Disease Network Mapping
Disease Network Mapping - Related Videos
09:52
Generation of Shear Adhesion Map Using SynVivo Synthetic Microvascular Networks
0 Views •
Cited by 10 •
2014
Flow chambers used in adhesion experiments typically consist of linear flow paths and require multiple experiments at different flow rates to generate a shear adhesion map. SynVivo-SMN enables the generation of shear adhesion map using a single experiment utilizing microliter volumes resulting in significant savings in time and consumables.
09:39
Mapping Dysfunctional Protein-Protein Interactions in Disease
0 Views •
2025
Here, we present a protocol to enable the capture and identification of disease-specific protein-protein interactions from native cells and tissues using chemical probes and mass spectrometry. The resulting interaction datasets are analyzed through a dedicated web-based platform to reveal dynamic network dysfunctions and pathway alterations linked to disease.
02:26
Protein Networks
0 Views •
2020
An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions. These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
03:19
Visualization and Mapping of Methoxy-X04-Labeled Amyloid Plaques in an Alzheimer’s Disease Mouse Brain Section
0 Views •
2025
Source: Bisht, K., et. al. Correlative Light and Electron Microscopy to Study Microglial Interactions with β-Amyloid Plaques. J. Vis. Exp. (2016) This video demonstrates the visualization and mapping of Methoxy-X04-labeled amyloid plaques in Alzheimer’s disease mouse brain sections using fluorescence and bright-field microscopy, enabling precise correlation of plaques with anatomical structures for studying Alzheimer’s pathology.
11:03
Motor Maps
0 Views •
2023
Source: Laboratories of Jonas T. Kaplan and Sarah I. Gimbel—University of Southern California One principle of brain organization is the topographic mapping of information. Especially in sensory and motor cortices, adjacent regions of the brain tend to represent information from adjacent parts of the body, resulting in maps of the body expressed on the surface of the brain. The primary sensory and motor maps in the brain surround a prominent sulcus known as the central sulcus. The cortex...
FAQs
An edge should be interpreted according to the evidence it represents, rather than treated as a universal connection. In a Disease Network Mapping model, it may encode a molecular interaction, a shared mechanism, or an association between clinical traits. Keeping these meanings distinct helps researchers compare relationships appropriately and prevents a network link from being mistaken for direct biological causation.
Clusters are useful because they group diseases or biological features that connect through recurring relationships. A cluster may therefore point to shared disease biology rather than merely a list of similar diagnoses. In neuroscience, examining such groupings can reveal overlap among brain disorders and support disease classification based on common mechanisms, alongside conventional diagnostic categories.
Highly influential components matter because their position can connect otherwise separate parts of a disease network. Identifying these components helps prioritize features for follow-up analysis, including genes, proteins, pathways, symptoms, or clinical traits. Their prominence does not by itself establish that they cause disease, but it can guide investigation of biomarkers and possible therapeutic targets.
Linking molecular features with patient phenotypes allows a map to connect biological changes with observed clinical traits. This integration can show whether related molecular patterns correspond to similar symptoms or other patient-level differences. For neuroscience research, that relationship provides a framework for studying how disease biology may contribute to phenotype variation and treatment-response patterns.
A practical workflow begins by selecting the biological or clinical entities relevant to the question, then assembling data that support relationships among them. Researchers represent those entities and relationships in a network and apply network analysis to identify clusters, influential components, and connections between disorders. The resulting structure can then be examined for biological or clinical interpretation.
Disease Network Mapping is especially useful when isolated diagnoses do not capture overlapping mechanisms across brain diseases. Researchers can use the resulting relationships to investigate comorbidity, compare disorders, and connect molecular findings with patient phenotypes. This broader view may also support biomarker discovery and therapeutic-target discovery by showing where disease-related features converge.
Maps can support disease classification by organizing relationships that cut across diagnostic boundaries. They can also provide context for studying why patients with related disorders may differ in treatment response, when clinical traits are connected to molecular or pathway-level features. The method therefore contributes to research on comorbidity, disease characterization, and response variation without replacing clinical evaluation.