{"external_id":{"isi":["000976610800001"],"pmid":["37004199"]},"publication_identifier":{"eissn":["1367-4811"]},"author":[{"full_name":"Beneš, Nikola","first_name":"Nikola","last_name":"Beneš"},{"full_name":"Brim, Luboš","first_name":"Luboš","last_name":"Brim"},{"full_name":"Huvar, Ondřej","last_name":"Huvar","first_name":"Ondřej"},{"first_name":"Samuel","last_name":"Pastva","full_name":"Pastva, Samuel","id":"07c5ea74-f61c-11ec-a664-aa7c5d957b2b"},{"full_name":"Šafránek, David","last_name":"Šafránek","first_name":"David"}],"publication":"Bioinformatics","has_accepted_license":"1","doi":"10.1093/bioinformatics/btad158","_id":"12876","article_number":"btad158","language":[{"iso":"eng"}],"article_type":"original","citation":{"short":"N. Beneš, L. Brim, O. Huvar, S. Pastva, D. Šafránek, Bioinformatics 39 (2023).","ista":"Beneš N, Brim L, Huvar O, Pastva S, Šafránek D. 2023. Boolean network sketches: A unifying framework for logical model inference. Bioinformatics. 39(4), btad158.","chicago":"Beneš, Nikola, Luboš Brim, Ondřej Huvar, Samuel Pastva, and David Šafránek. “Boolean Network Sketches: A Unifying Framework for Logical Model Inference.” <i>Bioinformatics</i>. Oxford Academic, 2023. <a href=\"https://doi.org/10.1093/bioinformatics/btad158\">https://doi.org/10.1093/bioinformatics/btad158</a>.","apa":"Beneš, N., Brim, L., Huvar, O., Pastva, S., &#38; Šafránek, D. (2023). Boolean network sketches: A unifying framework for logical model inference. <i>Bioinformatics</i>. Oxford Academic. <a href=\"https://doi.org/10.1093/bioinformatics/btad158\">https://doi.org/10.1093/bioinformatics/btad158</a>","ama":"Beneš N, Brim L, Huvar O, Pastva S, Šafránek D. Boolean network sketches: A unifying framework for logical model inference. <i>Bioinformatics</i>. 2023;39(4). doi:<a href=\"https://doi.org/10.1093/bioinformatics/btad158\">10.1093/bioinformatics/btad158</a>","ieee":"N. Beneš, L. Brim, O. Huvar, S. Pastva, and D. Šafránek, “Boolean network sketches: A unifying framework for logical model inference,” <i>Bioinformatics</i>, vol. 39, no. 4. Oxford Academic, 2023.","mla":"Beneš, Nikola, et al. “Boolean Network Sketches: A Unifying Framework for Logical Model Inference.” <i>Bioinformatics</i>, vol. 39, no. 4, btad158, Oxford Academic, 2023, doi:<a href=\"https://doi.org/10.1093/bioinformatics/btad158\">10.1093/bioinformatics/btad158</a>."},"ec_funded":1,"year":"2023","intvolume":"        39","volume":39,"project":[{"grant_number":"101034413","_id":"fc2ed2f7-9c52-11eb-aca3-c01059dda49c","name":"IST-BRIDGE: International postdoctoral program","call_identifier":"H2020"}],"ddc":["000"],"isi":1,"acknowledgement":"This work was partially supported by GACR [grant No. GA22-10845S]; and Grant Agency of Masaryk University [grant No. MUNI/G/1771/2020]. This work was partially supported by European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie [Grant Agreement No. 101034413 to S.P.].","file_date_updated":"2023-05-02T07:39:04Z","month":"04","article_processing_charge":"No","pmid":1,"date_updated":"2023-08-01T14:27:28Z","tmp":{"image":"/images/cc_by.png","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","short":"CC BY (4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode"},"type":"journal_article","file":[{"file_id":"12886","success":1,"file_size":478740,"date_created":"2023-05-02T07:39:04Z","date_updated":"2023-05-02T07:39:04Z","content_type":"application/pdf","file_name":"2023_Bioinformatics_Benes.pdf","access_level":"open_access","creator":"dernst","relation":"main_file","checksum":"2cb90ddf781baefddf47eac4b54e2a03"}],"day":"03","department":[{"_id":"ToHe"}],"abstract":[{"text":"Motivation: The problem of model inference is of fundamental importance to systems biology. Logical models (e.g. Boolean networks; BNs) represent a computationally attractive approach capable of handling large biological networks. The models are typically inferred from experimental data. However, even with a substantial amount of experimental data supported by some prior knowledge, existing inference methods often focus on a small sample of admissible candidate models only.\r\n\r\nResults: We propose Boolean network sketches as a new formal instrument for the inference of Boolean networks. A sketch integrates (typically partial) knowledge about the network’s topology and the update logic (obtained through, e.g. a biological knowledge base or a literature search), as well as further assumptions about the properties of the network’s transitions (e.g. the form of its attractor landscape), and additional restrictions on the model dynamics given by the measured experimental data. Our new BNs inference algorithm starts with an ‘initial’ sketch, which is extended by adding restrictions representing experimental data to a ‘data-informed’ sketch and subsequently computes all BNs consistent with the data-informed sketch. Our algorithm is based on a symbolic representation and coloured model-checking. Our approach is unique in its ability to cover a broad spectrum of knowledge and efficiently produce a compact representation of all inferred BNs. We evaluate the method on a non-trivial collection of real-world and simulated data.","lang":"eng"}],"publication_status":"published","date_published":"2023-04-03T00:00:00Z","issue":"4","publisher":"Oxford Academic","scopus_import":"1","oa_version":"Published Version","oa":1,"date_created":"2023-04-30T22:01:05Z","user_id":"4359f0d1-fa6c-11eb-b949-802e58b17ae8","title":"Boolean network sketches: A unifying framework for logical model inference","quality_controlled":"1","status":"public","related_material":{"link":[{"relation":"software","url":"https://doi.org/10.5281/zenodo.7688740"}]}}