Data analysis workflow

CDK Tavema is an open-source tool. CDK Taverna can be used to create chemical workflows. Recurring tasks can be automated using CDK Tavema. This can be applied for chemical data filtering, transformation, curation, migrating workflows, chemical documentation and information retrieval-related workflows (structures, reactions, pharmacophores, object relational data etc.), data analysis workflows (statistics and clustering/machine learning for QSAR, diversity analysis etc.) [17] (Fig. 9.4). 

Figure 10.4 A data analysis workflow. The training set and the validation set are subjected to an identical preprocessing procedure, resulting in N profiles for training and N profiles for validation. The N ...

As vitally important as the capabilities for experimental planning, screening, and data analysis are the procedures for preparation of inorganic catalysts. In contrast to the procedures usually applied in conventional catalyst synthesis, the synthetic techniques have to be adapted to the number of catalysts required in the screening process. Catalyst production can become a bottleneck and it is therefore necessary to ensure that HTE- and CombiChem-capable synthesis technologies are applied to ensure a seamless workflow. 

KNIME provides a user-friendly interface to visually create workflows allowing a step-by-step data analysis flow. A node—as a single entity of such a workflow— provides a very confined analysis step with a set of parameter configurations. Workflows can branch at any point, which allows easily implementing multiple approaches. 

Informatic environment pivotal data management for each of the preceding steps, which involves capture of data, storage in a consistent way, query and also stream data analysis (data workflow). 

Fig. I The typical metabolomics workflow has three key steps the isolation of metabolites, detection of the metabolites, and data analysis. The isolation step is typically determined by the class of metabolite being measured because of the physicochemical properties of different metabolite classes (i.e., hydrophobic, hydrophilic), which require different enrichment protocols. Two principle methods for metabolite detection are NMR- and MS-based methods. Finally, the data analysis can be performed in a variety of ways depending on the problem...

Lay out the basic workflow of computer applications and condnct a data analysis to identify electronic record creation and maintenance (include identification of snpporting raw data)... 

InforSense s ChemScience module targets cheminformatics solutions with a set of chemistry-specific components that a user can employ to construct a workflow for experimentation, data analysis, and visualization. Component capabilities include data import and export structure-, descriptor-, and fingerprint-based processing and library enumeration. 

Fig. 5.9 Schematic representation of the sample and data processing workflow for shotgun-proteomics-based analysis of unknown strains (si, s2, s3, s4, s5) revealing genomic inter-relationships among them. U unknown strain, PTB peptide sequence-to-bacterial strains assignments. (Reprinted with permission from Dworzanski et al. (2010, pp. 145 155). Copyright 2010 American Chemical Society)...

Fig. 6.2 A generie workflow from cultivation through data analysis to characterize bacteria using MALDI-TOF MS. At each step of the workflow, different approaches have been employed and can be optimized to maximize taxonomic resolution...

FIGURE 5.1. An overview of the workflow of a metabolomics study. A study is designed based on the original hypothesis, biofluid samples are collected and metabolomics performed on suitable platforms. The data is analyzed by use of multivariate data analysis and discriminating metabolites identfied. The metabolites are mapped to a pathway to infer biological meaning. 

Figure 4.8 Workflow diagram for a typical MS-based metabolite profiling. Step (1) is the sample preparation followed by MS analysis, which is usually coupled to a LC or GC separation step (Step 2). A key component is the data analysis (Step 3), which can be divided into the data preprocessing and chemometric analysis. This is followed by the identification of important metabolites (Step 4). Adapted from Want, E.J., et al. Nature Protocols. 2010, with permission.

In this chapter, we discuss the theory behind SMLM, labeling strategies for the fluorescent probes, describe a workflow and a detailed protocol for fixation and immunostaining of neuronal microtubules, and provide some tips for successful super-resolution imaging, data analysis, and image reconstruction. 

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