FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4312092983> ?p ?o ?g. }

• W4312092983 abstract "Abstract Despite extensive research efforts, reconstruction of gene regulatory networks (GRNs) from transcriptomics data remains a pressing challenge in systems biology. While non-linear approaches for reconstruction of GRNs show improved performance over simpler alternatives, we do not yet have understanding if joint modelling of multiple target genes may improve performance, even under linearity assumptions. To address this problem, we propose two novel approaches that cast the GRN reconstruction problem as a blend between regularized multivariate regression and graphical models that combine the L 2,1 -norm with classical regularization techniques. We used data and networks from the DREAM5 challenge to show that the proposed models provide consistently good performance in comparison to contenders whose performance varies with data sets from simulation and experiments from model unicellular organisms Escherichia coli and Saccharomyces cerevisiae . Since the models’ formulation facilitates the prediction of master regulators, we also used the resulting findings to identify master regulators over all data sets as well as their plasticity across different environments. Our results demonstrate that the identified master regulators are in line with experimental evidence from the model bacterium E. coli . Together, our study demonstrates that simultaneous modelling of several target genes results in improved inference of GRNs and can be used as an alternative in different applications. Author summary Reconstruction of cellular networks based on snapshots of molecular profiles of the network components has been one of the key challenges in systems biology. In the context of reconstruction of gene regulatory networks (GRNs), this problem translates into inferring regulatory relationships between transcription factor coding genes and their targets based on, often small, number of expression profiles. While unsupervised nonlinear machine learning approaches have shown better performance than regularized linear regression approaches, the existing modeling strategies usually do predictions of regulators for one target gene at a time. Here, we ask if and to what extent the joint modeling of regulation for multiple targets leads to improvement of the accuracy of the inferred GRNs. To address this question, we proposed, implemented, and compared the performance of models cast as a blend between regularized multivariate regression and graphical models that combine the L 2,1 -norm with classical regularization techniques. Our results demonstrate that the proposed models, despite relying on linearity assumptions, show consistently good performance in comparison to existing, widely used alternatives." @default.
• W4312092983 created "2023-01-04" @default.
• W4312092983 creator A5003878944 @default.
• W4312092983 creator A5059658943 @default.
• W4312092983 date "2022-12-22" @default.
• W4312092983 modified "2023-10-10" @default.
• W4312092983 title "Gene regulatory network inference using mixed-norms regularized multivariate model with covariance selection" @default.
• W4312092983 cites W1488435683 @default.
• W4312092983 cites W1980568723 @default.
• W4312092983 cites W2022067462 @default.
• W4312092983 cites W2037544231 @default.
• W4312092983 cites W2040769287 @default.
• W4312092983 cites W205654062 @default.
• W4312092983 cites W2059699217 @default.
• W4312092983 cites W2061425021 @default.
• W4312092983 cites W2061971803 @default.
• W4312092983 cites W2076513103 @default.
• W4312092983 cites W2081531193 @default.
• W4312092983 cites W2095622082 @default.
• W4312092983 cites W2099200059 @default.
• W4312092983 cites W2101717982 @default.
• W4312092983 cites W2102352719 @default.
• W4312092983 cites W2106487742 @default.
• W4312092983 cites W2109384743 @default.
• W4312092983 cites W2110265246 @default.
• W4312092983 cites W2112814716 @default.
• W4312092983 cites W2114382052 @default.
• W4312092983 cites W2115664087 @default.
• W4312092983 cites W2117179531 @default.
• W4312092983 cites W2120865735 @default.
• W4312092983 cites W2122718339 @default.
• W4312092983 cites W2130925862 @default.
• W4312092983 cites W2132555912 @default.
• W4312092983 cites W2136177512 @default.
• W4312092983 cites W2139997707 @default.
• W4312092983 cites W2142836135 @default.
• W4312092983 cites W2145227230 @default.
• W4312092983 cites W2161922735 @default.
• W4312092983 cites W2167171219 @default.
• W4312092983 cites W2169053773 @default.
• W4312092983 cites W2170448748 @default.
• W4312092983 cites W2230749025 @default.
• W4312092983 cites W2553107234 @default.
• W4312092983 cites W2571863445 @default.
• W4312092983 cites W2737543579 @default.
• W4312092983 cites W2800846444 @default.
• W4312092983 cites W2900310474 @default.
• W4312092983 cites W2921504004 @default.
• W4312092983 cites W2949641231 @default.
• W4312092983 cites W2997022986 @default.
• W4312092983 cites W3124607975 @default.
• W4312092983 cites W3162976634 @default.
• W4312092983 cites W4211118181 @default.
• W4312092983 cites W4221083556 @default.
• W4312092983 doi "https://doi.org/10.1101/2022.12.21.521538" @default.
• W4312092983 hasPublicationYear "2022" @default.
• W4312092983 type Work @default.
• W4312092983 citedByCount "0" @default.
• W4312092983 crossrefType "posted-content" @default.
• W4312092983 hasAuthorship W4312092983A5003878944 @default.
• W4312092983 hasAuthorship W4312092983A5059658943 @default.
• W4312092983 hasBestOaLocation W43120929831 @default.
• W4312092983 hasConcept C104317684 @default.
• W4312092983 hasConcept C105795698 @default.
• W4312092983 hasConcept C119857082 @default.
• W4312092983 hasConcept C124101348 @default.
• W4312092983 hasConcept C13280743 @default.
• W4312092983 hasConcept C150194340 @default.
• W4312092983 hasConcept C151730666 @default.
• W4312092983 hasConcept C152662350 @default.
• W4312092983 hasConcept C154945302 @default.
• W4312092983 hasConcept C155846161 @default.
• W4312092983 hasConcept C161584116 @default.
• W4312092983 hasConcept C178650346 @default.
• W4312092983 hasConcept C185798385 @default.
• W4312092983 hasConcept C199360897 @default.
• W4312092983 hasConcept C205649164 @default.
• W4312092983 hasConcept C207850805 @default.
• W4312092983 hasConcept C2776135515 @default.
• W4312092983 hasConcept C2776214188 @default.
• W4312092983 hasConcept C2779343474 @default.
• W4312092983 hasConcept C33923547 @default.
• W4312092983 hasConcept C41008148 @default.
• W4312092983 hasConcept C54355233 @default.
• W4312092983 hasConcept C67339327 @default.
• W4312092983 hasConcept C70721500 @default.
• W4312092983 hasConcept C81917197 @default.
• W4312092983 hasConcept C86803240 @default.
• W4312092983 hasConcept C93959086 @default.
• W4312092983 hasConceptScore W4312092983C104317684 @default.
• W4312092983 hasConceptScore W4312092983C105795698 @default.
• W4312092983 hasConceptScore W4312092983C119857082 @default.
• W4312092983 hasConceptScore W4312092983C124101348 @default.
• W4312092983 hasConceptScore W4312092983C13280743 @default.
• W4312092983 hasConceptScore W4312092983C150194340 @default.
• W4312092983 hasConceptScore W4312092983C151730666 @default.
• W4312092983 hasConceptScore W4312092983C152662350 @default.
• W4312092983 hasConceptScore W4312092983C154945302 @default.
• W4312092983 hasConceptScore W4312092983C155846161 @default.
• W4312092983 hasConceptScore W4312092983C161584116 @default.

• next