{"application": "software-mentions", "version": "0.9.0-SNAPSHOT", "revision": "54efbc6", "date": "2026-10-14T14:45+0000", "md5": "68F8FF216F0B4056265CEE47740E1BFC", "biblio": {"title": "AstroLogics: a simulation-based framework for the analysis of boolean model ensembles", "authors": "Saran Pankaew, Vincent No\u20ac El, Lo\u00efc Paulev\u00e9, Denis Thieffry, Emmanuel Barillot, Laurence Calzone, Virginie Uhlmann"}, "mentions": [{"type": "software", "software-type": "software", "software-name": {"rawForm": "MaBoSS", "normalizedForm": "MaBoSS", "offsetStart": 0, "offsetEnd": 6}, "context": "MaBoSS generates activation probabilities for each state, which can be converted into node activation probabilities and stored as a time-series matrix.", "mentionContextAttributes": {"used": {"value": false, "score": 0.00016105175018310547}, "created": {"value": false, "score": 7.62939453125e-06}, "shared": {"value": false, "score": 2.384185791015625e-07}}, "documentContextAttributes": {"used": {"value": true, "score": 0.9999947547912598}, "created": {"value": true, "score": 0.9994908571243286}, "shared": {"value": false, "score": 1.5497207641601562e-06}}}, {"type": "software", "software-type": "software", "software-name": {"rawForm": "MaBoSS", "normalizedForm": "MaBoSS", "offsetStart": 0, "offsetEnd": 6}, "context": "MaBoSS simulation provides flexibility through tunable transition rates (R up and R down ) (see Supplementary Material, available  as supplementary data at Bioinformatics online).", "mentionContextAttributes": {"used": {"value": false, "score": 2.3603439331054688e-05}, "created": {"value": false, "score": 2.7298927307128906e-05}, "shared": {"value": false, "score": 1.5497207641601562e-06}}, "documentContextAttributes": {"used": {"value": true, "score": 0.9999947547912598}, "created": {"value": true, "score": 0.9994908571243286}, "shared": {"value": false, "score": 1.5497207641601562e-06}}}, {"type": "software", "software-type": "software", "software-name": {"rawForm": "MaBoSS", "normalizedForm": "MaBoSS", "offsetStart": 0, "offsetEnd": 6}, "context": "MaBoSS simulations output the probabilities for node activations at successive time points, thereby enabling the analysis of transient dynamics.", "mentionContextAttributes": {"used": {"value": false, "score": 0.00012195110321044922}, "created": {"value": false, "score": 3.2067298889160156e-05}, "shared": {"value": false, "score": 1.1920928955078125e-07}}, "documentContextAttributes": {"used": {"value": true, "score": 0.9999947547912598}, "created": {"value": true, "score": 0.9994908571243286}, "shared": {"value": false, "score": 1.5497207641601562e-06}}}, {"type": "software", "software-type": "software", "software-name": {"rawForm": "AstroLogics", "normalizedForm": "AstroLogics", "offsetStart": 0, "offsetEnd": 11}, "context": "AstroLogics also identifies key logical properties that govern each cluster, highlighting the core regulatory structures that differentiate model behaviors.", "mentionContextAttributes": {"used": {"value": false, "score": 9.059906005859375e-05}, "created": {"value": false, "score": 0.000508427619934082}, "shared": {"value": false, "score": 2.384185791015625e-07}}, "documentContextAttributes": {"used": {"value": true, "score": 0.9999425411224365}, "created": {"value": true, "score": 0.9999356269836426}, "shared": {"value": true, "score": 0.987003743648529}}}, {"type": "software", "software-type": "software", "software-name": {"rawForm": "AstroLogics", "normalizedForm": "AstroLogics", "offsetStart": 0, "offsetEnd": 11}, "context": "AstroLogics is a comprehensive framework designed for analyzing BNs within the model ensembles that arise from BN inference methods.", "mentionContextAttributes": {"used": {"value": false, "score": 2.0503997802734375e-05}, "created": {"value": true, "score": 0.9731267094612122}, "shared": {"value": false, "score": 2.384185791015625e-07}}, "documentContextAttributes": {"used": {"value": true, "score": 0.9999425411224365}, "created": {"value": true, "score": 0.9999356269836426}, "shared": {"value": true, "score": 0.987003743648529}}}, {"type": "software", "software-type": "software", "software-name": {"rawForm": "AstroLogics", "normalizedForm": "AstroLogics", "offsetStart": 3, "offsetEnd": 14}, "context": "In AstroLogics, the logic-feature extraction assumes Boolean update rules whose DNF representation is well-defined.", "mentionContextAttributes": {"used": {"value": false, "score": 0.00042003393173217773}, "created": {"value": false, "score": 2.7179718017578125e-05}, "shared": {"value": false, "score": 1.1920928955078125e-07}}, "documentContextAttributes": {"used": {"value": true, "score": 0.9999425411224365}, "created": {"value": true, "score": 0.9999356269836426}, "shared": {"value": true, "score": 0.987003743648529}}}, {"type": "software", "software-type": "software", "software-name": {"rawForm": "AstroLogics", "normalizedForm": "AstroLogics", "offsetStart": 4, "offsetEnd": 15}, "context": "The AstroLogics framework aims to provide a tool for analyzing and identifying differences between Boolean models within the model ensemble.", "mentionContextAttributes": {"used": {"value": false, "score": 2.3603439331054688e-05}, "created": {"value": true, "score": 0.808156430721283}, "shared": {"value": false, "score": 3.5762786865234375e-07}}, "documentContextAttributes": {"used": {"value": true, "score": 0.9999425411224365}, "created": {"value": true, "score": 0.9999356269836426}, "shared": {"value": true, "score": 0.987003743648529}}}, {"type": "software", "software-type": "software", "software-name": {"rawForm": "AstroLogics", "normalizedForm": "AstroLogics", "offsetStart": 4, "offsetEnd": 15}, "context": "The AstroLogics workflow is illustrated in Fig. 1.", "mentionContextAttributes": {"used": {"value": false, "score": 0.00226438045501709}, "created": {"value": false, "score": 1.239776611328125e-05}, "shared": {"value": false, "score": 3.5762786865234375e-07}}, "documentContextAttributes": {"used": {"value": true, "score": 0.9999425411224365}, "created": {"value": true, "score": 0.9999356269836426}, "shared": {"value": true, "score": 0.987003743648529}}}, {"type": "software", "software-type": "software", "software-name": {"rawForm": "AstroLogics", "normalizedForm": "AstroLogics", "offsetStart": 4, "offsetEnd": 15}, "context": "The AstroLogics pipeline relies on the DNF to convert logic rules to features.", "mentionContextAttributes": {"used": {"value": false, "score": 0.0041307806968688965}, "created": {"value": false, "score": 4.1365623474121094e-05}, "shared": {"value": false, "score": 2.384185791015625e-07}}, "documentContextAttributes": {"used": {"value": true, "score": 0.9999425411224365}, "created": {"value": true, "score": 0.9999356269836426}, "shared": {"value": true, "score": 0.987003743648529}}}, {"type": "software", "software-type": "software", "software-name": {"rawForm": "AstroLogics", "normalizedForm": "AstroLogics", "offsetStart": 4, "offsetEnd": 15}, "context": "The AstroLogics framework integrates a method to compare the long-term behavior of BNs and cluster them using boolsim.", "mentionContextAttributes": {"used": {"value": false, "score": 3.24249267578125e-05}, "created": {"value": false, "score": 0.010311365127563477}, "shared": {"value": false, "score": 2.384185791015625e-07}}, "documentContextAttributes": {"used": {"value": true, "score": 0.9999425411224365}, "created": {"value": true, "score": 0.9999356269836426}, "shared": {"value": true, "score": 0.987003743648529}}}, {"type": "software", "software-type": "software", "software-name": {"rawForm": "AstroLogics", "normalizedForm": "AstroLogics", "offsetStart": 4, "offsetEnd": 15}, "context": "The AstroLogics package, along with tutorials and datasets, are available at https://  github.com/sysbio-curie/AstroLogics.", "mentionContextAttributes": {"used": {"value": false, "score": 0.000493168830871582}, "created": {"value": false, "score": 0.00044077634811401367}, "shared": {"value": true, "score": 0.9825368523597717}}, "documentContextAttributes": {"used": {"value": true, "score": 0.9999425411224365}, "created": {"value": true, "score": 0.9999356269836426}, "shared": {"value": true, "score": 0.987003743648529}}}, {"type": "software", "software-type": "software", "software-name": {"rawForm": "BNclassifier", "normalizedForm": "BNclassifier", "offsetStart": 4, "offsetEnd": 16}, "context": "The BNclassifier investigate how changes in logical parameters affect the attractor landscape.", "mentionContextAttributes": {"used": {"value": false, "score": 0.008131802082061768}, "created": {"value": false, "score": 0.0002461075782775879}, "shared": {"value": false, "score": 2.384185791015625e-07}}, "documentContextAttributes": {"used": {"value": false, "score": 0.008131802082061768}, "created": {"value": false, "score": 0.0002461075782775879}, "shared": {"value": false, "score": 2.384185791015625e-07}}}, {"type": "software", "software-type": "software", "software-name": {"rawForm": "MaBoSS", "normalizedForm": "MaBoSS", "offsetStart": 5, "offsetEnd": 11}, "context": "With MaBoSS, we specify initial parameters, including initial states, simulation time steps, and the number of sample counts.", "mentionContextAttributes": {"used": {"value": false, "score": 0.00672757625579834}, "created": {"value": true, "score": 0.6844720244407654}, "shared": {"value": false, "score": 1.1920928955078125e-07}}, "documentContextAttributes": {"used": {"value": true, "score": 0.9999947547912598}, "created": {"value": true, "score": 0.9994908571243286}, "shared": {"value": false, "score": 1.5497207641601562e-06}}}, {"type": "software", "software-type": "software", "software-name": {"rawForm": "AstroLogics", "normalizedForm": "AstroLogics", "offsetStart": 9, "offsetEnd": 20}, "context": "Overall, AstroLogics is an exploratory analysis framework that generates hypotheses from inferred BN ensembles.", "mentionContextAttributes": {"used": {"value": false, "score": 3.600120544433594e-05}, "created": {"value": false, "score": 0.009837150573730469}, "shared": {"value": false, "score": 2.384185791015625e-07}}, "documentContextAttributes": {"used": {"value": true, "score": 0.9999425411224365}, "created": {"value": true, "score": 0.9999356269836426}, "shared": {"value": true, "score": 0.987003743648529}}}, {"type": "software", "software-type": "software", "software-name": {"rawForm": "AstroLogics", "normalizedForm": "AstroLogics", "offsetStart": 14, "offsetEnd": 25}, "context": "The developed AstroLogics package is available on GitHub https://github.com/sysbio-curie/AstroLogics,", "mentionContextAttributes": {"used": {"value": false, "score": 0.00022786855697631836}, "created": {"value": true, "score": 0.5280097723007202}, "shared": {"value": true, "score": 0.987003743648529}}, "documentContextAttributes": {"used": {"value": true, "score": 0.9999425411224365}, "created": {"value": true, "score": 0.9999356269836426}, "shared": {"value": true, "score": 0.987003743648529}}}, {"type": "software", "software-type": "software", "software-name": {"rawForm": "MaBoSS", "normalizedForm": "MaBoSS", "offsetStart": 15, "offsetEnd": 21}, "context": "We carried out MaBoSS simulations across all 616 models and calculated intermodel distances using both terminal time point results and whole simulation trajectories.", "mentionContextAttributes": {"used": {"value": true, "score": 0.9999796152114868}, "created": {"value": false, "score": 0.001757979393005371}, "shared": {"value": false, "score": 1.1920928955078125e-07}}, "documentContextAttributes": {"used": {"value": true, "score": 0.9999947547912598}, "created": {"value": true, "score": 0.9994908571243286}, "shared": {"value": false, "score": 1.5497207641601562e-06}}}, {"type": "software", "software-type": "software", "software-name": {"rawForm": "AstroLogics", "normalizedForm": "AstroLogics", "offsetStart": 15, "offsetEnd": 26}, "context": "Meanwhile, the AstroLogics framework clusters fully specified models by simulation-based dynamical similarity and uses logic features to explain cluster differences.", "mentionContextAttributes": {"used": {"value": false, "score": 3.9458274841308594e-05}, "created": {"value": false, "score": 0.16568386554718018}, "shared": {"value": false, "score": 3.5762786865234375e-07}}, "documentContextAttributes": {"used": {"value": true, "score": 0.9999425411224365}, "created": {"value": true, "score": 0.9999356269836426}, "shared": {"value": true, "score": 0.987003743648529}}}, {"type": "software", "software-type": "software", "software-name": {"rawForm": "AstroLogics", "normalizedForm": "AstroLogics", "offsetStart": 18, "offsetEnd": 29}, "context": "From this matrix, AstroLogics calculates model differences using either terminal time point data (Euclidean distance) or whole trajectory data via Dynamic Time Warping (DTW) (Tavenard et al. 2020).", "mentionContextAttributes": {"used": {"value": false, "score": 0.0014984607696533203}, "created": {"value": false, "score": 3.731250762939453e-05}, "shared": {"value": false, "score": 2.384185791015625e-07}}, "documentContextAttributes": {"used": {"value": true, "score": 0.9999425411224365}, "created": {"value": true, "score": 0.9999356269836426}, "shared": {"value": true, "score": 0.987003743648529}}}, {"type": "software", "software-type": "software", "software-name": {"rawForm": "AstroLogics", "normalizedForm": "AstroLogics", "offsetStart": 19, "offsetEnd": 30}, "context": "We emphasized that AstroLogics classifies Boolean models primarily based on their dynamical behavior, such as the sets of reachable attractors.", "mentionContextAttributes": {"used": {"value": false, "score": 0.08737242221832275}, "created": {"value": false, "score": 0.06628328561782837}, "shared": {"value": false, "score": 2.384185791015625e-07}}, "documentContextAttributes": {"used": {"value": true, "score": 0.9999425411224365}, "created": {"value": true, "score": 0.9999356269836426}, "shared": {"value": true, "score": 0.987003743648529}}}, {"type": "software", "software-type": "software", "software-name": {"rawForm": "MaBoSS", "normalizedForm": "MaBoSS", "offsetStart": 20, "offsetEnd": 26}, "context": "However, the use of MaBoSS also restricts us to its continuous-time asynchronous update scheme.", "mentionContextAttributes": {"used": {"value": false, "score": 0.001033782958984375}, "created": {"value": false, "score": 7.593631744384766e-05}, "shared": {"value": false, "score": 2.384185791015625e-07}}, "documentContextAttributes": {"used": {"value": true, "score": 0.9999947547912598}, "created": {"value": true, "score": 0.9994908571243286}, "shared": {"value": false, "score": 1.5497207641601562e-06}}}, {"type": "software", "software-type": "software", "software-name": {"rawForm": "AstroLogics", "normalizedForm": "AstroLogics", "offsetStart": 20, "offsetEnd": 31}, "context": "Results: We present AstroLogics, a novel framework designed to analyze and identify differences in both dynamical behavior and logical regulation within a BN model ensemble.", "mentionContextAttributes": {"used": {"value": false, "score": 8.881092071533203e-05}, "created": {"value": true, "score": 0.9999356269836426}, "shared": {"value": false, "score": 2.384185791015625e-07}}, "documentContextAttributes": {"used": {"value": true, "score": 0.9999425411224365}, "created": {"value": true, "score": 0.9999356269836426}, "shared": {"value": true, "score": 0.987003743648529}}}, {"type": "software", "software-type": "software", "software-name": {"rawForm": "MaBoSS", "normalizedForm": "MaBoSS", "offsetStart": 21, "offsetEnd": 27}, "context": "In this work, we use MaBoSS, a stochastic simulation tool for continuous-time Markov processes in C++                                         .", "mentionContextAttributes": {"used": {"value": false, "score": 0.03250604867935181}, "created": {"value": true, "score": 0.9994908571243286}, "shared": {"value": false, "score": 1.1920928955078125e-07}}, "documentContextAttributes": {"used": {"value": true, "score": 0.9999947547912598}, "created": {"value": true, "score": 0.9994908571243286}, "shared": {"value": false, "score": 1.5497207641601562e-06}}}, {"type": "software", "software-type": "software", "software-name": {"rawForm": "MaBoSS", "normalizedForm": "MaBoSS", "offsetStart": 23, "offsetEnd": 29}, "context": "Our approach leverages MaBoSS, a stochastic simulation tool that implements the Boolean Kinetic Monte-Carlo algorithm to address time interpretation in BNs.", "mentionContextAttributes": {"used": {"value": false, "score": 7.18832015991211e-05}, "created": {"value": true, "score": 0.998789370059967}, "shared": {"value": false, "score": 2.384185791015625e-07}}, "documentContextAttributes": {"used": {"value": true, "score": 0.9999947547912598}, "created": {"value": true, "score": 0.9994908571243286}, "shared": {"value": false, "score": 1.5497207641601562e-06}}}, {"type": "software", "software-type": "software", "software-name": {"rawForm": "MaBoSS", "normalizedForm": "MaBoSS", "offsetStart": 26, "offsetEnd": 32}, "context": "In parallel, we performed MaBoSS simulations on all models, obtained node activation probabilities at the terminal time point, and calculated Euclidean distances between models.", "mentionContextAttributes": {"used": {"value": true, "score": 0.9999867677688599}, "created": {"value": false, "score": 0.0004709959030151367}, "shared": {"value": false, "score": 1.1920928955078125e-07}}, "documentContextAttributes": {"used": {"value": true, "score": 0.9999947547912598}, "created": {"value": true, "score": 0.9994908571243286}, "shared": {"value": false, "score": 1.5497207641601562e-06}}}, {"type": "software", "software-type": "software", "software-name": {"rawForm": "AstroLogics", "normalizedForm": "AstroLogics", "offsetStart": 29, "offsetEnd": 40}, "context": "This finding illustrates how AstroLogics can guide subsequent model refinements.", "mentionContextAttributes": {"used": {"value": false, "score": 6.210803985595703e-05}, "created": {"value": false, "score": 0.001887679100036621}, "shared": {"value": false, "score": 2.384185791015625e-07}}, "documentContextAttributes": {"used": {"value": true, "score": 0.9999425411224365}, "created": {"value": true, "score": 0.9999356269836426}, "shared": {"value": true, "score": 0.987003743648529}}}, {"type": "software", "software-type": "software", "software-name": {"rawForm": "AstroLogics", "normalizedForm": "AstroLogics", "offsetStart": 31, "offsetEnd": 42}, "context": "To demonstrate the function of AstroLogics, we first study a model of the differentiation of the Central Nervous System (CNS) (Qiu et al. 2017) (Fig. 2A), which has been presented in a tutorial network for BoNesis (Chevalier et al. 2024).", "mentionContextAttributes": {"used": {"value": false, "score": 0.011031031608581543}, "created": {"value": true, "score": 0.5455234050750732}, "shared": {"value": false, "score": 5.960464477539062e-07}}, "documentContextAttributes": {"used": {"value": true, "score": 0.9999425411224365}, "created": {"value": true, "score": 0.9999356269836426}, "shared": {"value": true, "score": 0.987003743648529}}}, {"type": "software", "software-type": "software", "software-name": {"rawForm": "AstroLogics", "normalizedForm": "AstroLogics", "offsetStart": 32, "offsetEnd": 43}, "context": "This case study illustrates how AstroLogics can help understand the structure of model ensembles, potentially enabling their refinement.", "mentionContextAttributes": {"used": {"value": false, "score": 4.5418739318847656e-05}, "created": {"value": false, "score": 0.0786944031715393}, "shared": {"value": false, "score": 2.384185791015625e-07}}, "documentContextAttributes": {"used": {"value": true, "score": 0.9999425411224365}, "created": {"value": true, "score": 0.9999356269836426}, "shared": {"value": true, "score": 0.987003743648529}}}, {"type": "software", "software-type": "implicit", "software-name": {"rawForm": "scripts", "normalizedForm": "scripts", "offsetStart": 33, "offsetEnd": 40}, "url": {"rawForm": "https://github.com/sysbio-curie/  AstroLogics", "normalizedForm": "https://github.com/sysbio-curie/ AstroLogics"}, "context": "as well as datasets, models, and scripts to replicate each experiment and figure shown in this work.", "mentionContextAttributes": {"used": {"value": false, "score": 0.03457874059677124}, "created": {"value": false, "score": 0.39790958166122437}, "shared": {"value": false, "score": 1.1920928955078125e-07}}, "documentContextAttributes": {"used": {"value": false, "score": 0.04324352741241455}, "created": {"value": false, "score": 0.39790958166122437}, "shared": {"value": false, "score": 0.2299829125404358}}}, {"type": "software", "software-type": "software", "software-name": {"rawForm": "MaBoSS", "normalizedForm": "MaBoSS", "offsetStart": 42, "offsetEnd": 48}, "context": "To control the impact of stochasticity in MaBoSS simulations, we performed a convergence analysis considering different sample counts (see Supplementary Analysis 3, available as  supplementary data at Bioinformatics online).", "mentionContextAttributes": {"used": {"value": true, "score": 0.9999160766601562}, "created": {"value": false, "score": 9.179115295410156e-06}, "shared": {"value": false, "score": 7.152557373046875e-07}}, "documentContextAttributes": {"used": {"value": true, "score": 0.9999947547912598}, "created": {"value": true, "score": 0.9994908571243286}, "shared": {"value": false, "score": 1.5497207641601562e-06}}}, {"type": "software", "software-type": "software", "software-name": {"rawForm": "BoNesis", "normalizedForm": "BoNesis", "offsetStart": 43, "offsetEnd": 50}, "context": "These models have been used to demonstrate BoNesis' model inference functionality, with traceable constraints.", "mentionContextAttributes": {"used": {"value": true, "score": 0.9830176830291748}, "created": {"value": false, "score": 0.0035136938095092773}, "shared": {"value": false, "score": 1.1920928955078125e-07}}, "documentContextAttributes": {"used": {"value": true, "score": 0.999976634979248}, "created": {"value": true, "score": 0.5455234050750732}, "shared": {"value": false, "score": 5.960464477539062e-07}}, "references": [{"label": "(Chevalier et al. 2024)", "normalizedForm": "Chevalier et al. 2024", "refKey": 8}]}, {"type": "software", "software-type": "software", "software-name": {"rawForm": "AstroLogics", "normalizedForm": "AstroLogics", "offsetStart": 43, "offsetEnd": 54}, "context": "Following the logic comparison pipeline of AstroLogics (see Section \"Materials and methods\"), we were able to identify three major groups of logic features: (i) constant features, which are logic features that are included in all BNs and can be seen as the backbone of the model; (ii) variable features, which are logic features that may vary between BNs but are not statistically significant between clusters of BNs; and (iii) marker features, which are logic features that are statistically significant between two clusters of models.", "mentionContextAttributes": {"used": {"value": true, "score": 0.9998584985733032}, "created": {"value": false, "score": 1.7404556274414062e-05}, "shared": {"value": false, "score": 1.7881393432617188e-06}}, "documentContextAttributes": {"used": {"value": true, "score": 0.9999425411224365}, "created": {"value": true, "score": 0.9999356269836426}, "shared": {"value": true, "score": 0.987003743648529}}}, {"type": "software", "software-type": "software", "software-name": {"rawForm": "MaBoSS", "normalizedForm": "MaBoSS", "offsetStart": 47, "offsetEnd": 53}, "context": "We then computed model distances obtained with MaBoSS simulations and projected them onto the MDS latent space with k-means clustering.", "mentionContextAttributes": {"used": {"value": true, "score": 0.9999947547912598}, "created": {"value": false, "score": 3.814697265625e-06}, "shared": {"value": false, "score": 1.1920928955078125e-07}}, "documentContextAttributes": {"used": {"value": true, "score": 0.9999947547912598}, "created": {"value": true, "score": 0.9994908571243286}, "shared": {"value": false, "score": 1.5497207641601562e-06}}}, {"type": "software", "software-type": "software", "software-name": {"rawForm": "AstroLogics", "normalizedForm": "AstroLogics", "offsetStart": 47, "offsetEnd": 58}, "context": "Through multiple use cases, we demonstrate how AstroLogics facilitates comprehensive analyses of model diversity and discovery of key regulatory structures within a BN ensemble.", "mentionContextAttributes": {"used": {"value": false, "score": 0.000386655330657959}, "created": {"value": true, "score": 0.830112874507904}, "shared": {"value": false, "score": 2.384185791015625e-07}}, "documentContextAttributes": {"used": {"value": true, "score": 0.9999425411224365}, "created": {"value": true, "score": 0.9999356269836426}, "shared": {"value": true, "score": 0.987003743648529}}}, {"type": "software", "software-type": "software", "software-name": {"rawForm": "CoLoMoTo", "normalizedForm": "CoLoMoTo", "offsetStart": 50, "offsetEnd": 58}, "context": "Finally, the combination of a modular design with CoLoMoTo packages enhances cross-platform compatibility and improves reproducibility across different computational environments                     .", "mentionContextAttributes": {"used": {"value": false, "score": 6.42538070678711e-05}, "created": {"value": false, "score": 0.13424068689346313}, "shared": {"value": false, "score": 5.602836608886719e-06}}, "documentContextAttributes": {"used": {"value": false, "score": 0.0001519918441772461}, "created": {"value": true, "score": 0.9905984997749329}, "shared": {"value": false, "score": 5.602836608886719e-06}}}, {"type": "software", "software-type": "software", "software-name": {"rawForm": "AstroLogics", "normalizedForm": "AstroLogics", "offsetStart": 56, "offsetEnd": 67}, "context": "These visualizations constitute the final output of the AstroLogics pipeline.", "mentionContextAttributes": {"used": {"value": true, "score": 0.9960130453109741}, "created": {"value": false, "score": 0.00010204315185546875}, "shared": {"value": false, "score": 2.384185791015625e-07}}, "documentContextAttributes": {"used": {"value": true, "score": 0.9999425411224365}, "created": {"value": true, "score": 0.9999356269836426}, "shared": {"value": true, "score": 0.987003743648529}}}, {"type": "software", "software-type": "implicit", "software-name": {"rawForm": "script", "normalizedForm": "script", "offsetStart": 61, "offsetEnd": 67}, "context": "Nevertheless, the method remains applicable at this scale (a script for this analysis can be found in Results_large_model on the GitHub repository).", "mentionContextAttributes": {"used": {"value": false, "score": 0.00016164779663085938}, "created": {"value": false, "score": 8.285045623779297e-05}, "shared": {"value": false, "score": 4.649162292480469e-06}}, "documentContextAttributes": {"used": {"value": false, "score": 0.00016164779663085938}, "created": {"value": false, "score": 8.285045623779297e-05}, "shared": {"value": false, "score": 4.649162292480469e-06}}}, {"type": "software", "software-type": "software", "software-name": {"rawForm": "AstroLogics", "normalizedForm": "AstroLogics", "offsetStart": 62, "offsetEnd": 73}, "context": "These enhancements would further strengthen the capability of AstroLogics to analyze complex biological networks.", "mentionContextAttributes": {"used": {"value": false, "score": 0.0012015104293823242}, "created": {"value": false, "score": 0.0385744571685791}, "shared": {"value": false, "score": 3.5762786865234375e-07}}, "documentContextAttributes": {"used": {"value": true, "score": 0.9999425411224365}, "created": {"value": true, "score": 0.9999356269836426}, "shared": {"value": true, "score": 0.987003743648529}}}, {"type": "software", "software-type": "software", "software-name": {"rawForm": "AstroLogics", "normalizedForm": "AstroLogics", "offsetStart": 63, "offsetEnd": 74}, "context": "In order to supersede the current tool limitations, we present AstroLogics, a framework designed to compute dynamical distances between BNs, cluster models, and identify key logic regulations that characterize each cluster.", "mentionContextAttributes": {"used": {"value": false, "score": 4.744529724121094e-05}, "created": {"value": true, "score": 0.9998949766159058}, "shared": {"value": false, "score": 5.960464477539062e-07}}, "documentContextAttributes": {"used": {"value": true, "score": 0.9999425411224365}, "created": {"value": true, "score": 0.9999356269836426}, "shared": {"value": true, "score": 0.987003743648529}}}, {"type": "software", "software-type": "software", "software-name": {"rawForm": "MaBoSS", "normalizedForm": "MaBoSS", "offsetStart": 65, "offsetEnd": 71}, "context": "To address the increased runtime, a recent GPU implementation of MaBoSS ( \ufffd Smelko et al. 2024) could potentially scale simulations by more than 100-fold (based on real-world example models), enabling the simulation of much larger and more complex models.", "mentionContextAttributes": {"used": {"value": false, "score": 0.00012314319610595703}, "created": {"value": false, "score": 0.0404820442199707}, "shared": {"value": false, "score": 4.76837158203125e-07}}, "documentContextAttributes": {"used": {"value": true, "score": 0.9999947547912598}, "created": {"value": true, "score": 0.9994908571243286}, "shared": {"value": false, "score": 1.5497207641601562e-06}}}, {"type": "software", "software-type": "software", "software-name": {"rawForm": "MaBoSS", "normalizedForm": "MaBoSS", "offsetStart": 68, "offsetEnd": 74}, "context": "For this whole sample counts range, we showed that simulations with MaBoSS offered a significant time advantage for calculating dynamical properties.", "mentionContextAttributes": {"used": {"value": true, "score": 0.9999892711639404}, "created": {"value": false, "score": 0.00017696619033813477}, "shared": {"value": false, "score": 1.1920928955078125e-07}}, "documentContextAttributes": {"used": {"value": true, "score": 0.9999947547912598}, "created": {"value": true, "score": 0.9994908571243286}, "shared": {"value": false, "score": 1.5497207641601562e-06}}}, {"type": "software", "software-type": "software", "software-name": {"rawForm": "BoNesis", "normalizedForm": "BoNesis", "offsetStart": 68, "offsetEnd": 75}, "context": "For each network, we generated 1000 random BN model ensembles using BoNesis.", "mentionContextAttributes": {"used": {"value": true, "score": 0.999976634979248}, "created": {"value": false, "score": 0.007012307643890381}, "shared": {"value": false, "score": 1.1920928955078125e-07}}, "documentContextAttributes": {"used": {"value": true, "score": 0.999976634979248}, "created": {"value": true, "score": 0.5455234050750732}, "shared": {"value": false, "score": 5.960464477539062e-07}}, "references": [{"label": "(Chevalier et al. 2024)", "normalizedForm": "Chevalier et al. 2024", "refKey": 8}]}, {"type": "software", "software-type": "software", "software-name": {"rawForm": "AstroLogics", "normalizedForm": "AstroLogics", "offsetStart": 68, "offsetEnd": 79}, "context": "In this study, we aimed to address this gap with the development of AstroLogics.", "mentionContextAttributes": {"used": {"value": false, "score": 0.00038999319076538086}, "created": {"value": true, "score": 0.9998985528945923}, "shared": {"value": false, "score": 2.384185791015625e-07}}, "documentContextAttributes": {"used": {"value": true, "score": 0.9999425411224365}, "created": {"value": true, "score": 0.9999356269836426}, "shared": {"value": true, "score": 0.987003743648529}}}, {"type": "software", "software-type": "software", "software-name": {"rawForm": "AstroLogics", "normalizedForm": "AstroLogics", "offsetStart": 75, "offsetEnd": 86}, "context": "To demonstrate the functionality and capability of our framework, we apply AstroLogics to three Boolean network ensembles, each chosen to highlight a distinct aspect of the pipeline.", "mentionContextAttributes": {"used": {"value": true, "score": 0.9995450377464294}, "created": {"value": false, "score": 0.4638936519622803}, "shared": {"value": false, "score": 2.384185791015625e-07}}, "documentContextAttributes": {"used": {"value": true, "score": 0.9999425411224365}, "created": {"value": true, "score": 0.9999356269836426}, "shared": {"value": true, "score": 0.987003743648529}}}, {"type": "software", "software-type": "software", "software-name": {"rawForm": "minibn", "normalizedForm": "minibn", "offsetStart": 79, "offsetEnd": 85}, "context": "Taking advantage of the tools already integrated in the environment, including minibn for model import and boolsim for attractor calculation, AstroLogics ensures seamless integration with existing Boolean modeling workflows.", "mentionContextAttributes": {"used": {"value": false, "score": 4.017353057861328e-05}, "created": {"value": false, "score": 0.002913057804107666}, "shared": {"value": false, "score": 3.5762786865234375e-07}}, "documentContextAttributes": {"used": {"value": false, "score": 4.017353057861328e-05}, "created": {"value": false, "score": 0.002913057804107666}, "shared": {"value": false, "score": 3.5762786865234375e-07}}}, {"type": "software", "software-type": "software", "software-name": {"rawForm": "CoLoMoTo", "normalizedForm": "CoLoMoTo", "offsetStart": 79, "offsetEnd": 87}, "context": "Our framework further fosters interoperability by adhering to the standardized CoLoMoTo environment.", "mentionContextAttributes": {"used": {"value": false, "score": 0.0001519918441772461}, "created": {"value": true, "score": 0.9905984997749329}, "shared": {"value": false, "score": 2.5033950805664062e-06}}, "documentContextAttributes": {"used": {"value": false, "score": 0.0001519918441772461}, "created": {"value": true, "score": 0.9905984997749329}, "shared": {"value": false, "score": 5.602836608886719e-06}}}, {"type": "software", "software-type": "software", "software-name": {"rawForm": "MaBoSS", "normalizedForm": "MaBoSS", "offsetStart": 83, "offsetEnd": 89}, "context": "In our framework, we circumvent this issue by estimating the computation time with MaBoSS.", "mentionContextAttributes": {"used": {"value": false, "score": 0.05808842182159424}, "created": {"value": true, "score": 0.922863245010376}, "shared": {"value": false, "score": 1.1920928955078125e-07}}, "documentContextAttributes": {"used": {"value": true, "score": 0.9999947547912598}, "created": {"value": true, "score": 0.9994908571243286}, "shared": {"value": false, "score": 1.5497207641601562e-06}}}, {"type": "software", "software-type": "software", "software-name": {"rawForm": "AstroLogics", "normalizedForm": "AstroLogics", "offsetStart": 87, "offsetEnd": 98}, "context": "This allows the interpretation of the clusters and marker logic features identified by AstroLogics.", "mentionContextAttributes": {"used": {"value": false, "score": 0.00017386674880981445}, "created": {"value": false, "score": 0.003494858741760254}, "shared": {"value": false, "score": 1.1920928955078125e-07}}, "documentContextAttributes": {"used": {"value": true, "score": 0.9999425411224365}, "created": {"value": true, "score": 0.9999356269836426}, "shared": {"value": true, "score": 0.987003743648529}}}, {"type": "software", "software-type": "software", "software-name": {"rawForm": "AstroLogics", "normalizedForm": "AstroLogics", "offsetStart": 89, "offsetEnd": 100}, "context": "The developed AstroLogics package is available on GitHub https://github.com/sysbio-curie/AstroLogics,", "mentionContextAttributes": {"used": {"value": false, "score": 0.00022786855697631836}, "created": {"value": true, "score": 0.5280097723007202}, "shared": {"value": true, "score": 0.987003743648529}}, "documentContextAttributes": {"used": {"value": true, "score": 0.9999425411224365}, "created": {"value": true, "score": 0.9999356269836426}, "shared": {"value": true, "score": 0.987003743648529}}}, {"type": "software", "software-type": "software", "software-name": {"rawForm": "boolsim", "normalizedForm": "boolsim", "offsetStart": 94, "offsetEnd": 101}, "publisher": {"rawForm": "AstroLogics", "normalizedForm": "AstroLogics"}, "context": "We compared the computational time required for the exact calculation of the attractors using boolsim with MaBoSS simulation times (ranging from 50 to 10 000 sample counts), varying the number of simulated trajectories.", "mentionContextAttributes": {"used": {"value": true, "score": 0.999994158744812}, "created": {"value": false, "score": 1.9073486328125e-06}, "shared": {"value": false, "score": 1.1920928955078125e-07}}, "documentContextAttributes": {"used": {"value": true, "score": 0.999994158744812}, "created": {"value": false, "score": 0.020895063877105713}, "shared": {"value": false, "score": 3.5762786865234375e-07}}}, {"type": "software", "software-type": "software", "software-name": {"rawForm": "tslearn", "normalizedForm": "tslearn", "offsetStart": 95, "offsetEnd": 102}, "context": "We applied the Dynamic Time Warping (DTW) algorithm                       , implemented in the tslearn package                       , to compute distances between models, considering the whole simulation trajectories rather than focusing only on the terminal time point.", "mentionContextAttributes": {"used": {"value": true, "score": 0.9998555183410645}, "created": {"value": false, "score": 0.29744935035705566}, "shared": {"value": false, "score": 1.1920928955078125e-07}}, "documentContextAttributes": {"used": {"value": true, "score": 0.9998555183410645}, "created": {"value": false, "score": 0.29744935035705566}, "shared": {"value": false, "score": 1.1920928955078125e-07}}, "references": [{"label": "(Tavenard et al. 2020)", "normalizedForm": "Tavenard et al. 2020", "refKey": 41, "offsetStart": 22995, "offsetEnd": 23017}]}, {"type": "software", "software-type": "implicit", "software-name": {"rawForm": "scripts", "normalizedForm": "scripts", "offsetStart": 98, "offsetEnd": 105}, "url": {"rawForm": "https://github.com/sysbio-curie/  AstroLogics", "normalizedForm": "https://github.com/sysbio-curie/ AstroLogics", "offsetStart": 156, "offsetEnd": 201}, "context": "Of note, all figures and plots presented in the examples can be replicated with the corresponding scripts along with detailed tutorials on the GitHub page: https://github.com/sysbio-curie/  AstroLogics.", "mentionContextAttributes": {"used": {"value": false, "score": 0.04324352741241455}, "created": {"value": false, "score": 0.00013005733489990234}, "shared": {"value": false, "score": 0.2299829125404358}}, "documentContextAttributes": {"used": {"value": false, "score": 0.04324352741241455}, "created": {"value": false, "score": 0.39790958166122437}, "shared": {"value": false, "score": 0.2299829125404358}}}, {"type": "software", "software-type": "component", "software-name": {"rawForm": "Results_large_model", "normalizedForm": "Results_large_model", "offsetStart": 102, "offsetEnd": 121}, "context": "Nevertheless, the method remains applicable at this scale (a script for this analysis can be found in Results_large_model on the GitHub repository).", "mentionContextAttributes": {"used": {"value": false, "score": 0.00016164779663085938}, "created": {"value": false, "score": 8.285045623779297e-05}, "shared": {"value": false, "score": 4.649162292480469e-06}}, "documentContextAttributes": {"used": {"value": false, "score": 0.00016164779663085938}, "created": {"value": false, "score": 8.285045623779297e-05}, "shared": {"value": false, "score": 4.649162292480469e-06}}}, {"type": "software", "software-type": "software", "software-name": {"rawForm": "MaBoSS", "normalizedForm": "MaBoSS", "offsetStart": 107, "offsetEnd": 113}, "context": "We compared the computational time required for the exact calculation of the attractors using boolsim with MaBoSS simulation times (ranging from 50 to 10 000 sample counts), varying the number of simulated trajectories.", "mentionContextAttributes": {"used": {"value": true, "score": 0.999994158744812}, "created": {"value": false, "score": 1.9073486328125e-06}, "shared": {"value": false, "score": 1.1920928955078125e-07}}, "documentContextAttributes": {"used": {"value": true, "score": 0.9999947547912598}, "created": {"value": true, "score": 0.9994908571243286}, "shared": {"value": false, "score": 1.5497207641601562e-06}}}, {"type": "software", "software-type": "software", "software-name": {"rawForm": "boolsim", "normalizedForm": "boolsim", "offsetStart": 107, "offsetEnd": 114}, "publisher": {"rawForm": "AstroLogics", "normalizedForm": "AstroLogics", "offsetStart": 142, "offsetEnd": 153}, "context": "Taking advantage of the tools already integrated in the environment, including minibn for model import and boolsim for attractor calculation, AstroLogics ensures seamless integration with existing Boolean modeling workflows.", "mentionContextAttributes": {"used": {"value": false, "score": 4.017353057861328e-05}, "created": {"value": false, "score": 0.002913057804107666}, "shared": {"value": false, "score": 3.5762786865234375e-07}}, "documentContextAttributes": {"used": {"value": true, "score": 0.999994158744812}, "created": {"value": false, "score": 0.020895063877105713}, "shared": {"value": false, "score": 3.5762786865234375e-07}}}, {"type": "software", "software-type": "software", "software-name": {"rawForm": "boolsim", "normalizedForm": "boolsim", "offsetStart": 110, "offsetEnd": 117}, "publisher": {"rawForm": "AstroLogics", "normalizedForm": "AstroLogics"}, "context": "The AstroLogics framework integrates a method to compare the long-term behavior of BNs and cluster them using boolsim.", "mentionContextAttributes": {"used": {"value": false, "score": 3.24249267578125e-05}, "created": {"value": false, "score": 0.010311365127563477}, "shared": {"value": false, "score": 2.384185791015625e-07}}, "documentContextAttributes": {"used": {"value": true, "score": 0.999994158744812}, "created": {"value": false, "score": 0.020895063877105713}, "shared": {"value": false, "score": 3.5762786865234375e-07}}}, {"type": "software", "software-type": "software", "software-name": {"rawForm": "AstroLogics", "normalizedForm": "AstroLogics", "offsetStart": 111, "offsetEnd": 122}, "context": "The AstroLogics package, along with tutorials and datasets, are available at https://  github.com/sysbio-curie/AstroLogics.", "mentionContextAttributes": {"used": {"value": false, "score": 0.000493168830871582}, "created": {"value": false, "score": 0.00044077634811401367}, "shared": {"value": true, "score": 0.9825368523597717}}, "documentContextAttributes": {"used": {"value": true, "score": 0.9999425411224365}, "created": {"value": true, "score": 0.9999356269836426}, "shared": {"value": true, "score": 0.987003743648529}}}, {"type": "software", "software-type": "software", "software-name": {"rawForm": "Noel", "normalizedForm": "Noel", "offsetStart": 118, "offsetEnd": 122}, "context": "Saran Pankaew (Conceptualization [Lead], Methodology [Lead], Software [Lead], Writing-original draft [Lead]), Vincent Noel (Software [Supporting], Writing-review & editing [Supporting]), Lo\u00efc Paulev\u00e9 (Software [Supporting], Writing-review & editing [Supporting]), Denis Thieffry (Supervision [Supporting], Writingreview & editing [Supporting]), Emmanuel Barillot (Project administration [Supporting], Writing-review & editing [Supporting]), and Laurence Calzone (Conceptualization [Supporting], Supervision [Lead], Writing-review & editing [Lead])", "mentionContextAttributes": {"used": {"value": true, "score": 0.9979054927825928}, "created": {"value": false, "score": 3.2186508178710938e-06}, "shared": {"value": false, "score": 5.960464477539062e-07}}, "documentContextAttributes": {"used": {"value": true, "score": 0.9979054927825928}, "created": {"value": false, "score": 3.2186508178710938e-06}, "shared": {"value": false, "score": 5.960464477539062e-07}}}, {"type": "software", "software-type": "software", "software-name": {"rawForm": "AstroLogics", "normalizedForm": "AstroLogics", "offsetStart": 123, "offsetEnd": 134}, "context": "We first use a central nervous system (CNS) differentiation model (Qiu et al. 2017) to illustrate the core capabilities of AstroLogics.", "mentionContextAttributes": {"used": {"value": false, "score": 0.13932931423187256}, "created": {"value": false, "score": 0.021528303623199463}, "shared": {"value": false, "score": 1.1920928955078125e-07}}, "documentContextAttributes": {"used": {"value": true, "score": 0.9999425411224365}, "created": {"value": true, "score": 0.9999356269836426}, "shared": {"value": true, "score": 0.987003743648529}}}, {"type": "software", "software-type": "software", "software-name": {"rawForm": "AstroLogics", "normalizedForm": "AstroLogics", "offsetStart": 125, "offsetEnd": 136}, "context": "To assess the added value of this dynamics-based strategy relative to a simpler, structure-based alternative, we benchmarked AstroLogics against distances computed directly from structural edge features.", "mentionContextAttributes": {"used": {"value": true, "score": 0.9999425411224365}, "created": {"value": false, "score": 0.014749526977539062}, "shared": {"value": false, "score": 1.1920928955078125e-07}}, "documentContextAttributes": {"used": {"value": true, "score": 0.9999425411224365}, "created": {"value": true, "score": 0.9999356269836426}, "shared": {"value": true, "score": 0.987003743648529}}}, {"type": "software", "software-type": "software", "software-name": {"rawForm": "BoNesis", "normalizedForm": "BoNesis", "offsetStart": 181, "offsetEnd": 188}, "context": "Myt1L is constrained only in the zero (unreachable states with all components inactivated) and initial (init) state and is left unspecified in all transition and stable states (see BoNesis tutorial), allowing BoNesis to assign any consistent logical rule to it.", "mentionContextAttributes": {"used": {"value": false, "score": 0.005453824996948242}, "created": {"value": false, "score": 1.71661376953125e-05}, "shared": {"value": false, "score": 2.384185791015625e-07}}, "documentContextAttributes": {"used": {"value": true, "score": 0.999976634979248}, "created": {"value": true, "score": 0.5455234050750732}, "shared": {"value": false, "score": 5.960464477539062e-07}}, "references": [{"label": "(Chevalier et al. 2024)", "normalizedForm": "Chevalier et al. 2024", "refKey": 8}]}, {"type": "software", "software-type": "software", "software-name": {"rawForm": "Paulev\u00e9", "normalizedForm": "Paulev\u00e9", "offsetStart": 192, "offsetEnd": 199}, "context": "Saran Pankaew (Conceptualization [Lead], Methodology [Lead], Software [Lead], Writing-original draft [Lead]), Vincent Noel (Software [Supporting], Writing-review & editing [Supporting]), Lo\u00efc Paulev\u00e9 (Software [Supporting], Writing-review & editing [Supporting]), Denis Thieffry (Supervision [Supporting], Writingreview & editing [Supporting]), Emmanuel Barillot (Project administration [Supporting], Writing-review & editing [Supporting]), and Laurence Calzone (Conceptualization [Supporting], Supervision [Lead], Writing-review & editing [Lead])", "mentionContextAttributes": {"used": {"value": true, "score": 0.9979054927825928}, "created": {"value": false, "score": 3.2186508178710938e-06}, "shared": {"value": false, "score": 5.960464477539062e-07}}, "documentContextAttributes": {"used": {"value": true, "score": 0.9979054927825928}, "created": {"value": false, "score": 3.2186508178710938e-06}, "shared": {"value": false, "score": 5.960464477539062e-07}}}, {"type": "software", "software-type": "software", "software-name": {"rawForm": "boolsim", "normalizedForm": "boolsim", "offsetStart": 193, "offsetEnd": 219}, "publisher": {"rawForm": "AstroLogics", "normalizedForm": "AstroLogics"}, "context": "Inspired by the AEON approach (Bene\ufffd s et al. 2020), which exploits long-term behavior of BN to characterize BNs into groups, we used the attractor calculation function provided by the package boolsim                    to calculate a set of the model's attractors.", "mentionContextAttributes": {"used": {"value": true, "score": 0.9997150301933289}, "created": {"value": false, "score": 0.020895063877105713}, "shared": {"value": false, "score": 2.384185791015625e-07}}, "documentContextAttributes": {"used": {"value": true, "score": 0.999994158744812}, "created": {"value": false, "score": 0.020895063877105713}, "shared": {"value": false, "score": 3.5762786865234375e-07}}}, {"type": "software", "software-type": "software", "software-name": {"rawForm": "AstroLogics", "normalizedForm": "AstroLogics", "offsetStart": 199, "offsetEnd": 210}, "context": "The result confirmed that the dynamicsbased representation captures information that is not recoverable from network structure alone, supporting the use of simulated dynamics as the primary input to AstroLogics.", "mentionContextAttributes": {"used": {"value": true, "score": 0.9985882639884949}, "created": {"value": false, "score": 9.918212890625e-05}, "shared": {"value": false, "score": 2.384185791015625e-07}}, "documentContextAttributes": {"used": {"value": true, "score": 0.9999425411224365}, "created": {"value": true, "score": 0.9999356269836426}, "shared": {"value": true, "score": 0.987003743648529}}}, {"type": "software", "software-type": "software", "software-name": {"rawForm": "BoNesis", "normalizedForm": "BoNesis", "offsetStart": 206, "offsetEnd": 213}, "context": "To demonstrate the function of AstroLogics, we first study a model of the differentiation of the Central Nervous System (CNS) (Qiu et al. 2017) (Fig. 2A), which has been presented in a tutorial network for BoNesis (Chevalier et al. 2024).", "mentionContextAttributes": {"used": {"value": false, "score": 0.011031031608581543}, "created": {"value": true, "score": 0.5455234050750732}, "shared": {"value": false, "score": 5.960464477539062e-07}}, "documentContextAttributes": {"used": {"value": true, "score": 0.999976634979248}, "created": {"value": true, "score": 0.5455234050750732}, "shared": {"value": false, "score": 5.960464477539062e-07}}, "references": [{"label": "(Chevalier et al. 2024)", "normalizedForm": "Chevalier et al. 2024", "refKey": 8, "offsetStart": 16374, "offsetEnd": 16397}]}, {"type": "software", "software-type": "software", "software-name": {"rawForm": "BoNesis", "normalizedForm": "BoNesis", "offsetStart": 209, "offsetEnd": 216}, "context": "Myt1L is constrained only in the zero (unreachable states with all components inactivated) and initial (init) state and is left unspecified in all transition and stable states (see BoNesis tutorial), allowing BoNesis to assign any consistent logical rule to it.", "mentionContextAttributes": {"used": {"value": false, "score": 0.005453824996948242}, "created": {"value": false, "score": 1.71661376953125e-05}, "shared": {"value": false, "score": 2.384185791015625e-07}}, "documentContextAttributes": {"used": {"value": true, "score": 0.999976634979248}, "created": {"value": true, "score": 0.5455234050750732}, "shared": {"value": false, "score": 5.960464477539062e-07}}, "references": [{"label": "(Chevalier et al. 2024)", "normalizedForm": "Chevalier et al. 2024", "refKey": 8}]}, {"type": "software", "software-type": "software", "software-name": {"rawForm": "pystablemotifs", "normalizedForm": "pystablemotifs", "offsetStart": 254, "offsetEnd": 287}, "context": "To facilitate the incorporation of novel dynamical analysis methods, we have designed our framework in a modular fashion, supporting different model dynamics calculation strategies, including attractor analysis and succession diagrams, as implemented by pystablemotifs                    .", "mentionContextAttributes": {"used": {"value": false, "score": 0.00012671947479248047}, "created": {"value": true, "score": 0.9997813105583191}, "shared": {"value": false, "score": 3.5762786865234375e-07}}, "documentContextAttributes": {"used": {"value": false, "score": 0.00012671947479248047}, "created": {"value": true, "score": 0.9997813105583191}, "shared": {"value": false, "score": 3.5762786865234375e-07}}}], "references": [{"refKey": 8, "tei": "<biblStruct status=\"consolidated\" source=\"crossref\" xml:id=\"b8\">\n\t<analytic>\n\t\t<title level=\"a\" type=\"main\">BoNesis: a Python-Based Declarative Environment for the Verification, Reprogramming, and Synthesis of Most Permissive Boolean Networks</title>\n\t\t<author>\n\t\t\t<persName><forename type=\"first\">St\u00e9phanie</forename><surname>Chevalier</surname></persName>\n\t\t</author>\n\t\t<author>\n\t\t\t<persName><forename type=\"first\">D\u00e9borah</forename><surname>Boyenval</surname></persName>\n\t\t</author>\n\t\t<author>\n\t\t\t<persName><forename type=\"first\">Gustavo</forename><surname>Maga\u00f1a-L\u00f3pez</surname></persName>\n\t\t</author>\n\t\t<author>\n\t\t\t<persName><forename type=\"first\">Th\u00e9o</forename><surname>Roncalli</surname></persName>\n\t\t</author>\n\t\t<author>\n\t\t\t<persName><forename type=\"first\">Ath\u00e9na\u00efs</forename><surname>Vaginay</surname></persName>\n\t\t</author>\n\t\t<author>\n\t\t\t<persName><forename type=\"first\">Lo\u00efc</forename><surname>Paulev\u00e9</surname></persName>\n\t\t\t<idno type=\"ORCID\">0000-0002-7219-2027</idno>\n\t\t</author>\n\t\t<idno type=\"DOI\">10.1007/978-3-031-71671-3_6</idno>\n\t</analytic>\n\t<monogr>\n\t\t<title level=\"m\">Lecture Notes in Computer Science</title>\n\t\t<editor>\n\t\t\t<persName><forename type=\"first\">R</forename><surname>Gori</surname></persName>\n\t\t</editor>\n\t\t<editor>\n\t\t\t<persName><forename type=\"first\">P</forename><surname>Milazzo</surname></persName>\n\t\t</editor>\n\t\t<editor>\n\t\t\t<persName><forename type=\"first\">M</forename><surname>Tribastone</surname></persName>\n\t\t</editor>\n\t\t<imprint>\n\t\t\t<publisher>Springer Nature Switzerland</publisher>\n\t\t\t<date type=\"published\" when=\"2024\">2024</date>\n\t\t\t<biblScope unit=\"volume\">14971</biblScope>\n\t\t\t<biblScope unit=\"page\" from=\"71\" to=\"79\" />\n\t\t</imprint>\n\t</monogr>\n</biblStruct>\n"}, {"refKey": 41, "tei": "<biblStruct status=\"consolidated\" source=\"crossref\" xml:id=\"b41\">\n\t<analytic>\n\t\t<title level=\"a\" type=\"main\">Cost-Aware Early Classification of Time Series</title>\n\t\t<author>\n\t\t\t<persName><forename type=\"first\">Romain</forename><surname>Tavenard</surname></persName>\n\t\t</author>\n\t\t<author>\n\t\t\t<persName><forename type=\"first\">Simon</forename><surname>Malinowski</surname></persName>\n\t\t</author>\n\t\t<idno type=\"DOI\">10.1007/978-3-319-46128-1_40</idno>\n\t</analytic>\n\t<monogr>\n\t\t<title level=\"m\">Lecture Notes in Computer Science</title>\n\t\t<imprint>\n\t\t\t<publisher>Springer International Publishing</publisher>\n\t\t\t<date type=\"published\" when=\"2016\">2020</date>\n\t\t\t<biblScope unit=\"volume\">21</biblScope>\n\t\t\t<biblScope unit=\"page\" from=\"632\" to=\"647\" />\n\t\t</imprint>\n\t</monogr>\n</biblStruct>\n"}], "runtime": 15965, "id": "0b4fea7e98675297326bc7330dcf952b87e77b91", "metadata": {"id": "0b4fea7e98675297326bc7330dcf952b87e77b91"}, "original_file_path": "../../projects/softcite-analysis-hal/data/grobid/xml/AstroLogicsasimulationbasedframeworkfortheanalysisofbooleanmodelensembles.grobid.tei.xml", "file_name": "AstroLogicsasimulationbasedframeworkfortheanalysisofbooleanmodelensembles.grobid.tei.xml"}