ExTRI: Extraction of transcription regulation interactions from literature
| Autor: | Miguel Vazquez, Martin Krallinger, Florian Leitner, Martin Kuiper, Alfonso Valencia, Astrid Laegreid |
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| Přispěvatelé: | Barcelona Supercomputing Center |
| Jazyk: | angličtina |
| Rok vydání: | 2022 |
| Předmět: |
Informàtica::Aplicacions de la informàtica::Bioinformàtica [Àrees temàtiques de la UPC]
Genetic transcription Biophysics Intel·ligència artificial--Aplicacions biològiques (Subd. geog.) Biochemistry Text-mining Gene regulation Text data mining Gene Expression Regulation Structural Biology Genetics Transcription factors Data Mining Intel·ligència artificial--Aplicacions a la medicina Informàtica::Intel·ligència artificial::Llenguatge natural [Àrees temàtiques de la UPC] Systems biology Molecular Biology Transcription Factors |
| Zdroj: | UPCommons. Portal del coneixement obert de la UPC Universitat Politècnica de Catalunya (UPC) |
| Popis: | The regulation of gene transcription by transcription factors is a fundamental biological process, yet the relations between transcription factors (TF) and their target genes (TG) are still only sparsely covered in databases. Text-mining tools can offer broad and complementary solutions to help locate and extract mentions of these biological relationships in articles. We have generated ExTRI, a knowledge graph of TF-TG relationships, by applying a high recall text-mining pipeline to MedLine abstracts identifying over 100,000 candidate sentences with TF-TG relations. Validation procedures indicated that about half of the candidate sentences contain true TF-TG relationships. Post-processing identified 53,000 high confidence sentences containing TF-TG relationships, with a cross-validation F1-score close to 75%. The resulting collection of TF-TG relationships covers 80% of the relations annotated in existing databases. It adds 11,000 other potential interactions, including relationships for ~100 TFs currently not in public TF-TG relation databases. The high confidence abstract sentences contribute 25,000 literature references not available from other resources and offer a wealth of direct pointers to functional aspects of the TF-TG interactions. Our compiled resource encompassing ExTRI together with publicly available resources delivers literature-derived TF-TG interactions for more than 900 of the 1500–1600 proteins considered to function as specific DNA binding TFs. The obtained result can be used by curators, for network analysis and modelling, for causal reasoning or knowledge graph mining approaches, or serve to benchmark text mining strategies. We thank the participants of the COST Action GREEKC (CA15205) for fruitful discussions during workshops supported by COST (European Cooperation in Science and Technology). |
| Databáze: | OpenAIRE |
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