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Introduction to Protein Structure Prediction(1st Edition)
Methods and Algorithms (Wiley Series in Bioinformatics)
by Huzefa Rangwala, George Karypis, Jane Doe
Hardcover, 516 Pages, Published 2010 by Wiley
ISBN-13: 978-0-470-47059-6, ISBN: 0-470-47059-3

Wiley Series in Bioinformatics Ser.
Introduction to Protein Structure Prediction : Methods and Algorithms
by Huzefa Rangwala, Editor: George Karypis
500 Pages, Published 2011 by John Wiley & Sons
ISBN-13: 978-1-118-09946-9, ISBN: 1-118-09946-X

"J. Wang, H. Lee, and S. Ahmad. Prediction and evolutionary information analysis of protein solvent accessibility using multiple linear regression. Proteins, 61:481– 491, 2005. 208. Z. Xu, C. Zhang, S. Liu, and Y. Zhou. QBES: Predicting real values of solvent accessibility from sequences by efficient, constrained energy optimization. Proteins, 63:961–966, 2006. 209. H. Naderi-Manesh, M. Sadeghi, S . Arab, and A.A.M. Movahedi. Predict ..."

Introduction to Parallel Computing(2nd Edition)
by Ananth Grama, Vipin Kumar, Anshul Gupta, George Karypis, Beckles Willson
Hardcover, 656 Pages, Published 2003 by Pearson
ISBN-13: 978-0-201-64865-2, ISBN: 0-201-64865-2

Wiley Series on Bioinformatics Ser.
Introduction to Protein Structure Prediction
by George Karypis, Huzefa Rangwala
Published 2010
ISBN-13: 978-1-282-78286-0, ISBN: 1-282-78286-X

Science and Technology Text Mining
Electric Power Sources
by Ronald N. Kostoff, Rene Tshiteya, Kirstin M. Pfeil, James A. Humenik, George Karypis
Spiral, 80 Pages, Published 2004 by Storming Media
ISBN-13: 978-1-4235-1576-0, ISBN: 1-4235-1576-5

"This is a OFFICE OF NAVAL RESEARCH ARLINGTON VA report procured by the Pentagon and made available for public release. It has been reproduced in the best form available to the Pentagon. It is not spiral-bound, but rather assembled with Velobinding in a soft, white linen cover. The Storming Media report number is A987124. The abstract provided by the Pentagon follows: Database Tomography (DT) is a textual database analysis system consist ..."

Macromolecule Mass Spectrometry
Citation Mining of User Documents
by Ronald N. Kostoff, Hector D. Cortes, Clifford D. Bedford, George Karypis
Spiral, 90 Pages, Published 2003 by Storming Media
ISBN-13: 978-1-4235-0000-1, ISBN: 1-4235-0000-8

"Identifying research users, applications, and impact is important for research performers, managers, evaluators, and sponsors."

"Introduction to Parallel Computing" with "Introduction to RISC Assembly Language Programming"
by Ananth Grama, George Karypis, Vipin Kumar, Anshul Gupta, John Waldron
Hardcover, 4 Pages, Published 2003 by Addison Wesley
ISBN-13: 978-0-582-83242-8, ISBN: 0-582-83242-X

"This Multi Pack is made up of the following components; Grama/ Introduction to Parallel Computing 0201648652 Waldron/ Introduction to RISC Assembly Language Programming 0201398281"

Advanced Data Mining and Applications
8th International Conference, ADMA 2012, Nanjing, China, December 15-18, 2012, Proceedings (Lecture Notes in ... / Lecture Notes in Artificial Intelligence)
by Shuigeng Zhou, Songmao Zhang, George Karypis
Paperback, 816 Pages, Published 2012 by Springer
ISBN-13: 978-3-642-35526-4, ISBN: 3-642-35526-9

Introduction to Parallel Computing(2nd Edition)
by Ananth Grama, Anshul Gupta, George Karypis, Vipin Kumar
Paperback, 656 Pages, Published 2007 by Pearson India
ISBN-13: 978-81-317-0807-1, ISBN: 81-317-0807-1

Protein Structure Methods and Algorithms
by Huzefa Rangwala, George Karypis
Digital, 533 Pages, Published 2010 by John Wiley & Sons
ISBN-13: , ISBN:

Lecture Notes in Computer Science
Advanced Data Mining and Applications : 8th International Conference, ADMA 2012, Nanjing, China, December 15-18, 2012, Proceedings 7713
by Shuigeng Zhou, Songmao Zhang, George Karypis
795 Pages, Published 2012 by Springer Science & Business Media
ISBN-13: 978-3-642-35527-1, ISBN: 3-642-35527-7

"Therefore, in each learning iteration of feedback, the confidence of unlabeled data point xu can be evaluated using a criterion as: Exu = ∑ ( (yi−M(xi))2− (yi−M( xi))2 ) (18) xi∈XL here, M is the original semi-supervised regressor trained by the labeled dataset (X L ,yL) and unlabeled dataset XU, while M is the one re-trained by the new labeled dataset {(XL,yL) ∪ (xu,ˆyu} and unlabeled dataset {XU − xu}. Here xu is an unlabeled data ..."

Books by George Karypis