This post is evolving from a reading list to a review of the academic papers published that are either about or use the Biological Expression Language (BEL). It’s divided into the categories of software/visualization tools, algorithms/analytical frameworks, data integration, natural language processing, curation workflows, and downstream applications.

In my history of BEL, I imposed some quality and impact standards. Without getting into a debate about how peer review is broken because it’s missing these standards, this list is more inclusive to papers whether they are likely to be reproducible or useful to the community, or not.

Some papers in this list were tricky to find - many of the PMI papers do not mention BEL in the abstract and the journals in which their papers were published don’t seem to index properly in MEDLINE. You’re welcome to use my search on PubMed and my search on Europe PubMed Central to see for yourself. I also looked through the publication lists of some key authors from Selventa and PMI over the last 10 years to find several other application papers. As always, this list is incomplete due to the lack of findability/accessibility of papers, my lack of knowledge of everything, and lack of time to deeply read through all authors’ histories. If you know of a search tool that might be helpful for improving this list, please let me know. I’ve been meaning to check out CoCites:

As always, improvements are welcome. Check my contact info at the bottom of this post. I’ll be making a major update to this post later to be more like a review as I’m putting together the new BEL reference paper.

Software and Visualization

PyBEL: a computational framework for Biological Expression Language.
Hoyt, C. T., Konotopez, A. & Ebeling, C. (2017).
Bioinformatics, 34(4), 703–704.

BEL Commons: an environment for exploration and analysis of networks encoded in Biological Expression Language.
Hoyt, C. T., Domingo-Fernández, D., & Hofmann-Apitius, M. (2018).
Database, 2018(3), 1–11

INDRA-IPM: Interactive pathway modeling using natural language with automated assembly.
Todorov, P. V., Gyori, B. M., Bachman, J. A., & Sorger, P. K. (2019).
Bioinformatics, 35(21), 4501–4503.

Algorithms and Analytical Frameworks

A network-based approach to quantifying the impact of biologically active substances.
Hoeng, J., Deehan, R., Pratt, D., Martin, F., Sewer, A., Thomson, T. M., Drubin, D. A., Waters, C. A., de Graaf, D., & Peitsch, M. C. (2012).
Drug Discovery Today, 17(9), 413–418.

Assessment of network perturbation amplitudes by applying high-throughput data to causal biological networks.
Martin, F., Thomson, T. M., Sewer, A., Drubin, D. a, Mathis, C., Weisensee, D., Pratt, D., Hoeng, J., & Peitsch, M. C. (2012).
BMC Systems Biology, 6, 54.

Quantitative assessment of biological impact using transcriptomic data and mechanistic network models.
Thomson, T. M., Sewer, A., Martin, F., Belcastro, V., Frushour, B. P., Gebel, S., Park, J., Schlage, W. K., Talikka, M., Vasilyev, D. M., Westra, J. W., Hoeng, J., & Peitsch, M. C. (2013).
Toxicology and Applied Pharmacology, 272(3), 863–878.

Reverse causal reasoning: applying qualitative causal knowledge to the interpretation of high-throughput data.
Catlett, N. L., Bargnesi, A. J., Ungerer, S., Seagaran, T., Ladd, W., Elliston, K. O., & Pratt, D. (2013).
BMC Bioinformatics, 14(1), 340.

An algorithm for score aggregation over causal biological networks based on random walk sampling.
Vasilyev, D. M., Thomson, T. M., Frushour, B. P., Martin, F., & Sewer, A. (2014).
BMC Research Notes, 7, 516.

Quantification of biological network perturbations for mechanistic insight and diagnostics using two-layer causal models.
Martin, F., Sewer, A., Talikka, M., Xiang, Y., Hoeng, J., & Peitsch, M. C. (2014).
BMC Bioinformatics, 15(1), 238.

Multimodal mechanistic signatures for neurodegenerative diseases (NeuroMMSig): a web server for mechanism enrichment.
Domingo-Fernández, D., Kodamullil, A. T., Iyappan, A., Naz, M., Emon, M. A., Raschka, T., Karki, R., Springstubbe, S., Ebeling, C., & Hofmann-Apitius, M. (2017).
Bioinformatics, 33(22), 3679–3681.

BEL2ABM: Agent-based simulation of static models in Biological Expression Language.
Gündel, M., Hoyt, C. T., & Hofmann-Apitius, M. (2018).
Bioinformatics, 34(13), 2316–2318.

BioKEEN: a library for learning and evaluating biological knowledge graph embeddings.
Ali, M., Hoyt, C. T., Domingo-Fernández, D., Lehmann, J., & Jabeen, H. (2019).
Bioinformatics, 35(18), 3538–3540.

Quantifying mechanisms in neurodegenerative diseases using candidate mechanism perturbation amplitude algorithm.
Karki, R., Kodamullil, A. T., Hoyt, C. T., & Hofmann-Apitius, M. (2019).
BMC Bioinformatics, 20(1), 494.

NPA: an R package for computing network perturbation amplitudes using gene expression data and two-layer networks..
Martin, F., Gubian, S., Talikka, M., Hoeng, J., & Peitsch, M. C. (2019).
BMC Bioinformatics, 20(1), 451.

Leveraging Structured Biological Knowledge for Counterfactual Inference: a Case Study of Viral Pathogenesis
Zucker, J., Paneri, K., Mohammad-Taheri, S., Bhargava, S., Kolambkar, P., Bakker, C., Teuton, J., Hoyt, C. T., Oxford, K., Ness, R., & Vitek, O. (2021).
IEEE Transactions on Big Data, 2021.3050680

Data Integration

Using Drugs as Molecular Probes: A Computational Chemical Biology Approach in Neurodegenerative Diseases.
Emon, M. A. E. K., Kodamullil, A. T., Karki, R., Younesi, E., & Hofmann-Apitius, M. (2017).
Journal of Alzheimer’s Disease, 56(2), 677–686.

ComPath: an ecosystem for exploring, analyzing, and curating mappings across pathway databases.
Domingo-Fernandez, D., Hoyt, C. T., Alvarez, C. B., Marin-Llao, J., & Hofmann-Apitius, M. (2018).
npj Systems Biology and Applications, 5(1), 3.

Integration of Structured Biological Data Sources using Biological Expression Language.
Hoyt, C. T., Domingo-Fernández, D., Mubeen, S., Llaó, J. M., Konotopez, A., Ebeling, C., Birkenbihl, C., Muslu, Ö., English, B., Müller, S., de Lacerda, M. P., Ali, M., Colby, S., Türei, D., Palacio-Escat, N., & Hofmann-Apitius, M. (2019).
bioRxiv, 631812.

PathMe: merging and exploring mechanistic pathway knowledge.
Domingo-Fernández, D., Mubeen, S., Marín-Llaó, J., Hoyt, C. T., & Hofmann-Apitius, M. (2019).
BMC Bioinformatics, 20(1), 243.

The Impact of Pathway Database Choice on Statistical Enrichment Analysis and Predictive Modeling.
Mubeen, S., Hoyt, C. T., Gemünd, A., Hofmann-Apitius, M., Fröhlich, H., & Domingo-Fernández, D. (2019).
Frontiers in Genetics, 10, 654442.

Natural Language Processing

Construction of biological networks from unstructured information based on a semi-automated curation workflow.
Szostak, J., Ansari, S., Madan, S., Fluck, J., Talikka, M., Iskandar, A., De Leon, H., Hofmann-Apitius, M., Peitsch, M. C., & Hoeng, J. (2015).
Database, 2015, bav057.

Training and evaluation corpora for the extraction of causal relationships encoded in biological expression language (BEL).
Fluck, J., Madan, S., Ansari, S., Kodamullil, A. T., Karki, R., Rastegar-Mojarad, M., Catlett, N. L., Hayes, W., Szostak, J., Hoeng, J., & Peitsch, M. (2016).
Database, 2016, 1–20.

BioCreative V track 4: a shared task for the extraction of causal network information using the Biological Expression Language.
Rinaldi, F., Ellendorff, T. R., Madan, S., Clematide, S., van der Lek, A., Mevissen, T., & Fluck, J. (2016).
Database, 2016, baw067.

Coreference resolution improves extraction of Biological Expression Language statements from texts.
Choi, M., Liu, H., Baumgartner, W., Zobel, J., & Verspoor, K. (2016).
Database, 2016, 1–14.

The BEL information extraction workflow (BELIEF): evaluation in the BioCreative V BEL and IAT track.
Madan, S., Hodapp, S., Senger, P., Ansari, S., Szostak, J., Hoeng, J., Peitsch, M., & Fluck, J. (2016).
Database, 2016(September), 1–17.

BelSmile: a biomedical semantic role labeling approach for extracting biological expression language from text.
Lai, P. T., Lo, Y. Y., Huang, M. S., Hsiao, Y. C., & Tsai, R. T. H. (2016).
Database, 2016(June), 1–9.

BELTracker: evidence sentence retrieval for BEL statements.
Rastegar-Mojarad, M., Komandur Elayavilli, R., & Liu, H. (2016).
Database, 2016(June), 1–11.

BELMiner: Adapting a rule-based relation extraction system to extract biological expression language statements from bio-medical literature evidence sentences.
Ravikumar, K. E., Rastegar-Mojarad, M., & Liu, H. (2017).
Database, 2017(1), 1–12.

Automatic Extraction of BEL-Statements based on Neural Networks.
Ali, M., Madan, S., Fischer, A., Petzka, H., & Fluck, J. (2017).
Proceedings of BioCreative VI Challenge and Workshop, October.

From word models to executable models of signaling networks using automated assembly.
Gyori, B. M., Bachman, J. A., Subramanian, K., Muhlich, J. L., Galescu, L., & Sorger, P. K. (2017).
Molecular Systems Biology, 13(11), 954.

The extraction of complex relationships and their conversion to biological expression language (BEL) overview of the BioCreative VI (2017) BEL track.
Madan, S., Szostak, J., Komandur Elayavilli, R., Tsai, R. T. H., Ali, M., Qian, L., Rastegar-Mojarad, M., Hoeng, J., & Fluck, J. (2019).
Database, 2019(1), 1–17.

Combining relation extraction with function detection for BEL statement extraction.
Liu, S., Cheng, W., Qian, L., & Zhou, G. (2019).
Database, 2019(4), 1–12.

Hierarchical sequence labeling for extracting BEL statements from biomedical literature.
Liu, S., Shao, Y., Qian, L., & Zhou, G. (2019).
BMC Medical Informatics and Decision Making, 19(Suppl 2).

Extraction of causal relations based on SBEL and BERT model
Shao, Y., Li, H., Gu, J., Qian, L., & Zhou, G. (2021).
Database, 2021.

Curation Workflows

On crowd-verification of biological networks.
Ansari, S., Binder, J., Boue, S., Di Fabio, A., Hayes, W., Hoeng, J., Iskandar, A., Kleiman, R., Norel, R., O’Neel, B., Peitsch, M. C., Poussin, C., Pratt, D., Rhrissorrakrai, K., Schlage, W. K., Stolovitzky, G., & Talikka, M. (2013).
Bioinformatics and Biology Insights, 7, 307–325.

sbv IMPROVER: Modern Approach to Systems Biology BT - Biological Networks and Pathway Analysis.
Guryanova, S., & Guryanova, A. (2017).
T. V Tatarinova & Y. Nikolsky (eds.), pp. 21–29. Springer New York.

Re-curation and rational enrichment of knowledge graphs in Biological Expression Language.
Hoyt, C. T., Domingo-Fernández, D., Aldisi, R., Xu, L., Kolpeja, K., Spalek, S., Wollert, E., Bachman, J., Gyori, B. M., Greene, P., & Hofmann-Apitius, M. (2019).
Database, 2019(1).

Content and Applications

A computable cellular stress network model for non-diseased pulmonary and cardiovascular tissue.
Schlage, W. K., Westra, J. W., Gebel, S., Catlett, N. L., Mathis, C., Frushour, B. P., Hengstermann, A., Van Hooser, A., Poussin, C., & Wong, B. (2011).
BMC Syst Biol, 5.

Construction of a Computable Cell Proliferation Network Focused on Non-Diseased Lung Cells.
Westra, J. W., Schlage, W. K., Frushour, B. P., Gebel, S., Catlett, N. L., Han, W., Eddy, S. F., Hengstermann, A., Matthews, A. L., & Mathis, C. (2011).
BMC Syst Biol, 5.

Construction of a computable network model for DNA damage, autophagy, cell death, and senescence.
Gebel, S., Lichtner, R. B., Frushour, B., Schlage, W. K., Hoang, V., Talikka, M., Hengstermann, A., Mathis, C., Veljkovic, E., Peck, M., Peitsch, M. C., Deehan, R., Hoeng, J., & Westra, J. W. (2013).
Bioinformatics and Biology Insights, 7, 97–117.

A modular cell-type focused inflammatory process network model for non-diseased pulmonary tissue.
Westra, J. W., Schlage, W. K., Hengstermann, A., Gebel, S., Mathis, C., Thomson, T., Wong, B., Hoang, V., Veljkovic, E., Peck, M., Lichtner, R. B., Weisensee, D., Talikka, M., Deehan, R., Hoeng, J., & Peitsch, M. C. (2013).
Bioinformatics and Biology Insights, 7, 167–192.

Systematic verification of upstream regulators of a computable cellular proliferation network model on non-diseased lung cells using a dedicated dataset.
Belcastro, V., Poussin, C., Gebel, S., Mathis, C., Schlage, W. K., Lichtner, R. B., Quadt-Humme, S., Wagner, S., Hoeng, J., & Peitsch, M. C. (2013).
Bioinformatics and Biology Insights, 7, 217–230.

Case study: the role of mechanistic network models in systems toxicology.
Hoeng, J., Talikka, M., Martin, F., Sewer, A., Yang, X., Iskandar, A., Schlage, W. K., & Peitsch, M. C. (2014).
Drug Discovery Today, 19(2), 183–192.

In vitro systems toxicology approach to investigate the effects of repeated cigarette smoke exposure on human buccal and gingival organotypic epithelial tissue cultures..
Schlage, W. K., Iskandar, A. R., Kostadinova, R., Xiang, Y., Sewer, A., Majeed, S., Kuehn, D., Frentzel, S., Talikka, M., Geertz, M., Mathis, C., Ivanov, N., Hoeng, J., & Peitsch, M. C. (2014).
Toxicology Mechanisms and Methods, 24(7), 470–487.

A vascular biology network model focused on inflammatory processes to investigate atherogenesis and plaque instability.
De León, H., Boué, S., Schlage, W. K., Boukharov, N., Westra, J. W., Gebel, S., VanHooser, A., Talikka, M., Fields, R. B., Veljkovic, E., Peck, M. J., Mathis, C., Hoang, V., Poussin, C., Deehan, R., Stolle, K., Hoeng, J., & Peitsch, M. C. (2014).
Journal of Translational Medicine, 12(1).

Micropublications: A semantic model for claims, evidence, arguments and annotations in biomedical communications.
Clark, T., Ciccarese, P. N., & Goble, C. A. (2014).
Journal of Biomedical Semantics, 5(1), 1–33.

Computational Modelling Approaches on Epigenetic Factors in Neurodegenerative and Autoimmune Diseases and Their Mechanistic Analysis.
Khanam Irin, A., Tom Kodamullil, A., Gündel, M., & Hofmann-Apitius, M. (2015).
Journal of Immunology Research, 2015, 1–10.

Semi-automated curation allows causal network model building for the quantification of age-dependent plaque progression in ApoE−/− mouse.
Szostak, J., Martin, F., Talikka, M., Peitsch, M. C., & Hoeng, J. (2016).
Gene Regulation and Systems Biology, 10, 95–103.

Community-reviewed biological network models for toxicology and drug discovery applications.
Namasivayam, A. A., Morales, A. F., Lacave, Á. M. F., Tallam, A., Simovic, B., Alfaro, D. G., Bobbili, D. R., Martin, F., Androsova, G., Shvydchenko, I., Park, J., Val Calvo, J., Hoeng, J., Peitsch, M. C., Racero, M. G. V., Biryukov, M., Talikka, M., Pérez, M. B., Rohatgi, N., … Xiang, Y. (2016).
Gene Regulation and Systems Biology, 10, 51–66.

Reasoning over genetic variance information in cause-and-effect models of neurodegenerative diseases.
Naz, M., Kodamullil, A. T., & Hofmann-Apitius, M. (2016).
Briefings in Bioinformatics, 17(3), 505–516.

Of Mice and Men: Comparative Analysis of Neuro-Inflammatory Mechanisms in Human and Mouse Using Cause-and-Effect Models.
Kodamullil, A. T., Iyappan, A., Karki, R., Madan, S., Younesi, E., & Hofmann-Apitius, M. (2017).
Journal of Alzheimer’s Disease, 59(3), 1045–1055.

Comorbidity Analysis between Alzheimer’s Disease and Type 2 Diabetes Mellitus (T2DM) Based on Shared Pathways and the Role of T2DM Drugs.
Karki, R., Kodamullil, A. T., & Hofmann-Apitius, M. (2017).
Journal of Alzheimer’s Disease, 60(2), 721–731.

Novel approaches to develop community-built biological network models for potential drug discovery.
Talikka, M., Bukharov, N., Hayes, W. S., Hofmann-Apitius, M., Alexopoulos, L., Peitsch, M. C., & Hoeng, J. (2017).
Expert Opinion on Drug Discovery, 12(8), 849–857.

A systematic approach for identifying shared mechanisms in epilepsy and its comorbidities.
Hoyt, C. T., Domingo-Fernández, D., Balzer, N., Güldenpfennig, A., & Hofmann-Apitius, M. (2018).
Database, 2018(1).

Construction of a suite of computable biological network models focused on mucociliary clearance in the repiratory tract.
Yepiskoposyan, H., Talikka, M., Vavassori, S., Martin, F., Sewer, A., Gubian, S., Luettich, K., Peitsch, M. C., & Hoeng, J. (2019).
Frontiers in Genetics, 10(FEB), 1–12.

34th Annual Meeting & Pre-Conference Programs of the Society for Immunotherapy of Cancer (SITC 2019): part 2 : National Harbor, MD, USA. 10 November 2019.
Journal for Immunotherapy of Cancer, 7(Suppl 1), 283.

  • See entry entitled “Reverse causal inferencing on lung adenocarcinoma patients reveals a stem cell-like molecular subtype associated with pack year history”

The reduction of DSS-induced colitis severity in mice exposed to cigarette smoke is linked to immune modulation and microbial shifts.
Lo Sasso, G., Phillips, B. W., Sewer, A., Battey, J. N. D., Kondylis, A., Talikka, M., Titz, B., Guedj, E., Peric, D., Bornand, D., Dulize, R., Merg, C., Corciulo, M., Ouadi, S., Yanuar, R., Tung, C. K., Ivanov, N. V, Peitsch, M. C., & Hoeng, J. (2020).
Scientific Reports, 10(1), 3829.

A Computational Approach for Mapping Heme Biology in the Context of Hemolytic Disorders.
Humayun, F., Domingo-Fernández, D., Paul George, A. A., Hopp, M. T., Syllwasschy, B. F., Detzel, M. S., Hoyt, C. T., Hofmann-Apitius, M., & Imhof, D. (2020).
Frontiers in Bioengineering and Biotechnology, 8(March), 1–10.

COVID-19 Knowledge Graph: a computable, multi-modal, cause-and-effect knowledge model of COVID-19 pathophysiology.
Domingo-Fernández, D., Baksi, S., Schultz, B., Gadiya, Y., Karki, R., Raschka, T., Ebeling, C., Hofmann-Apitius, M., & Kodamullil, A. T. (2020).
bioRxiv, 2020.04.14.040667.

Unravelling the debate on heme effects in COVID-19 infections.
Hopp, M., Domingo-Fernández, D., Gadiya, Y., Detzel, M. S., Schmalohr, B. F., Steinbock, F., Imhof, D., & Martin Hofmann-Apitius, M. (2020).
bioRxiv, 2020.06.09.142125

Data-Driven Modeling of Knowledge Assemblies in Understanding Comorbidity Between Type 2 Diabetes Mellitus and Alzheimer’s Disease.
Karki, R., Madan, S., Gadiya, Y., Domingo-Fernández, D., Kodamullil, A. T., & Hofmann-Apitius, M. (2020).
Journal of Alzheimer’s Disease, 78(1), 87–95.

A Systems Biology Approach for Hypothesizing the Effect of Genetic Variants on Neuroimaging Features in Alzheimer’s Disease.
Khatami, S. G., Domingo-Fernández, D., Mubeen, S., Hoyt, C. T., Robinson, C., Karki, R., Iyappan, A., Kodamullil, A. T., & Hofmann-Apitius, M. (2021).
Journal of Alzheimer’s Disease, 201397