Computational Data Analysis
Computational Data Analysis - Related Videos
09:37
Extracting Metrics for Three-dimensional Root Systems: Volume and Surface Analysis from In-soil X-ray Computed Tomography Data
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Cited by 2 •
2016
A methodology for obtaining visual and quantitative root structure information from X-ray computed tomography data acquired in-soil is presented.
11:06
3D Printing of Preclinical X-ray Computed Tomographic Data Sets
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Cited by 16 •
2013
Using modern plastic extrusion and printing technologies, it is now possible to quickly and inexpensively produce physical models of X-ray CT data taken in a laboratory. The three -dimensional printing of tomographic data is a powerful visualization, research, and educational tool that may now be accessed by the preclinical imaging community.
06:35
Basics of Multivariate Analysis in Neuroimaging Data
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Cited by 37 •
2010
The current article describes the basics of multivariate analysis and contrasts it to the more commonly used voxel-wise univariate analysis. Both types of analysis are applied to a clinical-neuroscience data set. Supplementary split-half simulations show better replication of the multivariate results in independent data sets.
07:01
Navigating the Mass Spectrometry-Based Proteomic Data Using Free Computational Tools
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2025
Mass spectrometry-based proteomic data is available in open databases and accessible using free tools. Given the complexity of database searches and descriptions, many biologists lack the knowledge to utilize these datasets. Here, we provide a guide on using free tools for basic proteomic data searches.
06:17
Analysis of Multidimensional Microscopy Data Using Cell-ACDC
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2025
Accurate analysis of multidimensional microscopy data requires complex workflows. This article demonstrates how to use the software Cell-ACDC. It leverages state-of-the-art AI-driven models for segmentation, tracking, cell pedigree analysis, and quantification of microscopy data. Crucially, it complements these models with an innovative framework for semi-automated correction of the models' output.
FAQs
Integration brings different data types into a shared analytical framework so their relationships can be examined together. Analysts first preprocess the datasets and apply methods such as normalization and feature extraction before combining relevant information. This approach allows molecular measurements, image-derived characteristics, and clinical observations to contribute jointly to analyses of tumor biology and patient-related outcomes.
Normalization prepares measurements for meaningful comparison within an analysis. Cancer studies often combine data produced in different forms, so untreated values may be difficult to interpret together. Applying normalization during preprocessing helps create a more consistent basis for subsequent classification, biomarker evaluation, and predictive modeling, allowing computational results to reflect relevant biological or clinical patterns.
Feature extraction converts complex datasets into selected characteristics that can be analyzed systematically. In cancer research, these features may come from genomic, transcriptomic, imaging, or clinical data. Examining their variation helps characterize tumor heterogeneity, meaning differences among tumor features, and can identify patterns that support biomarker discovery or the separation of biologically or clinically distinct groups.
Classification assigns observations to defined categories, whereas predictive modeling uses available data to estimate an outcome or response. Both methods can be applied after preprocessing and feature extraction, but they answer different analytical questions. Together, they help evaluate whether measured cancer characteristics distinguish groups, support patient stratification, or provide information relevant to treatment response.
A typical workflow begins by organizing the available datasets, followed by preprocessing and integration of relevant genomic, transcriptomic, imaging, and clinical information. Analysts then apply normalization, extract informative features, and use classification or predictive modeling. The resulting patterns are interpreted as potential findings about tumor heterogeneity, biomarkers, treatment responses, or patient groups.
Researchers apply these analyses when datasets contain measurements that may be associated with how tumors respond to treatment. By integrating relevant molecular, imaging, and clinical information, analysts can examine response-related patterns and use predictive modeling to evaluate them. The results may help identify treatment-associated biomarkers and support patient stratification for cancer research studies.
Its analytical outcomes include improved characterization of tumor heterogeneity, identification of molecular biomarkers, evaluation of treatment responses, and support for patient stratification. These findings convert complex genomic, transcriptomic, imaging, and clinical measurements into testable research results. They can therefore inform studies of cancer biology and contribute to the development of more precise diagnostic and therapeutic approaches.