Data-driven

Whereas a model-driven method imposes a rigid classification scheme onto a set of reactions, the data-driven methods try to derive a classification from the data presented. 

There are two fundamental approaches to automatic reaction classification model-driven and data-driven methods. 

Saraiva, P.M., 1995. Inductive and analogical learning Data-driven improvement of Process operation. In Intelligent Systems in Process Engineering. Eds. G. Stepha-nopoulos and C. Han, Advances in Chemical Engineering, 22, 377. 

The specification development process is a data-driven activity that requires a validated analytical method. The levels of data needed include assay precision, replicate process results (process precision), and real-time stability profiles. A statistical analysis of these data is critical in setting a realistic specification. Most often, aggregation and fragmentation degradation mechanisms are common to protein and peptide therapeutics. Therefore, the SE-HPLC method provides a critical quality parameter that would need to be controlled by a specification limit. 

Tracking SARs and Determining Mechanism of Action Data-driven Drug-based Pharmacology... 

When dealing with large numbers of investigational compounds to be tested for agonist or antagonist activity, the methods used to determine system-independent measures of activity must be identified from the initial profile of activity (data-driven analysis). 

Christopher R, Dhiman A, Fox J, Gendelman R, Haberitcher T, Kagle D, et al. Data-driven computer simulation of human cancer cell. Ann NY Acad Sci 2004 1020 132-53. 

Scientists believe they are data driven—not true They use data to confirm decisions they wish to take. Data is used selectively in support. 

INDUCTIVE AND ANALOGICAL LEARNING DATA-DRIVEN IMPROVEMENT OF PROCESS OPERATIONS ... 

To address the modified problem statements and uncover final solutions with the desired alternative formats, data-driven nonparametric learning methodologies, based on direct sampling approaches, were described. They require far fewer assumptions and a priori decisions on the part of the user than most conventional techniques. These practical frameworks for extracting knowledge from operating data present the final uncovered solutions to the decisionmaker in formats that are both easy to understand and implement. 

Saraiva, P., Data-driven learning frameworks for continuous process analysis and improvement. Ph.D. Thesis. Massachusetts Institute of Technology, Dept. Chem. Eng., Cambridge. MA. 1993. 

Chonghun Han, Ramachandran Lakshmanan, Bhavik Bakshi, and George Stephanopoulos, Nonmonotonic Reasoning The Synthesis of Operating Procedures in Chemical Plants Pedro M. Saraiva, Inductive and Analogical Learning Data-Driven Improvement of Process Operations... 

We use a method that implements the Unbiased Prediction Risk criterion [13] to provide a data-driven approach for the selection of the regularization parameter. The equality constraints are handled with LQ factorization [14] and an iterative method suggested by Villalobos and Wahba [15] is used to incorporate the inequality constraints [10]. The method is well suited for the relatively large-scale problem associated with analyzing each image voxel as no user intervention is required and all the voxels can be analyzed in parallel. 

In this proposal, a manufacturer could also have the option of using a third party to assess conformance with the recognized standard. The third party could perform an assessment of conformance with the standard and provide the manufacturer with a statement to this effect, and the S10(k) application could then include the statement as well as summary on declaration of conformity. The abbreviated 510(k) submissions may compete with routine 510(k)s, and it is anticipated that their review and processing will be more efficient and timely than those of routine (traditional) submissions, which tend to be intensively data driven (Fig. 2). 

These techniques may be coarsely classified into techniques that mimic human reasoning about toxicological phenomena (Expert Systems) and methods that derive predictions from a training set of experimentally determined data (Data Driven Systems). 

Teegavarapu RSV, Chandramouli V (2005) Improved weighting methods, deterministic and stochastic data-driven models for estimation of missing precipitation records. J Hydrol 312 191-206... 

This practice supports the CSB observation that effective process hazard analysis for a reactive system is essentially more data driven than conventional process hazard analysis given the technical complexity of the reactive hazard. Three of the five visited companies use reactivity test data when conducting process hazard analysis two use qualitative hazard evaluation methods only. 

Despite the broad definition of chemometrics, the most important part of it is the application of multivariate data analysis to chemistry-relevant data. Chemistry deals with compounds, their properties, and their transformations into other compounds. Major tasks of chemists are the analysis of complex mixtures, the synthesis of compounds with desired properties, and the construction and operation of chemical technological plants. However, chemical/physical systems of practical interest are often very complicated and cannot be described sufficiently by theory. Actually, a typical chemometrics approach is not based on first principles—that means scientific laws and mles of nature—but is data driven. Multivariate statistical data analysis is a powerful tool for analyzing and structuring data sets that have been obtained from such systems, and for making empirical mathematical models that are for instance capable to predict the values of important properties not directly measurable (Figure 1.1). 

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