Deep Blue: Browsing Interdisciplinary and Peer-Reviewed by Title

M x –O y –Si z bonding models for silica-supported Ziegler–Natta catalysts

Liu, Jia-Chu (John Wiley & Sons, Ltd., 1999-04)

(93.5KB)

M-flation: inflation from matrix valued scalar fields

Ashoorioon, Amjad (IOP Publishing, 2009)

(466.6KB)

M-theory on special holonomy spaces

Cvetič, Mirjam; Gibbons, G. W.; Lü, Hong; Pope, Christopher N. (The American Institute of Physics, 2002-01-23)

(1.4MB)

M. Breitenbach, R. Crameri, and S. B. Lehrer. Fungal Allergy and Pathogenicity .

Hogaboam, Cory M. (Kluwer Academic Publishers; Springer Science+Business Media, 2004-01)

(26.2KB)

M. Brewster Smith. For a Significant Social Psychology: The Collected Writings of M. Brewster Smith . New York: New York University Press, 2003. 306 pp. $65.00 (cloth). ISBN 0-8147-9822-5. $22.00 (paper). ISBN 0-8147-9823-3

Kahn, Robert L. (Wiley Subscription Services, Inc., A Wiley Company, 2008)

(261.6KB)

M. Michael Cohen, Jr.: Author, diagnostician, geneticist, teacher, mentor, syndrome scholar extraordinaire (1937–2018)

Carey, John C.; Hennekam, Raoul C. M.; Lin, Angela E.; Barr, Mason (Oxford University PressWiley Periodicals, Inc., 2018-08)

(3MB)

(185.3KB)

M2-M5 systems in calN = 6 Chern-Simons theory

Hanaki, Kentaro; Lin, Hai (2008)

(200.3KB)

Maastricht Type 2 Donors: Unrealized Opportunities

Punch, Jeffery D.; Anderson, C. D. (Blackwell Publishing IncWiley Periodicals, Inc., 2012-01)

(428.2KB)

MACE II - A Space Shuttle experiment for investigating adaptive control of flexible spacecraft

Denoyer, Keith K.; Hyland, David C.; Davis, Lawrence D.; Miller, David W. (American Institute of Aeronautics and Astronautics, 1998)

(637.3KB)

MACF1 gene structure: a hybrid of plectin and dystrophin

Gong, Tzy-Wen L.; Besirli, Cagri G.; Lomax, Margaret I. (Springer-Verlag; Springer-Verlag New York Inc., 2001-11)

(1MB)

Mach number effects on entrainment and mixing in supersonic planar turbulent wakes

Nakagawa, Masaki; Dahm, Werner J. A. (American Institute of Aeronautics and Astronautics, 2000)

(1.7MB)

Mach's principle, mass, and the fine structure constant

Tomozawa, Yukio (Kluwer Academic Publishers-Plenum Publishers; Plenum Publishing Corporation ; Springer Science+Business Media, 1972-01)

(325.8KB)

MaCH: using sequence and genotype data to estimate haplotypes and unobserved genotypes

Li, Yun; Willer, Cristen J.; Ding, Jun; Scheet, Paul; Abecasis, Gonçalo R. (Wiley Subscription Services, Inc., A Wiley Company, 2010-12)

(158.6KB)

Machine and deep learning methods for radiomics

Avanzo, Michele; Wei, Lise; Stancanello, Joseph; Valli�res, Martin; Rao, Arvind; Morin, Olivier; Mattonen, Sarah A.; El Naqa, Issam (Wiley Periodicals, Inc.Springer International Publishing, 2020-06)

(475.9KB)

(814.1KB)

Machine intelligence and related topics: An information scientist's weekend book : Donald Michie, (Gordon and Breach, New York, 1982); 328 pages, $57.75

Gordon, Michael D. (Elsevier, 1987-03)

(51.6KB)

Machine learning algorithms for outcome prediction in (chemo)radiotherapy: An empirical comparison of classifiers

Deist, Timo M.; Dankers, Frank J. W. M.; Valdes, Gilmer; Wijsman, Robin; Hsu, I‐chow; Oberije, Cary; Lustberg, Tim; Soest, Johan; Hoebers, Frank; Jochems, Arthur; El Naqa, Issam; Wee, Leonard; Morin, Olivier; Raleigh, David R.; Bots, Wouter; Kaanders, Johannes H.; Belderbos, José; Kwint, Margriet; Solberg, Timothy; Monshouwer, René; Bussink, Johan; Dekker, Andre; Lambin, Philippe (Springerâ VerlagWiley Periodicals, Inc., 2018-07)

(835.2KB)

(501KB)

Machine learning and modeling: Data, validation, communication challenges

El Naqa, Issam; Ruan, Dan; Valdes, Gilmer; Dekker, Andre; McNutt, Todd; Ge, Yaorong; Wu, Q. Jackie; Oh, Jung Hun; Thor, Maria; Smith, Wade; Rao, Arvind; Fuller, Clifton; Xiao, Ying; Manion, Frank; Schipper, Matthew; Mayo, Charles; Moran, Jean M.; Ten Haken, Randall (McGraw‐HillWiley Periodicals, Inc., 2018-10)

(238KB)

(140.1KB)

Machine learning for automated quality assurance in radiotherapy: A proof of principle using EPID data description

El Naqa, Issam; Irrer, Jim; Ritter, Tim A.; DeMarco, John; Al‐hallaq, Hania; Booth, Jeremy; Kim, Grace; Alkhatib, Ahmad; Popple, Richard; Perez, Mario; Farrey, Karl; Moran, Jean M. (Wiley Periodicals, Inc.The New York Times, 2019-04)

(565.8KB)

(475KB)

Machine Learning for Cancer Drug Combination

Wang, Ziyan; Li, Hongyang; Guan, Yuanfang (Wiley Periodicals, Inc., 2020-04)

(538.1KB)

(466.9KB)

Machine learning for crystal identification and discovery

Spellings, Matthew; Glotzer, Sharon C. (Wiley Periodicals, Inc., 2018-06)

(1.8MB)

(7MB)

Machine learning for heterogeneous catalyst design and discovery

Goldsmith, Bryan R.; Esterhuizen, Jacques; Liu, Jin‐xun; Bartel, Christopher J.; Sutton, Christopher (John Wiley & Sons, 2018-07)

(565.9KB)

(862.1KB)

Machine learning for radiation outcome modeling and prediction

Luo, Yi; Chen, Shifeng; Valdes, Gilmer (Wiley Periodicals, Inc.CRC Press, 2020-06)

(323.2KB)

(442.6KB)

Machine learning in genome-wide association studies

Szymczak, Silke; Biernacka, Joanna M.; Cordell, Heather J.; González-Recio, Oscar; König, Inke R.; Zhang, Heping; Sun, Yan V. (Wiley Subscription Services, Inc., A Wiley Company, 2009)

(107.9KB)

Machine Learning Methods for Data-Driven Turbulence Modeling

Zhang, Ze Jia; Duraisamy, Karthikeyan (American Institute of Aeronautics and Astronautics, 2015-06-18)

(9.7MB)

Machine learning techniques for personalized breast cancer risk prediction: comparison with the BCRAT and BOADICEA models

Ming, Chang; Viassolo, Valeria; Probst-Hensch, Nicole; Chappuis, Pierre O.; Dinov, Ivo D.; Katapodi, Maria C. (2019-06-20)

(777KB)