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- W3010242829 abstract "Abstract Consider the functional interaction of gene A to an interaction subject X ; for instance, it is the gene-gene interaction if X represents for a gene, or gene-tissue interaction (expression status) if X for a tissue. In the simplest case, the status of this A-X interaction is r =1 if they are interacted, or r =0 otherwise. A fundamental problem in molecular evolution is, given two homologous (orthologous or paralogous) genes A and B , to what extent their functional overlapping could be by the means of interaction networks. Given a set of interaction subjects ( X 1 , … X N ), it is straightforward to calculate the interaction distance ( I AB ) between genes A and B , by a Markov-chain model. However, since the high throughput interaction data always involve a high level of noises, reliable inference of r= 1 or r =0 for each gene remains a big challenge. Consequently, the estimated interaction distance ( I AB ) is highly sensitive to the cutoff of interaction inference which is subject to some arbitrary. In this paper we will address this issue by developing a statistical method for estimating I AB based on the p -values (significant levels). Computer simulations are carried out to evaluate the performance of different p -value transformations against the uncertainty of interaction networks." @default.
- W3010242829 created "2020-03-13" @default.
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- W3010242829 date "2020-03-09" @default.
- W3010242829 modified "2023-09-23" @default.
- W3010242829 title "Evolutionary Distance of Gene-Gene Interactions: Estimation under Statistical Uncertainty" @default.
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- W3010242829 doi "https://doi.org/10.1101/2020.03.08.982710" @default.
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