Discover Community Create You

Loading publication...

Noodle always counts identifier-free feature events for service operation. With your permission, Google Analytics can also estimate audience and engagement. Search text, publication identifiers, cookies, IP addresses and browser fingerprints are never included in Noodle's first-party event counters.

Privacy details

Article

DiSWOP: a novel measure for cell-level protein network analysis in localized proteomics image data.

Bioinformatics (Oxford, England) - 1 Feb 2014

Kovacheva Violeta N, Khan Adnan M, Khan Michael, Epstein David B A, Rajpoot Nasir M

Abstract excerpt

MOTIVATION: New bioimaging techniques have recently been proposed to visualize the colocation or interaction of several proteins within individual cells, displaying the heterogeneity of neighbouring cells within the same tissue specimen. Such techniques could hold the key to understanding complex biological systems such as the protein interactions involved in cancer. However, there is a need for new algorithmic...

Read the complete abstract on PubMed

Topics

Share this publication in a Topic to start or enrich a Post.

Share to Algorithms
Share to Colonic Neoplasms

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Image Processing, Computer-Assisted

Share to Phenotype

Share to Protein Interaction Mapping

Share to Proteomics

Source

Identifiers

PMID: 24273247
DOI: 10.1093/bioinformatics/btt676

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 6 Sept 2026. Manifest publication-graph-cohort-2026-09-06.1.

Source receipt: pubmed, retrieved 6 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 8 closest

DiSWOP: a novel measure for cell-level protein network analysis in localized proteomics image data.PMID 24273247Read ↗

Toponome Imaging System: <i>In Situ</i> Protein Network Mapping in Normal and Cancerous Colon from the Same Patient Reveals More than Five-Thousand Cancer Specific Protein Clusters and Their Subcellular Annotation by Using a Three Symbol CodeSemantic proximity Read ↗

netPCF: Geometry-aware pair correlation functions for spatial biologySemantic proximity Read ↗

Quantification of spatial and phenotypic heterogeneity in an agent-based model of tumour-macrophage interactionsSemantic proximity Read ↗

Proximity-Informed Graph Learning Defines Spatial Protein Communities for Tumor-Associated Proximity Antigen DiscoverySemantic proximity Read ↗

Quantification of spatial and phenotypic heterogeneity in an agent-based model of tumour-macrophage interactions.Semantic proximity Read ↗

Complex networks approach to gene expression driven phenotype imaging.Semantic proximity Read ↗

A quantitative spatial cell-cell colocalizations framework enabling comparisons between in vitro assembloids and pathological specimensSemantic proximity Read ↗

Feasibility of Inferring Spatial Transcriptomics from Single-Cell Histological Patterns for Studying Colon Cancer Tumor HeterogeneitySemantic proximity Read ↗

⌁Select a neighboring publication to make it the new centre.