Zobrazeno 1 - 9
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pro vyhledávání: '"William S. Ford"'
Publikováno v:
Complex Adaptive Systems
Recent outbreaks of listeria, salmonella, and other pathogens have reinforced the need for more rigorous testing of food products. Millions are spent each year testing food. Certifying the safety of the food is a challenging task using traditional te
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https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1fd8761367577068b1b670866bec3a32
Publikováno v:
Complex Adaptive Systems
The objective of this research is to develop a complex adaptive piecewise linear regression/probabilistic neural network (PNN) intelligent system for the rapid detection and classification of Escherichia coli (E.coli). The rapid detection and classif
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::14af683609215a57bc4490a5ee31e9f2
Autor:
Jing Zhang, Omowunmi A. Sadik, Kun Xiang, Walker H. Land, Yinglei Li, J. David Schaffer, Robert Congdon, William S. Ford
Publikováno v:
The Analyst. 141(4)
We hereby report the design and implementation of an Autonomous Microbial Cell Culture and Classification (AMC(3)) system for rapid detection of food pathogens. Traditional food testing methods require multistep procedures and long incubation period,
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https://explore.openaire.eu/search/publication?articleId=doi_dedup___::01e803b6454e6953dd20beabe353dd4c
https://pubmed.ncbi.nlm.nih.gov/26818563
https://pubmed.ncbi.nlm.nih.gov/26818563
Publikováno v:
Southern Journal of Applied Forestry. 36:26-32
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::92ec64c5734ed14e1e14ddd21c3d17cb
https://doi.org/10.5849/sjaf.10-023
https://doi.org/10.5849/sjaf.10-023
Publikováno v:
Complex Adaptive Systems
An accurate prognostic model of a cancer patient after treatment can be useful in deciding the next course of treatment or efficacy of said treatment. Gene expression microarray data has been used to predict survival times [1], or to classify the pat
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https://explore.openaire.eu/search/publication?articleId=doi_dedup___::07a76a1de27fe44971af861497aacca4
Autor:
Walker H. Land, John J. Heine, Ravi Mathur, Steven A. Eschrich, Jin-Woo Park, Timothy J. Yeatman, William S. Ford, Xingye Qiao, Nathan Hotchkiss
Publikováno v:
Complex Adaptive Systems
PLS initially creates uncorrelated latent variables which are linear combinations of the original input vectors Xi, where weights are used to determine linear combinations, which are proportional to the covariance. Secondly, a least squares regressio
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https://doi.org/10.1016/j.procs.2011.08.051
https://doi.org/10.1016/j.procs.2011.08.051
Autor:
Jeffrey A. Borgia, Daniel Margolis, Jack Y. Yang, Xingye Qiao, William S. Ford, Walker H. Land, Youping Deng, Joseph Perez-Rogers, Christopher T Paquette
Publikováno v:
BMC Systems Biology
BMC Systems Biology, Vol 5, Iss Suppl 3, p S13 (2011)
BMC Systems Biology, Vol 5, Iss Suppl 3, p S13 (2011)
Background The primary objectives of this paper are: 1.) to apply Statistical Learning Theory (SLT), specifically Partial Least Squares (PLS) and Kernelized PLS (K-PLS), to the universal "feature-rich/case-poor" (also known as "large p small n", or "
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e4ea5560b36b9ec5649e4c6db1a09af4
http://europepmc.org/articles/PMC3287568
http://europepmc.org/articles/PMC3287568
Publikováno v:
Memory & Cognition. 1:205-208
A number of studies have shown that children learn noun pairs faster when they have been presented in a noun-verb-noun context than when they are presented in a noun-conjunction-noun context. Suzuki and Rohwer (1969) hypothesized that the underlying
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https://doi.org/10.3758/bf03198096
https://doi.org/10.3758/bf03198096
Autor:
William S. Ford, Walker H. Land
Publikováno v:
Complex Adaptive Systems
Gene expression microarray analysis is a rapid, low cost method of analyzing gene expression profiles for cancer prognosis/diagnosis. Microarray data generated from oncological studies typically contain thousands of expression values with few cases.
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