Multi-target Processing
Multi-target Processing - Related Videos
22:10
Multi-target Parallel Processing Approach for Gene-to-structure Determination of the Influenza Polymerase PB2 Subunit
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Cited by 3 •
2013
Structure-based drug design plays an important role in drug development. Pursuing multiple targets in parallel greatly increases the chance of success for lead discovery. The following article highlights how the Seattle Structural Genomics Center for Infectious Disease utilizes a multi-target approach for gene-to-structure determination of the PB2 influenza A subunit.
13:40
Examining Local Network Processing using Multi-contact Laminar Electrode Recording
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Cited by 14 •
2011
A fundamental issue in our understanding of cortical circuitry is how networks in different cortical layers encode sensory information. Here we describe electrophysiological techniques utilizing multi-contact laminar electrodes to record single-units and local field potentials and present analyses to identify cortical layers.
10:17
Multi-target Chromogenic Whole-mount In Situ Hybridization for Comparing Gene Expression Domains in Drosophila Embryos
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Cited by 9 •
2016
We describe a multi-target chromogenic whole-mount in situ hybridization (MC-WISH) procedure in intact Drosophila embryos allowing simultaneous and specific detection of three different mRNA distribution patterns by contrasting color precipitates.
08:51
Application of Unsupervised Multi-Omic Factor Analysis to Uncover Patterns of Variation and Molecular Processes Linked to Cardiovascular Disease
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Cited by 3 •
2024
We present a flexible, extendible Jupyter-lab-based workflow for the unsupervised analysis of complex multi-omics datasets that combines different pre-processing steps, estimation of the multi-omics factor analysis model, and several downstream analyses.
07:35
The Multi-group Experiment
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2023
Source: Laboratories of Gary Lewandowski, Dave Strohmetz, and Natalie Ciarocco—Monmouth University A multi-group design is an experimental design that has 3 or more conditions/groups of the same independent variable. This video demonstrates a multi-group experiment that examines how different interethnic ideologies (multiculturalism and color-blind) influence feelings about diversity and actions toward and out-group member. In providing an overview of how a researcher conducts a multi-group...
FAQs
Distinct labels, channels, or analytical measurements allow each target to generate a separately recognizable signal. Target-specific antibodies, probes, or other recognition reagents provide the selectivity, while the analytical system resolves the resulting signals individually. This separation makes it possible to attribute measurements to particular pathogens, antigens, cytokines, immune-cell markers, or therapeutic targets within one workflow.
Multiplexing preserves relationships among targets that may be missed when measurements are performed separately. Assessing pathogens, immune-cell markers, cytokines, or antigens together can show how components of an immune response occur in relation to one another. That integrated view supports interpretation of complex responses rather than limiting analysis to an isolated measurement.
Reliable discrimination depends on matching each biological target with an appropriate recognition reagent and assigning distinguishable labels, channels, or analytical measurements. The workflow must therefore connect target identity to a separately interpretable signal. When these elements are coordinated, multiple measurements can be analyzed together while retaining information about which signal corresponds to which target.
A practical workflow begins by selecting the pathogens, antigens, cytokines, immune-cell markers, or therapeutic targets relevant to the research question. The investigator then pairs those targets with suitable antibodies, probes, or other recognition reagents and establishes distinguishable signal assignments. After simultaneous processing, the separate measurements are resolved and interpreted together to evaluate the biological response.
This approach is useful when researchers need coordinated information about pathogen characteristics, immune status, or responses to intervention. It can support pathogen characterization, immune monitoring, biomarker discovery, and evaluation of combination interventions. Measuring related targets within one workflow helps connect infection-associated signals with immune-cell, cytokine, antigen, or therapeutic-target measurements.
Coordinating several measurements in one workflow can conserve sample and reduce processing time while producing a broader biological profile. The resulting dataset can support comparisons among targets and reveal relationships relevant to complex immune responses. These advantages are particularly valuable when the study requires simultaneous assessment of infection features, immune markers, and therapeutic targets.