Compact integration of multi-network topology for functional analysis of genes
Open accessAbstract
The topological landscape of molecular or functional interaction networks provides a rich source of information for inferring functional patterns of genes or proteins. However, a pressing yet-unsolved challenge is how to combine multiple heterogeneous networks, each having different connectivity patterns, to achieve more accurate inference. Here, we describe the Mashup framework for scalable and robust network integration. In Mashup, the diffusion in each network is first analyzed to characterize the topological context of each node. Next, the high-dimensional topological patterns in individual networks are canonically represented using low-dimensional vectors, one per gene or protein. These vectors can then be plugged into off-the-shelf machine learning methods to derive functional insights about genes or proteins. We present tools based on Mashup that achieve state-of-the-art performance in three diverse functional inference tasks: protein function prediction, gene ontology reconstruction, and genetic interaction prediction. Mashup enables deeper insights into the structure of rapidly accumulating and diverse biological network data and can be broadly applied to other network science domains.
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The Mashup algorithm analyzes the topology of each network separately and learns a single weighted network representation that best explains the topological patterns observed across all networks.
“In a related method, the Mashup algorithm analyzes the topology of each network separately and learns a single weighted network representation that best explains the topological patterns observed across all networks (Cho et al., 2016).”
From Mapping the multiscale structure of biological systems (2021) · cites this paper as Cho et al., 2016
The NeXO and Mashup efforts found that the majority of cellular components catalogued in GO could be recovered from hierarchical detection of communities in protein network data.
“For instance, the multiscale models of cellular components constructed by the NeXO and Mashup efforts (see above) found that the majority of cellular components catalogued in GO could be recovered from hierarchical detection of communities in protein network data (Cho et al., 2016; Dutkowski et al., 2013).”
From Mapping the multiscale structure of biological systems (2021) · cites this paper as Cho et al., 2016
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- Authors
- Cho, H., Berger, B., & Peng, J.
- Affiliations
- MIT Computer Science and Artificial Intelligence Laboratory (US)
- University of Illinois Urbana-Champaign (US)
- Corresponding author
- Berger, Bonnie
- Peng, Jian
- Venue
- Cell Systems
- Publisher
- Elsevier BV
- Published
- 2016-12
- Volume
- 3
- Issue
- 6
- Pages
- 540-548.e5
- ISSN
- 2405-4712 (ISSN-L)
- PMC
- PMC5225290
- OpenAlex
- W2556519015
- Cited by
- 241
- References
- 68
- Citations by year
- 2026: 10 · 2025: 21 · 2024: 22 · 2023: 24 · 2022: 37 · 2021: 40 · 2020: 29 · 2019: 30 · 2018: 19 · 2017: 9
- FWCI
- 10.05
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Cell Systems (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by-nc-nd)
- Funders
- NIH (R01GM081871)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Biomedical Text Mining and Ontologies
- Keywords
- network integration, heterogeneous biological networks, diffusion analysis, topological patterns, low-dimensional embedding, protein function prediction
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