Data analysis in biomedical research

Albert A (1995) Survival data analysis in clinical research. Biocybernetics and Biomedical... 

Multivariate Data Analysis and Experimental Design in Biomedical Research... 

This chapter presents certain simple methods for describing mathematically some of the types of signals which are encountered in biomedical research. Techniques are described for both periodic and aperiodic signals. Techniques for extraction of signals from noise are also presented. Signal description and not data reduction is the theme of this chapter. For techniques directed toward statistical analysis of data, the reader is referred to standard reference works (for example, Wortham and Smith, 1959). 

CellProfiler and CellProfiler Analyst are free Open Source software for automated image analysis, data visualization, and machine learning. Versions for Mac, Windows, and Linux are available and both software can be downloaded at http //www.cellprofiler. org. CellProfiler was developed by Anne Carpenter and Thouis Jones in the laboratory of David Sabatini at the Whitehead Institute for Biomedical Research and by Polina Golland at the CSAIL of the MIT. 

Since its inception about 15 year ago, MALDI-IMS has been developed into a powerful and versatile tool for biomedical research. It allows for the investigation of the spatial distribution of molecules at complex surfaces. The combination of molecular speciation with local analysis makes a chemical microscope that can be used for the direct biomolecular characterization of histological tissue section surface. However, successful detection of the analytes of interest at the desired spatial resolution requires careful attention to several steps in the IMS protocol matrix selection, matrix coating, data acquisition, and data processing. MALDI-IMS is increasingly playing an important role in the drug discovery and development and disease treatment. 

In 2000 the Declaration of Helsinki on biomedical research was amended and now takes into account the Three Rs. It now opens possibilities for testing in humans based, among other sources of information, on data obtained from validated in vitro analysis without using live animals. In light of this development more focus should be given to alternative approaches in relation to human experimentation. 

Data analysis strategies and their appropriate statistical methodologies ultimately depend on the goals of the study. As recent applications of MSI, and MALDI-MSI in particular, have been predominantly in the field of biomedical research, this chapter focuses on examples from a clinical context. The relationship between the different goals and the corresponding data analysis strategies are described in Table 6. 

OTA s approach to R D cost assessment relied on a detailed analysis of the validity of the Hansen and DiMasi studies. First, OTA examined the validity of the methods used to estimate each component of R D costs (cash outlays, project time profiles, and success rates). Second, OTA tested the consistency of the resulting estimates with corroborative studies. Third, OTA examined whether the rate of increase in real (i.e., inflation-adjusted) R D cost implied by the two studies is consistent with data on trends in major cost drivers, such as the number of subjects of clinical trials, biomedical research personnel costs, and animal research costs. 

The rank-by-feature framework was shown effective in facUitating users exploration of large multidimensional datasets fi om several different research fields including microarray data analysis (Seo and Shneiderman, 2006). We believe that it can be successfully apphed to biomedical datasets that are very often large multidimensional datasets. In the following sections, we introduce visual interface frameworks and ranking criteria for ID and 2D projections for multidimensional datasets. 

Biomedical science researchers This group uses biomedical instruments, simulation tools, data-analysis software, and data-mining software to conduct research and development in laboratories or in the field. 

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