New GO-based Measures and Their Statistical Significance in Multiple Network Alignment
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
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2023
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| _version_ | 1866916341931311104 |
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| author | Mousapour, Reza Yazdani, Kimia Hayes, Wayne B. |
| author_facet | Mousapour, Reza Yazdani, Kimia Hayes, Wayne B. |
| contents | Protein-protein interaction (PPI) networks provide valuable insights into the function of biological systems, and aligning multiple PPI networks can reveal important functional relationships between different species. However, assessing the quality of multiple network alignments is a challenging problem. In this paper, we propose two new measures, the Squared GO Score (SGS) and the Exposed G Score, to evaluate the quality of multiple network alignments while using functional information from Gene Ontology (GO) terms. We also introduce a $p$-value measure, the Statistical Exposed G Score, to compute the exact significance of a multiple network alignment based on its revealed GO terms. We also show that our measures are highly correlated with the recovered Ortholog count, providing further evidence for their effectiveness. Our work contributes to the development of more reliable and accurate measures for evaluating multiple network alignments and has potential applications in predicting gene function and identifying evolutionary relationships between different species using multiple network alignment. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2302_13440 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | New GO-based Measures and Their Statistical Significance in Multiple Network Alignment Mousapour, Reza Yazdani, Kimia Hayes, Wayne B. Molecular Networks Protein-protein interaction (PPI) networks provide valuable insights into the function of biological systems, and aligning multiple PPI networks can reveal important functional relationships between different species. However, assessing the quality of multiple network alignments is a challenging problem. In this paper, we propose two new measures, the Squared GO Score (SGS) and the Exposed G Score, to evaluate the quality of multiple network alignments while using functional information from Gene Ontology (GO) terms. We also introduce a $p$-value measure, the Statistical Exposed G Score, to compute the exact significance of a multiple network alignment based on its revealed GO terms. We also show that our measures are highly correlated with the recovered Ortholog count, providing further evidence for their effectiveness. Our work contributes to the development of more reliable and accurate measures for evaluating multiple network alignments and has potential applications in predicting gene function and identifying evolutionary relationships between different species using multiple network alignment. |
| title | New GO-based Measures and Their Statistical Significance in Multiple Network Alignment |
| topic | Molecular Networks |
| url | https://arxiv.org/abs/2302.13440 |