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Mining of Multivariate Temporal Biological Data: A Framework for the Rational Design of Data-Driven Models.

DE200415006266

Publication Date	2001
Personal Author	Kamimura, R. T.; Bicciato, S.; Shimizu, H.; Alford, J.; Stephanopoulos, G. N.
Page Count	16
Abstract	A framework is presented that emphasizes the need to understand the strengths and weaknesses of the data prior to modeling. In short, given a list of constraints, the idea is to let the data sort itself along those guidelines. Once the data has been organized into some coherent faction, the user has a better understanding of what the strengths and weaknesses of the data are as the analysis proceeds. The goal is to understand the character of the data so that the user is not overwhelmed but is able to systematically organize and decompose information so as to facilitate the analysis and build an effective model. The data analyzed is that from an industrial fermentation but the framework presented is generic enough that it can be used in any application involving multivariate time series data, such as time varying microarray measurements.
Keywords	Data management Information storage and retrieval systems Multivariate analysis Modeling Data collection Technology Analyses Design Data analysis Data processing Mining (Data) Strengths (Potency) Weaknesses (Deficiencies) Data driven models
Source Agency	Technical Information Center Oak Ridge Tennessee
Corporate Authors	Lawrence Livermore National Lab., CA.; Department of Energy, Washington, DC.
Supplemental Notes	Sponsored by Department of Energy, Washington, DC.
Document Type	Technical Report
NTIS Issue Number	200414

Mining of Multivariate Temporal Biological Data: A Framework for the Rational Design of Data-Driven Models.

Mining of Multivariate Temporal Biological Data: A Framework for the Rational Design of Data-Driven Models.
DE200415006266

Publication Date	2001
Personal Author	Kamimura, R. T.; Bicciato, S.; Shimizu, H.; Alford, J.; Stephanopoulos, G. N.
Page Count	16
Abstract	A framework is presented that emphasizes the need to understand the strengths and weaknesses of the data prior to modeling. In short, given a list of constraints, the idea is to let the data sort itself along those guidelines. Once the data has been organized into some coherent faction, the user has a better understanding of what the strengths and weaknesses of the data are as the analysis proceeds. The goal is to understand the character of the data so that the user is not overwhelmed but is able to systematically organize and decompose information so as to facilitate the analysis and build an effective model. The data analyzed is that from an industrial fermentation but the framework presented is generic enough that it can be used in any application involving multivariate time series data, such as time varying microarray measurements.
Keywords	Data management Information storage and retrieval systems Multivariate analysis Modeling Data collection Technology Analyses Design Data analysis Data processing Mining (Data) Strengths (Potency) Weaknesses (Deficiencies) Data driven models
Source Agency	Technical Information Center Oak Ridge Tennessee
Corporate Authors	Lawrence Livermore National Lab., CA.; Department of Energy, Washington, DC.
Supplemental Notes	Sponsored by Department of Energy, Washington, DC.
Document Type	Technical Report
NTIS Issue Number	200414