FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2103673431> ?p ?o ?g. }

• W2103673431 endingPage "37" @default.
• W2103673431 startingPage "37" @default.
• W2103673431 abstract "The inference of gene regulatory networks (GRNs) from experimental observations is at the heart of systems biology. This includes the inference of both the network topology and its dynamics. While there are many algorithms available to infer the network topology from experimental data, less emphasis has been placed on methods that infer network dynamics. Furthermore, since the network inference problem is typically underdetermined, it is essential to have the option of incorporating into the inference process, prior knowledge about the network, along with an effective description of the search space of dynamic models. Finally, it is also important to have an understanding of how a given inference method is affected by experimental and other noise in the data used.This paper contains a novel inference algorithm using the algebraic framework of Boolean polynomial dynamical systems (BPDS), meeting all these requirements. The algorithm takes as input time series data, including those from network perturbations, such as knock-out mutant strains and RNAi experiments. It allows for the incorporation of prior biological knowledge while being robust to significant levels of noise in the data used for inference. It uses an evolutionary algorithm for local optimization with an encoding of the mathematical models as BPDS. The BPDS framework allows an effective representation of the search space for algebraic dynamic models that improves computational performance. The algorithm is validated with both simulated and experimental microarray expression profile data. Robustness to noise is tested using a published mathematical model of the segment polarity gene network in Drosophila melanogaster. Benchmarking of the algorithm is done by comparison with a spectrum of state-of-the-art network inference methods on data from the synthetic IRMA network to demonstrate that our method has good precision and recall for the network reconstruction task, while also predicting several of the dynamic patterns present in the network.Boolean polynomial dynamical systems provide a powerful modeling framework for the reverse engineering of gene regulatory networks, that enables a rich mathematical structure on the model search space. A C++ implementation of the method, distributed under LPGL license, is available, together with the source code, at http://www.paola-vera-licona.net/Software/EARevEng/REACT.html." @default.
• W2103673431 created "2016-06-24" @default.
• W2103673431 creator A5005340752 @default.
• W2103673431 creator A5032719130 @default.
• W2103673431 creator A5074945538 @default.
• W2103673431 creator A5083154892 @default.
• W2103673431 creator A5091010756 @default.
• W2103673431 date "2014-01-01" @default.
• W2103673431 modified "2023-09-27" @default.
• W2103673431 title "An algebra-based method for inferring gene regulatory networks" @default.
• W2103673431 cites W1970230237 @default.
• W2103673431 cites W1977692160 @default.
• W2103673431 cites W1986688807 @default.
• W2103673431 cites W1995522185 @default.
• W2103673431 cites W2014716295 @default.
• W2103673431 cites W2015271344 @default.
• W2103673431 cites W2025228038 @default.
• W2103673431 cites W2026726507 @default.
• W2103673431 cites W2027334863 @default.
• W2103673431 cites W2034241086 @default.
• W2103673431 cites W2036707090 @default.
• W2103673431 cites W2041658299 @default.
• W2103673431 cites W2043562679 @default.
• W2103673431 cites W2051262326 @default.
• W2103673431 cites W2076372398 @default.
• W2103673431 cites W2076382102 @default.
• W2103673431 cites W2088488298 @default.
• W2103673431 cites W2088926759 @default.
• W2103673431 cites W2090855185 @default.
• W2103673431 cites W2091934641 @default.
• W2103673431 cites W2100169396 @default.
• W2103673431 cites W2103723044 @default.
• W2103673431 cites W2106555403 @default.
• W2103673431 cites W2106927126 @default.
• W2103673431 cites W2108421561 @default.
• W2103673431 cites W2109102999 @default.
• W2103673431 cites W2109384743 @default.
• W2103673431 cites W2111932463 @default.
• W2103673431 cites W2114518254 @default.
• W2103673431 cites W2120064570 @default.
• W2103673431 cites W2121008927 @default.
• W2103673431 cites W2128374508 @default.
• W2103673431 cites W2128981863 @default.
• W2103673431 cites W2135816732 @default.
• W2103673431 cites W2142107342 @default.
• W2103673431 cites W2152563826 @default.
• W2103673431 cites W2156841851 @default.
• W2103673431 cites W2157121418 @default.
• W2103673431 cites W2160145365 @default.
• W2103673431 cites W2160533336 @default.
• W2103673431 cites W2161511352 @default.
• W2103673431 cites W2163833586 @default.
• W2103673431 cites W2166106382 @default.
• W2103673431 cites W2166913844 @default.
• W2103673431 cites W2168175751 @default.
• W2103673431 cites W2611370172 @default.
• W2103673431 doi "https://doi.org/10.1186/1752-0509-8-37" @default.
• W2103673431 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/4022379" @default.
• W2103673431 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/24669835" @default.
• W2103673431 hasPublicationYear "2014" @default.
• W2103673431 type Work @default.
• W2103673431 sameAs 2103673431 @default.
• W2103673431 citedByCount "26" @default.
• W2103673431 countsByYear W21036734312014 @default.
• W2103673431 countsByYear W21036734312015 @default.
• W2103673431 countsByYear W21036734312016 @default.
• W2103673431 countsByYear W21036734312017 @default.
• W2103673431 countsByYear W21036734312018 @default.
• W2103673431 countsByYear W21036734312019 @default.
• W2103673431 countsByYear W21036734312020 @default.
• W2103673431 countsByYear W21036734312021 @default.
• W2103673431 countsByYear W21036734312022 @default.
• W2103673431 crossrefType "journal-article" @default.
• W2103673431 hasAuthorship W2103673431A5005340752 @default.
• W2103673431 hasAuthorship W2103673431A5032719130 @default.
• W2103673431 hasAuthorship W2103673431A5074945538 @default.
• W2103673431 hasAuthorship W2103673431A5083154892 @default.
• W2103673431 hasAuthorship W2103673431A5091010756 @default.
• W2103673431 hasBestOaLocation W21036734311 @default.
• W2103673431 hasConcept C104317684 @default.
• W2103673431 hasConcept C11413529 @default.
• W2103673431 hasConcept C134444547 @default.
• W2103673431 hasConcept C150194340 @default.
• W2103673431 hasConcept C152662350 @default.
• W2103673431 hasConcept C154945302 @default.
• W2103673431 hasConcept C187455244 @default.
• W2103673431 hasConcept C2776214188 @default.
• W2103673431 hasConcept C28225019 @default.
• W2103673431 hasConcept C41008148 @default.
• W2103673431 hasConcept C55493867 @default.
• W2103673431 hasConcept C63479239 @default.
• W2103673431 hasConcept C67339327 @default.
• W2103673431 hasConcept C70721500 @default.
• W2103673431 hasConcept C80444323 @default.
• W2103673431 hasConcept C86803240 @default.
• W2103673431 hasConceptScore W2103673431C104317684 @default.
• W2103673431 hasConceptScore W2103673431C11413529 @default.
• W2103673431 hasConceptScore W2103673431C134444547 @default.

• next