Integrated Data Sets
Integrated Data Sets - Related Videos
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.
01:09
Design Example: Setting a Curve Using Design Data
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2025
Designing and plotting a curve using field data requires precise calculations and execution. A horizontal curve with a radius of 200 meters and an intersection angle of 20 degrees is established using the method of perpendicular offsets from the long chord. The long chord, which spans between the curve's endpoints, is calculated to be 69.46 meters in length. To maintain accuracy in plotting, intervals of 3 meters are selected along the chord.The engineer determines the offset distances for each...
12:53
Bidirectional Retroviral Integration Site PCR Methodology and Quantitative Data Analysis Workflow
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Cited by 5 •
2017
This manuscript describes the experimental procedure and software analysis for a bidirectional integration site assay that can simultaneously analyze upstream and downstream vector-host junction DNA. Bidirectional PCR products can be used for any downstream sequencing platform. The resulting data are useful for a high-throughput, quantitative comparison of integrated DNA targets.
08:22
IR-TEx: An Open Source Data Integration Tool for Big Data Transcriptomics Designed for the Malaria Vector Anopheles gambiae
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Cited by 1 •
2020
IR-TEx explores insecticide resistance-related transcriptional profiles in the species Anopheles gambiae. Provided here are full instructions for using the application, modifications for exploring multiple transcriptomic datasets, and using the framework to build an interactive database for collections of transcriptomic data from any organism, generated in any platform.
07:40
A Data Integration Workflow to Identify Drug Combinations Targeting Synthetic Lethal Interactions
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2021
Large genetic screens in model organisms have led to the identification of negative genetic interactions. Here, we describe a data integration workflow using data from genetic screens in model organisms to delineate drug combinations targeting synthetic lethal interactions in cancer.
FAQs
Shared identifiers connect measurements that refer to the same pathogen, host, sample, or clinical context, while standardized metadata records how each measurement was generated and described. Together, these features help computational analyses align genomic, transcriptomic, proteomic, clinical, and epidemiological information. This alignment allows relationships across experiments and platforms to be examined rather than treated as isolated observations.
Normalization helps place measurements from different experimental platforms into a form suitable for comparison and computational analysis. Without this step, differences in data format or measurement scale could obscure relationships among genomic, transcriptomic, proteomic, and clinical observations. Properly prepared data can therefore support more coherent interpretation of host responses, pathogen behavior, and disease-associated patterns.
Computational analysis of aligned data can connect pathogen features with host-cell responses, immune activity, clinical characteristics, and epidemiological patterns. These relationships may show how pathogens interact with host cells or which molecular patterns accompany disease progression and treatment response. The value comes from examining linked measurements across biological levels instead of interpreting each data type separately.
Combining molecular measurements with clinical information can associate variation in immune responses with differences among individuals. Genomic, transcriptomic, and proteomic patterns may be considered alongside disease status, progression, or treatment response, while standardized metadata preserves the relevant context. This systems-level view supports investigation of why similar infections can produce different biological or clinical outcomes.
A supported workflow begins by gathering measurements from relevant experimental and clinical sources, then organizing shared identifiers and standardized metadata. Researchers next align the data formats, apply normalization, and use computational analysis to examine relationships across data types. The resulting data set can then be interpreted in relation to host-pathogen interactions, immune variation, disease progression, or treatment response.
They are particularly useful when a research question spans more than one biological or clinical level. Examples include studying pathogen interactions with host cells, comparing immune responses among individuals, tracking disease progression, and examining treatment response. The same approach also supports biomarker discovery, vaccine and therapeutic development, outbreak investigation, and reproducible systems-level research.
Integrated analyses can identify molecular patterns associated with disease progression or treatment response and can support discovery of biomarkers. They also provide evidence relevant to vaccine and therapeutic development and can connect biological findings with epidemiological observations during outbreak investigation. Because the approach combines standardized information across sources, it contributes to reproducible systems-level research.