ArticleMethods (San Diego, Calif.)2020
Generating topological protein interaction scores and data visualization with TopS.
Article in Methods (San Diego, Calif.), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Revolutionizing Stem Cell Sorting with Machine Learning: A Review of Trends, Tools, and Future Directions.Iranian journal of medical sciences · 2026Review
- Distinct Regions within SAP25 Recruit O-Linked Glycosylation, DNA Demethylation, and Ubiquitin Ligase and Hydrolase Activities to the Sin3/HDAC Complex.Journal of proteome research · 2024Article
- Article
- Mass Spectrometry-Based Proteomics Technologies to Define Endogenous Protein-Protein Interactions and Their Applications to Cancer and Viral Infectious Diseases.Mass spectrometry reviewsReview
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Authors and funding
3 authors.
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Abstract
Detecting subnetworks in large networks is of great interest. Recently, we developed a topological score framework for the analysis of protein interaction networks and implemented it as a web application, called TopS. Given a multivariate data presented as a matrix, TopS generates topological scores between any column and row in the matrix aiming to identify overwhelming preference interactions. This information can be further used into visualization tools such as clusters and networks to investigate how networks benefit from these interactions. We present a web tool called TopS that aims to have an intuitive user interface. Users can upload data from a simple delimited CSV file that can be created in a spreadsheet program. As an output, user is provided with a scoring matrix as tab-delimited file that can be interchanged with other software, heatmap and clustering figures in pdf format. Here we demonstrate the current capabilities of TopS using an existing dataset generated for the study of the human Sin3 chromatin remodeling complex.
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