ArticleInternational journal of molecular sciences2019
Neglog: Homology-Based Negative Data Sampling Method for Genome-Scale Reconstruction of Human Protein-Protein Interaction Networks.
Article in International journal of molecular sciences, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 17 citations in OpenAlex.
- Integrating multi-omics to unravel host-microbiome interactions in inflammatory bowel disease.Cell reports. Medicine · 2024Review
- Artificial intelligence and neoantigens: paving the path for precision cancer immunotherapy.Frontiers in immunology · 2024Review
- Protein-protein interaction and non-interaction predictions using gene sequence natural vector.Communications biology · 2022Article
- Article
- Bioinformatics Methods for Mass Spectrometry-Based Proteomics Data Analysis.International journal of molecular sciences · 2020Review
- In silico unravelling pathogen-host signaling cross-talks via pathogen mimicry and human protein-protein interaction networks.Computational and structural biotechnology journal · 2020Article
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Authors and funding
2 authors at 2 institutions in 2 countries.
Funding
Abstract
Rapid reconstruction of genome-scale protein-protein interaction (PPI) networks is instrumental in understanding the cellular processes and disease pathogenesis and drug reactions. However, lack of experimentally verified negative data (i.e., pairs of proteins that do not interact) is still a major issue that needs to be properly addressed in computational modeling. In this study, we take advantage of the very limited experimentally verified negative data from Negatome to infer more negative data for computational modeling. We assume that the paralogs or orthologs of two non-interacting proteins also do not interact with high probability. We coin an assumption as "Neglog" this assumption is to some extent supported by paralogous/orthologous structure conservation. To reduce the risk of bias toward the negative data from Negatome, we combine Neglog with less biased random sampling according to a certain ratio to construct training data. L
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Registered trials
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