Statistical network analysis applications

Polymer networks are conveniently characterized in the elastomeric state, which is exhibited at temperatures above the glass-to-rubber transition temperature T. In this state, the large ensemble of configurations accessible to flexible chain molecules by Brownian motion is very amenable to statistical mechanical analysis. Polymers with relatively high values of such as polystyrene or elastin are generally studied in the swollen state to lower their values of to below the temperature of investigation. It is also advantageous to study network behavior in the swollen state since this facilitates the approach to elastic equilibrium, which is required for application of rubber elasticity theories based on statistical thermodynamics. ... 

Table 3 lists the major advanced computational software tools that are currently used for data analysis, visualization, modeling, simulation, and statistical computing, especially for microbial metabolic networks, models, and omics experiments. The given selection while intended to cover currently available software in this field is subjective, and the reader should consider available literature to focus on the specialized aspects and specific applications of the listed databases and software tools. 

As an example, we can look at how data for a clinical study would be entered without networking and groupware applications. First, data entry screens would be designed and copied to a disk to be distributed to the computers where the data entry was done. The data would be entered and copied back to a disk. The disks would then be collected and merged together at some computer. The data would then be analyzed for correctness and completeness. If problems were found, the problem would be relayed back to the data entry person responsible for the problem (by a phone call, memo, or post), the work rekeyed and then re-collected and remerged. When the data entry was complete, it would be copied and handed to others for statistical analysis. 

There are several chemometric methods applicable which provide numerical analytical data and their implementation remains a specialist affair. They vary from simple statistical tools such as principal component analysis (PCA) to highly complex methods such as neural networks. 

An inexperienced user or sometimes even an avid practitioner of QSAR could be easily con-fiased by the multitude of methodologies and naming conventions used in QSAR studies. Two-dimensional (2D) and three-dimensional (3D) QSAR, variable selection and artificial neural network methods, comparative molecular field analysis (CoMFA), and binary QSAR present examples of various terms that may appear to describe totally independent approaches, which cannot be even compared to each other. In fact, any QSAR method can be generally defined as the application of mathematical and statistical methods to the problem of finding empirical relationships (QSARmod-els)of the form, D . D ), where... 

The analysis of a series of chiroptical spectra and recovery of systematic trends in a given set can be carried out in several ways. In the past, the results strongly depended on the spectroscopist s personal experience actually, this was the least objective part of the circular dichroism application. Nowadays, we can rely on general procedures of statistical data treatment like singular value decomposition, factor analysis (especially its first part, analysis of the correlation matrix and the projection of the experimental spectra onto the space of orthogonal components), cluster analysis and the use of neural networks. This field has been pioneered by Pancoska and Keiderling [72-76], and also by Johnson [77] when analyzing the chiroptical properties of biopolymers. 

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