[{"publication_status":"published","author":[{"first_name":"Van Giang","last_name":"Trinh","full_name":"Trinh, Van Giang"},{"full_name":"Park, Kyu Hyong","first_name":"Kyu Hyong","last_name":"Park"},{"full_name":"Pastva, Samuel","orcid":"0000-0003-1993-0331","first_name":"Samuel","id":"07c5ea74-f61c-11ec-a664-aa7c5d957b2b","last_name":"Pastva"},{"full_name":"Rozum, Jordan C.","first_name":"Jordan C.","last_name":"Rozum"}],"citation":{"ama":"Trinh VG, Park KH, Pastva S, Rozum JC. Mapping the attractor landscape of Boolean networks with biobalm. <i>Bioinformatics</i>. 2025;41(5). doi:<a href=\"https://doi.org/10.1093/bioinformatics/btaf280\">10.1093/bioinformatics/btaf280</a>","ieee":"V. G. Trinh, K. H. Park, S. Pastva, and J. C. Rozum, “Mapping the attractor landscape of Boolean networks with biobalm,” <i>Bioinformatics</i>, vol. 41, no. 5. Oxford University Press, 2025.","apa":"Trinh, V. G., Park, K. H., Pastva, S., &#38; Rozum, J. C. (2025). Mapping the attractor landscape of Boolean networks with biobalm. <i>Bioinformatics</i>. Oxford University Press. <a href=\"https://doi.org/10.1093/bioinformatics/btaf280\">https://doi.org/10.1093/bioinformatics/btaf280</a>","chicago":"Trinh, Van Giang, Kyu Hyong Park, Samuel Pastva, and Jordan C. Rozum. “Mapping the Attractor Landscape of Boolean Networks with Biobalm.” <i>Bioinformatics</i>. Oxford University Press, 2025. <a href=\"https://doi.org/10.1093/bioinformatics/btaf280\">https://doi.org/10.1093/bioinformatics/btaf280</a>.","short":"V.G. Trinh, K.H. Park, S. Pastva, J.C. Rozum, Bioinformatics 41 (2025).","ista":"Trinh VG, Park KH, Pastva S, Rozum JC. 2025. Mapping the attractor landscape of Boolean networks with biobalm. Bioinformatics. 41(5), btaf280.","mla":"Trinh, Van Giang, et al. “Mapping the Attractor Landscape of Boolean Networks with Biobalm.” <i>Bioinformatics</i>, vol. 41, no. 5, btaf280, Oxford University Press, 2025, doi:<a href=\"https://doi.org/10.1093/bioinformatics/btaf280\">10.1093/bioinformatics/btaf280</a>."},"OA_place":"publisher","corr_author":"1","project":[{"call_identifier":"H2020","name":"IST-BRIDGE: International postdoctoral program","_id":"fc2ed2f7-9c52-11eb-aca3-c01059dda49c","grant_number":"101034413"}],"title":"Mapping the attractor landscape of Boolean networks with biobalm","type":"journal_article","oa_version":"Published Version","_id":"19796","intvolume":"        41","department":[{"_id":"ToHe"}],"isi":1,"issue":"5","language":[{"iso":"eng"}],"acknowledgement":"V.-G.T. was supported by Institut Carnot STAR, Marseille, France. K.H.P. was supported by NSF grant MCB1715826 to Réka Albert. S.P. has received funding from the European Union’s Horizon 2020 Research and Innovation Programme under the Marie Sklodowska-Curie Grant Agreement No. 101034413. J.C.R. was supported by internal departmental funds provided by Luis M. Rocha. No funding bodies had any role in study design, analysis, decision to publish, or preparation of the article.","has_accepted_license":"1","user_id":"317138e5-6ab7-11ef-aa6d-ffef3953e345","related_material":{"link":[{"relation":"software","url":"https://github.com/jcrozum/biobalm"}],"record":[{"id":"19800","relation":"research_data","status":"public"}]},"scopus_import":"1","doi":"10.1093/bioinformatics/btaf280","day":"01","year":"2025","pmid":1,"external_id":{"isi":["001493400600001"],"pmid":["40327535"]},"tmp":{"name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","image":"/images/cc_by.png","short":"CC BY (4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode"},"file":[{"access_level":"open_access","creator":"dernst","date_created":"2025-06-10T07:07:45Z","success":1,"file_id":"19801","relation":"main_file","date_updated":"2025-06-10T07:07:45Z","file_name":"2025_Bioinformatics_Trinh.pdf","checksum":"fa9d68aa0f5ce37598a623c9be936f09","content_type":"application/pdf","file_size":2695801}],"date_updated":"2025-09-30T12:46:33Z","date_published":"2025-05-01T00:00:00Z","publication":"Bioinformatics","ec_funded":1,"DOAJ_listed":"1","publication_identifier":{"eissn":["1367-4811"]},"article_number":"btaf280","ddc":["000"],"article_type":"original","file_date_updated":"2025-06-10T07:07:45Z","publisher":"Oxford University Press","article_processing_charge":"Yes","volume":41,"oa":1,"OA_type":"gold","date_created":"2025-06-08T22:01:22Z","quality_controlled":"1","abstract":[{"text":"Motivation: Boolean networks are popular dynamical models of cellular processes in systems biology. Their attractors model phenotypes that arise from the interplay of key regulatory subcircuits. A succession diagram (SD) describes this interplay in a discrete analog of Waddington’s epigenetic attractor landscape that allows for fast identification of attractors and attractor control strategies. Efficient computational tools for studying SDs are essential for the understanding of Boolean attractor landscapes and connecting them to their biological functions.\r\nResults: We present a new approach to SD construction for asynchronously updated Boolean networks, implemented in the biologist’s Boolean attractor landscape mapper, biobalm. We compare biobalm to similar tools and find a substantial performance increase in SD construction, attractor identification, and attractor control. We perform the most comprehensive comparative analysis to date of the SD structure in experimentally-validated Boolean models of cell processes and random ensembles. We find that random models (including critical Kauffman networks) have relatively small SDs, indicating simple decision structures. In contrast, nonrandom models from the literature are enriched in extremely large SDs, indicating an abundance of decision points and suggesting the presence of complex Waddington landscapes in nature.\r\nAvailability and implementation: The tool biobalm is available online at https://github.com/jcrozum/biobalm. Further data, scripts for testing, analysis, and figure generation are available online at https://github.com/jcrozum/biobalm-analysis and in the reproducibility artefact at https://doi.org/10.5281/zenodo.13854760.","lang":"eng"}],"status":"public","month":"05"},{"article_processing_charge":"No","volume":39,"date_created":"2023-04-30T22:01:05Z","oa":1,"quality_controlled":"1","ddc":["000"],"article_number":"btad158","file_date_updated":"2023-05-02T07:39:04Z","article_type":"original","publisher":"Oxford University Press","abstract":[{"lang":"eng","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."}],"status":"public","month":"04","publication_identifier":{"eissn":["1367-4811"]},"publication":"Bioinformatics","ec_funded":1,"has_accepted_license":"1","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","related_material":{"link":[{"url":"https://doi.org/10.5281/zenodo.7688740","relation":"software"}]},"scopus_import":"1","isi":1,"issue":"4","language":[{"iso":"eng"}],"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.].","tmp":{"name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","image":"/images/cc_by.png","short":"CC BY (4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode"},"external_id":{"pmid":["37004199"],"isi":["000976610800001"]},"file":[{"date_updated":"2023-05-02T07:39:04Z","relation":"main_file","file_size":478740,"file_name":"2023_Bioinformatics_Benes.pdf","checksum":"2cb90ddf781baefddf47eac4b54e2a03","content_type":"application/pdf","creator":"dernst","date_created":"2023-05-02T07:39:04Z","access_level":"open_access","file_id":"12886","success":1}],"date_published":"2023-04-03T00:00:00Z","date_updated":"2025-05-14T11:06:50Z","doi":"10.1093/bioinformatics/btad158","day":"03","year":"2023","pmid":1,"author":[{"first_name":"Nikola","last_name":"Beneš","full_name":"Beneš, Nikola"},{"first_name":"Luboš","last_name":"Brim","full_name":"Brim, Luboš"},{"full_name":"Huvar, Ondřej","first_name":"Ondřej","last_name":"Huvar"},{"orcid":"0000-0003-1993-0331","full_name":"Pastva, Samuel","first_name":"Samuel","last_name":"Pastva","id":"07c5ea74-f61c-11ec-a664-aa7c5d957b2b"},{"first_name":"David","last_name":"Šafránek","full_name":"Šafránek, David"}],"citation":{"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 University Press, 2023.","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 University Press. <a href=\"https://doi.org/10.1093/bioinformatics/btad158\">https://doi.org/10.1093/bioinformatics/btad158</a>","mla":"Beneš, Nikola, et al. “Boolean Network Sketches: A Unifying Framework for Logical Model Inference.” <i>Bioinformatics</i>, vol. 39, no. 4, btad158, Oxford University Press, 2023, doi:<a href=\"https://doi.org/10.1093/bioinformatics/btad158\">10.1093/bioinformatics/btad158</a>.","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 University Press, 2023. <a href=\"https://doi.org/10.1093/bioinformatics/btad158\">https://doi.org/10.1093/bioinformatics/btad158</a>.","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."},"publication_status":"published","project":[{"name":"IST-BRIDGE: International postdoctoral program","_id":"fc2ed2f7-9c52-11eb-aca3-c01059dda49c","call_identifier":"H2020","grant_number":"101034413"}],"title":"Boolean network sketches: A unifying framework for logical model inference","_id":"12876","department":[{"_id":"ToHe"}],"intvolume":"        39","type":"journal_article","oa_version":"Published Version"},{"ec_funded":1,"publication":"Bioinformatics","publication_identifier":{"eissn":["1367-4811"],"issn":["1367-4803"]},"publisher":"Oxford University Press","file_date_updated":"2023-07-31T11:09:05Z","article_type":"original","ddc":["000"],"quality_controlled":"1","oa":1,"date_created":"2023-07-23T22:01:12Z","volume":39,"article_processing_charge":"Yes","month":"06","status":"public","abstract":[{"lang":"eng","text":"Motivation: Boolean networks are simple but efficient mathematical formalism for modelling complex biological systems. However, having only two levels of activation is sometimes not enough to fully capture the dynamics of real-world biological systems. Hence, the need for multi-valued networks (MVNs), a generalization of Boolean networks. Despite the importance of MVNs for modelling biological systems, only limited progress has been made on developing theories, analysis methods, and tools that can support them. In particular, the recent use of trap spaces in Boolean networks made a great impact on the field of systems biology, but there has been no similar concept defined and studied for MVNs to date.\r\n\r\nResults: In this work, we generalize the concept of trap spaces in Boolean networks to that in MVNs. We then develop the theory and the analysis methods for trap spaces in MVNs. In particular, we implement all proposed methods in a Python package called trapmvn. Not only showing the applicability of our approach via a realistic case study, we also evaluate the time efficiency of the method on a large collection of real-world models. The experimental results confirm the time efficiency, which we believe enables more accurate analysis on larger and more complex multi-valued models."}],"page":"i513-i522","title":"Trap spaces of multi-valued networks: Definition, computation, and applications","corr_author":"1","project":[{"_id":"fc2ed2f7-9c52-11eb-aca3-c01059dda49c","name":"IST-BRIDGE: International postdoctoral program","call_identifier":"H2020","grant_number":"101034413"}],"author":[{"first_name":"Van Giang","last_name":"Trinh","full_name":"Trinh, Van Giang"},{"first_name":"Belaid","last_name":"Benhamou","full_name":"Benhamou, Belaid"},{"full_name":"Henzinger, Thomas A","orcid":"0000-0002-2985-7724","last_name":"Henzinger","id":"40876CD8-F248-11E8-B48F-1D18A9856A87","first_name":"Thomas A"},{"last_name":"Pastva","id":"07c5ea74-f61c-11ec-a664-aa7c5d957b2b","first_name":"Samuel","orcid":"0000-0003-1993-0331","full_name":"Pastva, Samuel"}],"citation":{"ama":"Trinh VG, Benhamou B, Henzinger TA, Pastva S. Trap spaces of multi-valued networks: Definition, computation, and applications. <i>Bioinformatics</i>. 2023;39(Supplement_1):i513-i522. doi:<a href=\"https://doi.org/10.1093/bioinformatics/btad262\">10.1093/bioinformatics/btad262</a>","ieee":"V. G. Trinh, B. Benhamou, T. A. Henzinger, and S. Pastva, “Trap spaces of multi-valued networks: Definition, computation, and applications,” <i>Bioinformatics</i>, vol. 39, no. Supplement_1. Oxford University Press, pp. i513–i522, 2023.","apa":"Trinh, V. G., Benhamou, B., Henzinger, T. A., &#38; Pastva, S. (2023). Trap spaces of multi-valued networks: Definition, computation, and applications. <i>Bioinformatics</i>. Oxford University Press. <a href=\"https://doi.org/10.1093/bioinformatics/btad262\">https://doi.org/10.1093/bioinformatics/btad262</a>","mla":"Trinh, Van Giang, et al. “Trap Spaces of Multi-Valued Networks: Definition, Computation, and Applications.” <i>Bioinformatics</i>, vol. 39, no. Supplement_1, Oxford University Press, 2023, pp. i513–22, doi:<a href=\"https://doi.org/10.1093/bioinformatics/btad262\">10.1093/bioinformatics/btad262</a>.","chicago":"Trinh, Van Giang, Belaid Benhamou, Thomas A Henzinger, and Samuel Pastva. “Trap Spaces of Multi-Valued Networks: Definition, Computation, and Applications.” <i>Bioinformatics</i>. Oxford University Press, 2023. <a href=\"https://doi.org/10.1093/bioinformatics/btad262\">https://doi.org/10.1093/bioinformatics/btad262</a>.","short":"V.G. Trinh, B. Benhamou, T.A. Henzinger, S. Pastva, Bioinformatics 39 (2023) i513–i522.","ista":"Trinh VG, Benhamou B, Henzinger TA, Pastva S. 2023. Trap spaces of multi-valued networks: Definition, computation, and applications. Bioinformatics. 39(Supplement_1), i513–i522."},"publication_status":"published","oa_version":"Published Version","type":"journal_article","department":[{"_id":"ToHe"}],"intvolume":"        39","_id":"13263","acknowledgement":"This work was supported by L’Institut Carnot STAR, Marseille, France, and by the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie Grant Agreement No. [101034413].","language":[{"iso":"eng"}],"issue":"Supplement_1","isi":1,"related_material":{"link":[{"url":"https://github.com/giang-trinh/trap-mvn","relation":"software"}]},"scopus_import":"1","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","has_accepted_license":"1","pmid":1,"year":"2023","day":"30","doi":"10.1093/bioinformatics/btad262","date_published":"2023-06-30T00:00:00Z","date_updated":"2025-05-14T11:07:28Z","tmp":{"name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","image":"/images/cc_by.png","short":"CC BY (4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode"},"external_id":{"pmid":["37387165"],"isi":["001027457000060"]},"file":[{"date_updated":"2023-07-31T11:09:05Z","relation":"main_file","file_size":641736,"checksum":"ba3abe1171df1958413b7c7f957f5486","file_name":"2023_Bioinformatics_Trinh.pdf","content_type":"application/pdf","creator":"dernst","date_created":"2023-07-31T11:09:05Z","access_level":"open_access","file_id":"13335","success":1}]},{"keyword":["Computational Mathematics","Computational Theory and Mathematics","Computer Science Applications","Molecular Biology","Biochemistry","Statistics and Probability"],"related_material":{"link":[{"url":"https://github.com/ratschlab/scim","relation":"software"}]},"scopus_import":"1","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","language":[{"iso":"eng"}],"issue":"Supplement_2","main_file_link":[{"url":"https://doi.org/10.1093/bioinformatics/btaa843","open_access":"1"}],"date_updated":"2023-09-11T10:21:00Z","date_published":"2020-12-01T00:00:00Z","external_id":{"pmid":["33381818"]},"pmid":1,"year":"2020","day":"01","doi":"10.1093/bioinformatics/btaa843","title":"SCIM: Universal single-cell matching with unpaired feature sets","extern":"1","citation":{"ieee":"S. G. Stark <i>et al.</i>, “SCIM: Universal single-cell matching with unpaired feature sets,” <i>Bioinformatics</i>, vol. 36, no. Supplement_2. Oxford University Press, pp. i919–i927, 2020.","apa":"Stark, S. G., Ficek, J., Locatello, F., Bonilla, X., Chevrier, S., Singer, F., … Lehmann, K.-V. (2020). SCIM: Universal single-cell matching with unpaired feature sets. <i>Bioinformatics</i>. Oxford University Press. <a href=\"https://doi.org/10.1093/bioinformatics/btaa843\">https://doi.org/10.1093/bioinformatics/btaa843</a>","ama":"Stark SG, Ficek J, Locatello F, et al. SCIM: Universal single-cell matching with unpaired feature sets. <i>Bioinformatics</i>. 2020;36(Supplement_2):i919-i927. doi:<a href=\"https://doi.org/10.1093/bioinformatics/btaa843\">10.1093/bioinformatics/btaa843</a>","ista":"Stark SG et al. 2020. SCIM: Universal single-cell matching with unpaired feature sets. Bioinformatics. 36(Supplement_2), i919–i927.","chicago":"Stark, Stefan G, Joanna Ficek, Francesco Locatello, Ximena Bonilla, Stéphane Chevrier, Franziska Singer, Rudolf Aebersold, et al. “SCIM: Universal Single-Cell Matching with Unpaired Feature Sets.” <i>Bioinformatics</i>. Oxford University Press, 2020. <a href=\"https://doi.org/10.1093/bioinformatics/btaa843\">https://doi.org/10.1093/bioinformatics/btaa843</a>.","short":"S.G. Stark, J. Ficek, F. Locatello, X. Bonilla, S. Chevrier, F. Singer, R. Aebersold, F.S. Al-Quaddoomi, J. Albinus, I. Alborelli, S. Andani, P.-O. Attinger, M. Bacac, D. Baumhoer, B. Beck-Schimmer, N. Beerenwinkel, C. Beisel, L. Bernasconi, A. Bertolini, B. Bodenmiller, X. Bonilla, R. Casanova, S. Chevrier, N. Chicherova, M. D’Costa, E. Danenberg, N. Davidson, M.-A.D. gan, R. Dummer, S. Engler, M. Erkens, K. Eschbach, C. Esposito, A. Fedier, P. Ferreira, J. Ficek, A.L. Frei, B. Frey, S. Goetze, L. Grob, G. Gut, D. Günther, M. Haberecker, P. Haeuptle, V. Heinzelmann-Schwarz, S. Herter, R. Holtackers, T. Huesser, A. Irmisch, F. Jacob, A. Jacobs, T.M. Jaeger, K. Jahn, A.R. James, P.M. Jermann, A. Kahles, A. Kahraman, V.H. Koelzer, W. Kuebler, J. Kuipers, C.P. Kunze, C. Kurzeder, K.-V. Lehmann, M. Levesque, S. Lugert, G. Maass, M. Manz, P. Markolin, J. Mena, U. Menzel, J.M. Metzler, N. Miglino, E.S. Milani, H. Moch, S. Muenst, R. Murri, C.K. Ng, S. Nicolet, M. Nowak, P.G. Pedrioli, L. Pelkmans, S. Piscuoglio, M. Prummer, M. Ritter, C. Rommel, M.L. Rosano-González, G. Rätsch, N. Santacroce, J.S. del Castillo, R. Schlenker, P.C. Schwalie, S. Schwan, T. Schär, G. Senti, F. Singer, S. Sivapatham, B. Snijder, B. Sobottka, V.T. Sreedharan, S. Stark, D.J. Stekhoven, A.P. Theocharides, T.M. Thomas, M. Tolnay, V. Tosevski, N.C. Toussaint, M.A. Tuncel, M. Tusup, A.V. Drogen, M. Vetter, T. Vlajnic, S. Weber, W.P. Weber, R. Wegmann, M. Weller, F. Wendt, N. Wey, A. Wicki, B. Wollscheid, S. Yu, J. Ziegler, M. Zimmermann, M. Zoche, G. Zuend, G. Rätsch, K.-V. Lehmann, Bioinformatics 36 (2020) i919–i927.","mla":"Stark, Stefan G., et al. “SCIM: Universal Single-Cell Matching with Unpaired Feature Sets.” <i>Bioinformatics</i>, vol. 36, no. Supplement_2, Oxford University Press, 2020, pp. i919–27, doi:<a href=\"https://doi.org/10.1093/bioinformatics/btaa843\">10.1093/bioinformatics/btaa843</a>."},"publication_status":"published","author":[{"first_name":"Stefan G","last_name":"Stark","full_name":"Stark, Stefan G"},{"first_name":"Joanna","last_name":"Ficek","full_name":"Ficek, Joanna"},{"id":"26cfd52f-2483-11ee-8040-88983bcc06d4","last_name":"Locatello","first_name":"Francesco","full_name":"Locatello, Francesco","orcid":"0000-0002-4850-0683"},{"full_name":"Bonilla, Ximena","first_name":"Ximena","last_name":"Bonilla"},{"first_name":"Stéphane","last_name":"Chevrier","full_name":"Chevrier, Stéphane"},{"full_name":"Singer, Franziska","first_name":"Franziska","last_name":"Singer"},{"last_name":"Aebersold","first_name":"Rudolf","full_name":"Aebersold, Rudolf"},{"last_name":"Al-Quaddoomi","first_name":"Faisal S","full_name":"Al-Quaddoomi, Faisal S"},{"first_name":"Jonas","last_name":"Albinus","full_name":"Albinus, Jonas"},{"full_name":"Alborelli, Ilaria","last_name":"Alborelli","first_name":"Ilaria"},{"last_name":"Andani","first_name":"Sonali","full_name":"Andani, Sonali"},{"full_name":"Attinger, Per-Olof","first_name":"Per-Olof","last_name":"Attinger"},{"full_name":"Bacac, Marina","first_name":"Marina","last_name":"Bacac"},{"full_name":"Baumhoer, Daniel","last_name":"Baumhoer","first_name":"Daniel"},{"last_name":"Beck-Schimmer","first_name":"Beatrice","full_name":"Beck-Schimmer, Beatrice"},{"first_name":"Niko","last_name":"Beerenwinkel","full_name":"Beerenwinkel, Niko"},{"full_name":"Beisel, Christian","last_name":"Beisel","first_name":"Christian"},{"full_name":"Bernasconi, Lara","first_name":"Lara","last_name":"Bernasconi"},{"first_name":"Anne","last_name":"Bertolini","full_name":"Bertolini, Anne"},{"last_name":"Bodenmiller","first_name":"Bernd","full_name":"Bodenmiller, Bernd"},{"full_name":"Bonilla, Ximena","first_name":"Ximena","last_name":"Bonilla"},{"full_name":"Casanova, Ruben","first_name":"Ruben","last_name":"Casanova"},{"last_name":"Chevrier","first_name":"Stéphane","full_name":"Chevrier, Stéphane"},{"last_name":"Chicherova","first_name":"Natalia","full_name":"Chicherova, Natalia"},{"full_name":"D'Costa, Maya","last_name":"D'Costa","first_name":"Maya"},{"full_name":"Danenberg, Esther","last_name":"Danenberg","first_name":"Esther"},{"first_name":"Natalie","last_name":"Davidson","full_name":"Davidson, Natalie"},{"last_name":"gan","first_name":"Monica-Andreea Dră","full_name":"gan, Monica-Andreea Dră"},{"full_name":"Dummer, Reinhard","first_name":"Reinhard","last_name":"Dummer"},{"first_name":"Stefanie","last_name":"Engler","full_name":"Engler, Stefanie"},{"full_name":"Erkens, Martin","last_name":"Erkens","first_name":"Martin"},{"last_name":"Eschbach","first_name":"Katja","full_name":"Eschbach, Katja"},{"full_name":"Esposito, Cinzia","first_name":"Cinzia","last_name":"Esposito"},{"full_name":"Fedier, 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Marina"},{"full_name":"Drogen, Audrey Van","first_name":"Audrey Van","last_name":"Drogen"},{"full_name":"Vetter, Marcus","last_name":"Vetter","first_name":"Marcus"},{"last_name":"Vlajnic","first_name":"Tatjana","full_name":"Vlajnic, Tatjana"},{"last_name":"Weber","first_name":"Sandra","full_name":"Weber, Sandra"},{"first_name":"Walter P","last_name":"Weber","full_name":"Weber, Walter P"},{"last_name":"Wegmann","first_name":"Rebekka","full_name":"Wegmann, Rebekka"},{"first_name":"Michael","last_name":"Weller","full_name":"Weller, Michael"},{"full_name":"Wendt, Fabian","last_name":"Wendt","first_name":"Fabian"},{"full_name":"Wey, Norbert","last_name":"Wey","first_name":"Norbert"},{"last_name":"Wicki","first_name":"Andreas","full_name":"Wicki, Andreas"},{"last_name":"Wollscheid","first_name":"Bernd","full_name":"Wollscheid, Bernd"},{"last_name":"Yu","first_name":"Shuqing","full_name":"Yu, Shuqing"},{"first_name":"Johanna","last_name":"Ziegler","full_name":"Ziegler, Johanna"},{"last_name":"Zimmermann","first_name":"Marc","full_name":"Zimmermann, Marc"},{"last_name":"Zoche","first_name":"Martin","full_name":"Zoche, Martin"},{"last_name":"Zuend","first_name":"Gregor","full_name":"Zuend, Gregor"},{"full_name":"Rätsch, Gunnar","first_name":"Gunnar","last_name":"Rätsch"},{"first_name":"Kjong-Van","last_name":"Lehmann","full_name":"Lehmann, Kjong-Van"}],"intvolume":"        36","department":[{"_id":"FrLo"}],"_id":"14125","oa_version":"Published Version","type":"journal_article","quality_controlled":"1","oa":1,"date_created":"2023-08-21T12:28:20Z","article_processing_charge":"No","volume":36,"publisher":"Oxford University Press","article_type":"original","page":"i919-i927","month":"12","status":"public","abstract":[{"text":"Motivation: Recent technological advances have led to an increase in the production and availability of single-cell data. The ability to integrate a set of multi-technology measurements would allow the identification of biologically or clinically meaningful observations through the unification of the perspectives afforded by each technology. In most cases, however, profiling technologies consume the used cells and thus pairwise correspondences between datasets are lost. Due to the sheer size single-cell datasets can acquire, scalable algorithms that are able to universally match single-cell measurements carried out in one cell to its corresponding sibling in another technology are needed.\r\nResults: We propose Single-Cell data Integration via Matching (SCIM), a scalable approach to recover such correspondences in two or more technologies. SCIM assumes that cells share a common (low-dimensional) underlying structure and that the underlying cell distribution is approximately constant across technologies. It constructs a technology-invariant latent space using an autoencoder framework with an adversarial objective. Multi-modal datasets are integrated by pairing cells across technologies using a bipartite matching scheme that operates on the low-dimensional latent representations. We evaluate SCIM on a simulated cellular branching process and show that the cell-to-cell matches derived by SCIM reflect the same pseudotime on the simulated dataset. Moreover, we apply our method to two real-world scenarios, a melanoma tumor sample and a human bone marrow sample, where we pair cells from a scRNA dataset to their sibling cells in a CyTOF dataset achieving 90% and 78% cell-matching accuracy for each one of the samples, respectively.","lang":"eng"}],"publication_identifier":{"eissn":["1367-4811"]},"publication":"Bioinformatics"},{"doi":"10.1093/bioinformatics/bty340","pmid":1,"day":"01","year":"2018","tmp":{"legal_code_url":"https://creativecommons.org/licenses/by-nc/4.0/legalcode","short":"CC BY-NC (4.0)","image":"/images/cc_by_nc.png","name":"Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)"},"external_id":{"isi":["000450038900008"],"pmid":["29722803"]},"file":[{"relation":"main_file","date_updated":"2020-07-14T12:47:15Z","file_name":"2018_Oxford_Usmanova.pdf","checksum":"7e0495153f44211479674601d7f6ee03","content_type":"application/pdf","file_size":291969,"access_level":"open_access","creator":"kschuh","date_created":"2019-02-14T13:00:55Z","file_id":"5997"}],"date_published":"2018-11-01T00:00:00Z","date_updated":"2026-07-06T13:48:02Z","issue":"21","isi":1,"language":[{"iso":"eng"}],"scopus_import":"1","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","has_accepted_license":"1","type":"journal_article","oa_version":"Published Version","_id":"5995","das_tickbox":"1","department":[{"_id":"FyKo"}],"intvolume":"        34","project":[{"grant_number":"335980","_id":"26120F5C-B435-11E9-9278-68D0E5697425","name":"Systematic investigation of epistasis in molecular evolution","call_identifier":"FP7"}],"citation":{"short":"D.R. Usmanova, N.S. Bogatyreva, J. Ariño Bernad, A.A. Eremina, A.A. Gorshkova, G.M. Kanevskiy, L.R. Lonishin, A.V. Meister, A.G. Yakupova, F. Kondrashov, D. Ivankov, Bioinformatics 34 (2018) 3653–3658.","chicago":"Usmanova, Dinara R, Natalya S Bogatyreva, Joan Ariño Bernad, Aleksandra A Eremina, Anastasiya A Gorshkova, German M Kanevskiy, Lyubov R Lonishin, et al. “Self-Consistency Test Reveals Systematic Bias in Programs for Prediction Change of Stability upon Mutation.” <i>Bioinformatics</i>. Oxford University Press, 2018. <a href=\"https://doi.org/10.1093/bioinformatics/bty340\">https://doi.org/10.1093/bioinformatics/bty340</a>.","ista":"Usmanova DR, Bogatyreva NS, Ariño Bernad J, Eremina AA, Gorshkova AA, Kanevskiy GM, Lonishin LR, Meister AV, Yakupova AG, Kondrashov F, Ivankov D. 2018. Self-consistency test reveals systematic bias in programs for prediction change of stability upon mutation. Bioinformatics. 34(21), 3653–3658.","mla":"Usmanova, Dinara R., et al. “Self-Consistency Test Reveals Systematic Bias in Programs for Prediction Change of Stability upon Mutation.” <i>Bioinformatics</i>, vol. 34, no. 21, Oxford University Press, 2018, pp. 3653–58, doi:<a href=\"https://doi.org/10.1093/bioinformatics/bty340\">10.1093/bioinformatics/bty340</a>.","ama":"Usmanova DR, Bogatyreva NS, Ariño Bernad J, et al. Self-consistency test reveals systematic bias in programs for prediction change of stability upon mutation. <i>Bioinformatics</i>. 2018;34(21):3653-3658. doi:<a href=\"https://doi.org/10.1093/bioinformatics/bty340\">10.1093/bioinformatics/bty340</a>","apa":"Usmanova, D. R., Bogatyreva, N. S., Ariño Bernad, J., Eremina, A. A., Gorshkova, A. A., Kanevskiy, G. M., … Ivankov, D. (2018). Self-consistency test reveals systematic bias in programs for prediction change of stability upon mutation. <i>Bioinformatics</i>. Oxford University Press. <a href=\"https://doi.org/10.1093/bioinformatics/bty340\">https://doi.org/10.1093/bioinformatics/bty340</a>","ieee":"D. R. Usmanova <i>et al.</i>, “Self-consistency test reveals systematic bias in programs for prediction change of stability upon mutation,” <i>Bioinformatics</i>, vol. 34, no. 21. Oxford University Press, pp. 3653–3658, 2018."},"publication_status":"published","author":[{"last_name":"Usmanova","first_name":"Dinara R","full_name":"Usmanova, Dinara R"},{"last_name":"Bogatyreva","first_name":"Natalya S","full_name":"Bogatyreva, Natalya S"},{"last_name":"Ariño Bernad","first_name":"Joan","full_name":"Ariño Bernad, Joan"},{"last_name":"Eremina","first_name":"Aleksandra A","full_name":"Eremina, Aleksandra A"},{"last_name":"Gorshkova","first_name":"Anastasiya A","full_name":"Gorshkova, Anastasiya A"},{"first_name":"German M","last_name":"Kanevskiy","full_name":"Kanevskiy, German M"},{"last_name":"Lonishin","first_name":"Lyubov R","full_name":"Lonishin, Lyubov R"},{"first_name":"Alexander V","last_name":"Meister","full_name":"Meister, Alexander V"},{"full_name":"Yakupova, Alisa G","last_name":"Yakupova","first_name":"Alisa G"},{"id":"44FDEF62-F248-11E8-B48F-1D18A9856A87","last_name":"Kondrashov","first_name":"Fyodor","orcid":"0000-0001-8243-4694","full_name":"Kondrashov, Fyodor"},{"first_name":"Dmitry","id":"49FF1036-F248-11E8-B48F-1D18A9856A87","last_name":"Ivankov","orcid":"0000-0002-8224-4118","full_name":"Ivankov, Dmitry"}],"title":"Self-consistency test reveals systematic bias in programs for prediction change of stability upon mutation","status":"public","abstract":[{"text":"Motivation\r\nComputational prediction of the effect of mutations on protein stability is used by researchers in many fields. The utility of the prediction methods is affected by their accuracy and bias. Bias, a systematic shift of the predicted change of stability, has been noted as an issue for several methods, but has not been investigated systematically. Presence of the bias may lead to misleading results especially when exploring the effects of combination of different mutations.\r\n\r\nResults\r\nHere we use a protocol to measure the bias as a function of the number of introduced mutations. It is based on a self-consistency test of the reciprocity the effect of a mutation. An advantage of the used approach is that it relies solely on crystal structures without experimentally measured stability values. We applied the protocol to four popular algorithms predicting change of protein stability upon mutation, FoldX, Eris, Rosetta and I-Mutant, and found an inherent bias. For one program, FoldX, we manage to substantially reduce the bias using additional relaxation by Modeller. Authors using algorithms for predicting effects of mutations should be aware of the bias described here.","lang":"eng"}],"month":"11","page":"3653-3658","ddc":["570"],"publisher":"Oxford University Press","file_date_updated":"2020-07-14T12:47:15Z","volume":34,"article_processing_charge":"No","quality_controlled":"1","oa":1,"date_created":"2019-02-14T12:48:00Z","publication":"Bioinformatics","ec_funded":1,"publication_identifier":{"eissn":["1367-4811"]}}]