524455
Haben Sie eine Frage zum Produkt?
PDFPDF (Wasserzeichen)
Größe: 10,6 MB

DRM: Digitales Wasserzeichen
Dieses eBook enthält ein digitales Wasser­zeichen und ist damit für Sie persona­lisiert. Bei einer missbräuch­lichen Weiter­gabe des eBooks an Dritte ist eine Rück­ver­folgung an die Quelle möglich.

Dateiformat: PDF (Portable Document Format)
Mit einem festen Seiten­layout eignet sich die PDF besonders für Fach­bücher mit Spalten, Tabellen und Abbild­ungen. Eine PDF kann auf fast allen Geräten ange­zeigt werden, ist aber für kleine Displays (Smart­phone, eReader) nur einge­schränkt geeignet.

Systemvoraussetzungen:
PC/Mac: Mit einem PC oder Mac können Sie dieses eBook lesen. Sie benötigen dafür einen PDF-Viewer - z.B. den Adobe Reader oder Adobe Digital Editions.
eReader: Dieses eBook kann mit (fast) allen eBook-Readern gelesen werden. Mit dem amazon-Kindle ist es aber nicht kompatibel.
Smartphone/Tablet: Egal ob Apple oder Android, dieses eBook können Sie lesen. Sie benötigen dafür einen PDF-Viewer - z.B. die kostenlose Adobe Digital Editions-App.

Buying eBooks from abroad
For tax law reasons we can sell eBooks just within Germany and Switzerland. Regrettably we cannot fulfill eBook-orders from other countries.

Mehr entdecken
aus dem Bereich
der Praxis-Guide für Künstliche Intelligenz in Unternehmen - Chancen …

von Thomas R. Köhler; Julia Finkeissen

eBook Download (2024)
Campus Verlag
38,99
Wie du KI richtig nutzt - schreiben, recherchieren, Bilder erstellen, …

von Rainer Hattenhauer

eBook Download (2023)
Rheinwerk Computing (Verlag)
18,68