2019
DOI: 10.1093/bioinformatics/btz132
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Abstract: Motivation Combining multiple layers of information underlying biological complexity into a structured framework represent a challenge in systems biology. A key task is the formalization of such information in models describing how biological entities interact to mediate the response to external and internal signals. Several databases with signalling information, focus on capturing, organizing and displaying signalling interactions by representing them as binary, causal relationships between … Show more

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Cited by 31 publications
(32 citation statements)
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“…In addition, relevant high throughput transcriptomic and chromatin immunoprecipitation data were analyzed to support the biological relevance of predicted HSF1 targets. The format of the HSF1base is PSI-MITAB 2.8, an extended version of the PSI-MI tab-delimited format [24]. This uses standardized expression data denoted with a well-defined ID, thereby facilitating the accurate use of the database for systems biology.…”
Section: Introductionmentioning
confidence: 99%
“…In addition, relevant high throughput transcriptomic and chromatin immunoprecipitation data were analyzed to support the biological relevance of predicted HSF1 targets. The format of the HSF1base is PSI-MITAB 2.8, an extended version of the PSI-MI tab-delimited format [24]. This uses standardized expression data denoted with a well-defined ID, thereby facilitating the accurate use of the database for systems biology.…”
Section: Introductionmentioning
confidence: 99%
“…(C) A vsm-box user-interface element that holds a template; plus functionality descriptions. For a vsm-box application, see CausalBuilder, which also produces MI2CAST and PSI-MITAB2.8 compliant data (Touré et al, 2020a(Touré et al, , 2020bPerfetto et al, 2019). taxon).…”
Section: Vsm-dictionaries Primermentioning
confidence: 99%
“…Yet since most physical interactions are known to be involved in regulatory processes, several knowledge base resources started to collect causal interactions by incorporating directionality information as well [4][5][6]. Therefore, the PSI-MI standard has been extended to also represent the causality of interactions through a direction and sign (up-or down-regulation) [7]. The extraction and annotation of causal interactions are predominantly performed via detailed manual curation of scientific publications [4]; but as techniques to infer causality through natural language processing [8] or 'omics data using prior knowledge [9][10][11] are also maturing, their results should also be supplied with the essential context details.…”
Section: Introductionmentioning
confidence: 99%
“…The extraction and annotation of causal interactions are predominantly performed via detailed manual curation of scientific publications [4]; but as techniques to infer causality through natural language processing [8] or 'omics data using prior knowledge [9][10][11] are also maturing, their results should also be supplied with the essential context details. Current formats of causal statements range from the simplest, with only two entities and the causal regulatory effect (e.g., the Simple Interaction Format (SIF) with "A activates B" or "A -> B"), to more complex statements including contextual description (e.g., BEL (Biological Expression Language, https://bel.bio/) [12], GO-CAM [13], and PSI-MITAB2.8 [7]). At present, various resources host molecular causal relationships (e.g., IntAct [14], SIGNOR [4,15], Causal Biological Network [16], SignaLink [5], TRRUST [17], TFacTS [18], DoRothEA [19]), each adhering to some of the formats mentioned above and annotated with specific controlled vocabularies (CVs) or ontologies (PSI-MI CV, Gene Ontology [20]).…”
Section: Introductionmentioning
confidence: 99%
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