Matches in SemOpenAlex for { <https://semopenalex.org/work/W3100316918> ?p ?o ?g. }
- W3100316918 abstract "Abstract The interactions among the components of a living cell that constitute the gene regulatory network (GRN) can be inferred from perturbation-based gene expression data. Such networks are useful for providing mechanistic insights of a biological system. In order to explore the feasibility and quality of GRN inference at a large scale, we used the L1000 data where ~1000 genes have been perturbed and their expression levels have been quantified in 9 cancer cell lines. We found that these datasets have a very low signal-to-noise ratio (SNR) level causing them to be too uninformative to infer accurate GRNs. We developed a gene reduction pipeline in which we eliminate uninformative genes from the system using a selection criterion based on SNR, until reaching an informative subset. The results show that our pipeline can identify an informative subset in an overall uninformative dataset, allowing inference of accurate subset GRNs. The accurate GRNs were functionally characterized and potential novel cancer-related regulatory interactions were identified." @default.
- W3100316918 created "2020-11-23" @default.
- W3100316918 creator A5010008378 @default.
- W3100316918 creator A5021044817 @default.
- W3100316918 creator A5032432650 @default.
- W3100316918 creator A5039574191 @default.
- W3100316918 creator A5051667380 @default.
- W3100316918 creator A5080320054 @default.
- W3100316918 creator A5083934419 @default.
- W3100316918 date "2020-11-09" @default.
- W3100316918 modified "2023-09-23" @default.
- W3100316918 title "Uncovering cancer gene regulation by accurate regulatory network inference from uninformative data" @default.
- W3100316918 cites W1967545615 @default.
- W3100316918 cites W1969312124 @default.
- W3100316918 cites W1985213868 @default.
- W3100316918 cites W2003699768 @default.
- W3100316918 cites W2044525257 @default.
- W3100316918 cites W2052290565 @default.
- W3100316918 cites W2076513103 @default.
- W3100316918 cites W2081129357 @default.
- W3100316918 cites W2099740093 @default.
- W3100316918 cites W2099786058 @default.
- W3100316918 cites W2109384743 @default.
- W3100316918 cites W2129265801 @default.
- W3100316918 cites W2139997707 @default.
- W3100316918 cites W2142321063 @default.
- W3100316918 cites W2142635246 @default.
- W3100316918 cites W2168175751 @default.
- W3100316918 cites W2175370850 @default.
- W3100316918 cites W2345189580 @default.
- W3100316918 cites W2345639342 @default.
- W3100316918 cites W2558715006 @default.
- W3100316918 cites W2590615405 @default.
- W3100316918 cites W2608543022 @default.
- W3100316918 cites W2612467560 @default.
- W3100316918 cites W2622445425 @default.
- W3100316918 cites W2768151985 @default.
- W3100316918 cites W2782454362 @default.
- W3100316918 cites W2794115436 @default.
- W3100316918 cites W2806429407 @default.
- W3100316918 cites W2897805491 @default.
- W3100316918 cites W4233120011 @default.
- W3100316918 cites W4294541781 @default.
- W3100316918 cites W605677922 @default.
- W3100316918 cites W2893264376 @default.
- W3100316918 doi "https://doi.org/10.1038/s41540-020-00154-6" @default.
- W3100316918 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/7652823" @default.
- W3100316918 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/33168813" @default.
- W3100316918 hasPublicationYear "2020" @default.
- W3100316918 type Work @default.
- W3100316918 sameAs 3100316918 @default.
- W3100316918 citedByCount "11" @default.
- W3100316918 countsByYear W31003169182022 @default.
- W3100316918 countsByYear W31003169182023 @default.
- W3100316918 crossrefType "journal-article" @default.
- W3100316918 hasAuthorship W3100316918A5010008378 @default.
- W3100316918 hasAuthorship W3100316918A5021044817 @default.
- W3100316918 hasAuthorship W3100316918A5032432650 @default.
- W3100316918 hasAuthorship W3100316918A5039574191 @default.
- W3100316918 hasAuthorship W3100316918A5051667380 @default.
- W3100316918 hasAuthorship W3100316918A5080320054 @default.
- W3100316918 hasAuthorship W3100316918A5083934419 @default.
- W3100316918 hasBestOaLocation W31003169181 @default.
- W3100316918 hasConcept C104317684 @default.
- W3100316918 hasConcept C119857082 @default.
- W3100316918 hasConcept C124101348 @default.
- W3100316918 hasConcept C150194340 @default.
- W3100316918 hasConcept C154945302 @default.
- W3100316918 hasConcept C199360897 @default.
- W3100316918 hasConcept C2776214188 @default.
- W3100316918 hasConcept C41008148 @default.
- W3100316918 hasConcept C43521106 @default.
- W3100316918 hasConcept C54355233 @default.
- W3100316918 hasConcept C67339327 @default.
- W3100316918 hasConcept C70721500 @default.
- W3100316918 hasConcept C86803240 @default.
- W3100316918 hasConceptScore W3100316918C104317684 @default.
- W3100316918 hasConceptScore W3100316918C119857082 @default.
- W3100316918 hasConceptScore W3100316918C124101348 @default.
- W3100316918 hasConceptScore W3100316918C150194340 @default.
- W3100316918 hasConceptScore W3100316918C154945302 @default.
- W3100316918 hasConceptScore W3100316918C199360897 @default.
- W3100316918 hasConceptScore W3100316918C2776214188 @default.
- W3100316918 hasConceptScore W3100316918C41008148 @default.
- W3100316918 hasConceptScore W3100316918C43521106 @default.
- W3100316918 hasConceptScore W3100316918C54355233 @default.
- W3100316918 hasConceptScore W3100316918C67339327 @default.
- W3100316918 hasConceptScore W3100316918C70721500 @default.
- W3100316918 hasConceptScore W3100316918C86803240 @default.
- W3100316918 hasFunder F4320320940 @default.
- W3100316918 hasFunder F4320322795 @default.
- W3100316918 hasIssue "1" @default.
- W3100316918 hasLocation W31003169181 @default.
- W3100316918 hasLocation W31003169182 @default.
- W3100316918 hasLocation W31003169183 @default.
- W3100316918 hasOpenAccess W3100316918 @default.
- W3100316918 hasPrimaryLocation W31003169181 @default.
- W3100316918 hasRelatedWork W2103524869 @default.
- W3100316918 hasRelatedWork W2540716273 @default.
- W3100316918 hasRelatedWork W2909382709 @default.