dataesr/scientific-paragraphs-categorization huggingface dataset:
ASP solving. Given a PKN and a phosphoproteomic dataset, a family of candidate BNs, compatible with this PKN, is exhaustively enumerated including the main nodes (the sets S,I,R) of the experimental data. We refer the reader to  [12]  for a detailed description of BN's compatibility with a PKN. Afterwards an over-approximation constraint (see Materials and methods) is imposed upon each candidate BN to filter out invalid BNs  [8] , that do not result in an over-approximation of the reachability between the Boolean states given by the phosphoproteomic dataset. Finally, an optimization step is performed to select those BNs having a minimal distance between the actual time series T P and the over-approximated time series Y P. We have adopted the Root Mean Square Error (RMSE) as the objective function: RMSE ¼ ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffiffi ffi ffi 1 m � k � jPj X m i¼1 X k j¼1 X P2P ððt j P Þ i À ðy j P Þ i Þ 2 v u u t ð1Þ where m is the number of observed nodes, k is the number of time points, and P is the set of perturbations.
Prior knowledge network The structure of the protein signaling network was generated by mapping the experimentally measured phosphorylated proteins (HPN-DREAM dataset) to their equivalents from literature-curated databases and connecting them together within one network (see Materials and methods). The reference network (Fig 2 ) was built using the ReactomeFIViz app (also called the ReactomeFIPlugIn or Reactome FI Cytoscape app)  [25] , which accesses the interactions existing in the Reactome and other databases  [25, 26].
Data processing The learning and testing datasets used in this study were extracted from the HPN-DREAM challenge and correspond to time series protein measurements upon different perturbations of four breast cancer cell lines-UACC812, BT20, BT549, and MCF7  [20, 21]  (see Materials and methods). Since readout signals are measured on variable ranges depending on the protein, a normalization step was necessary. The learning dataset had a few noisy, inconsistent and incomplete time series data points. The caspo-ts system identified these inconsistencies existing in the time series data. The recurrent experimental inconsistency observed was an oscillation in the protein signal upon experimental inhibition of the same protein. To resolve the above mentioned issues, we performed the following data processing steps on the learning dataset: 1.
Evaluation The performance of the caspo-ts method is evaluated using three criteria: 1) RMSE calculation using a typical learning and testing data approach, 2) random data comparison, 3) AUROC (Area Under the Operating Curve) score. The BNs are learned using the learning dataset (see Materials and methods) only. The prediction accuracy is evaluated by comparing the RMSE of trajectories in the testing dataset with those recovered by the learned networks (see Eq 1). There are two types of RMSE-discrete and model. The discrete RMSE is imposed by the discretization of the method. Since we use a discrete learning approach, our recovered traces will be in {0,1} and this introduces an error with respect to continuous measurements in [0;1]. The model RMSE refers to the learned BN error with respect to the normalized time series data; that is, the model RMSE is at least as large as the discrete RMSE.
When the difference between these two is zero then the inferred BNs are able to recover the discrete trajectories without any error. If the model RMSE is greater than the discrete RMSE then the inferred BNs have some errors in the recoverability of the discrete time series data. To check how our method performs in case of random time series, we have calculated the RMSE score for random data and compared it with learning and testing data. Next, the validity of these networks is verified by comparing them with the canonical MTOR signaling pathway using two parameters, i. e. , true positive rate (TPR) and false positive rate (FPR). Validation using root mean square error criteria. The goal was not only to infer optimal BNs but also to verify that these BNs are able to recover trajectories that do not exist in the learning data.
For this purpose, we use experimental testing data to check the specificity of the trajectories of the proposed networks. This testing data is provided by the HPN-DREAM challenge organizers (see Materials and methods). Table  2  shows the corresponding RMSE in case of learning and testing data. It can be seen that the inferred BNs are able to produce the trajectories without any error in the learning dataset for all cell lines. It is encouraging to see that the inferred BNs are able to recover the discrete testing trajectories without any error in MCF7, and with a minimal error of 0. 0009, 0. 0106, and 0. 0094 in BT20, BT549, and UACC812, respectively. We also compared the RMSE score with the top two best performers of the HPN-DREAM challenge. We got the top position with an RMSE score of 0.
31 as compared to their RMSE  Validation using random data samples. The objective of this analysis is to show that the BNs obtained with caspo-ts using the HPN-DREAM datasets for the four cell lines have a worse RMSE score with respect to random trajectories, and therefore are very specific to the HPN-DREAM datasets. For this purpose, we generated 100 random data samples per cell line. In each sample, we generated a random value in [0; 1] for each readout protein in each time point without changing the perturbations. Then, we calculated the RMSE of these samples with respect to the inferred BNs of each cell line, and finally compared it with the learning and testing RMSE of these BNs.
RMSE ratio ¼ Discrete RMSE Model RMSE ð2Þ In Fig  6 , the RMSE ratio for random datasets is displayed by red boxplots for each cell line, and the RMSE ratio for testing and learning datasets is shown as clear outliers in green and https://doi. org/10. 1371/journal. pcbi. 1006538. g006 blue colors respectively. It is worth noting that the caspo-ts method has failed to recover random data time series, hence proving the specificity of the learned networks with respect to the HPN-DREAM challenge dataset. Additionally, we computed the RMSE of the testing data by using a leave one out approach. For this we generated slightly modified samples, by selecting random values of 5% of the learning data points. The same experimental perturbations and readout proteins were kept. Our results show that the BNs learned from the 5% randomized data have an RMSE of 0.
3113 with respect to the testing data, demonstrating caspo-ts robustness. For such 5% modified datasets, true positive BNs are difficult to obtain with the model checker; most candidates were false positive models. This highlights the complexity of this BN learning problem when few experimental perturbations are given because the space of candidate ASP-optimal BNs to verify is large and it is heavily populated with false positive Boolean models. Please refer to the supplementary information for details S2 Text. Validation using MTOR canonical pathway. To perform the validation of the structure of the BNs, we calculated a set of standard nodes from our PKN which are downstream nodes of MTOR and belong to the canonical MTOR pathway. We then evaluated how many of these standard nodes are also downstream nodes of MTOR in the learned BNs.
Discussion In this paper, we built cell line specific signaling networks for the DREAM time series dataset of 4 breast cancer cell lines (BT20, BT549, MCF7, and UACC812) using caspo-ts. This method combines Answer Set Programming and Model Checking techniques to infer true positive BNs verifying the experimental data. Caspo-ts allowed us to handle a midscale PKN (64 nodes and 178 edges) and a real dataset subject to experimental error. Caspo-ts enabled us to learn key dynamic mechanisms within the BNs explaining the time series data. Our results suggest that the behavior of cell line specific signaling networks is highly variable even under the same perturbations, agreeing with the heterogeneity of breast cancer and specifically with previous analysis on this data  [21]. The inferred Boolean models of each cell line were analyzed to identify commonalities as well as discrepancies.
Moreover, these inferred models can be executed computationally to identify potential drug targets or to see the effect of unseen perturbations. The predictive power of these models can be increased with improvements in protein interaction databases and comprehensive experimental data. We have discovered 38% of the cell line dependent behaviors as compared to the 33% of the HPN-DREAM challenge winner  [31]. We have implemented an algorithm to analyze the variability among cell lines and observed pairwise similarities among these cell lines. The similarity index varies from 15% (BT20 & BT549) to 23% (MCF7 & BT549, BT20 & MCF7). We have analyzed the similarity among the family of BNs of the same cell line as well, which varies from 43% to 73%. We have evaluated the accuracy of our method with RMSE and AUROC scores.
The average RMSE of the inferred BNs was 0. 31 placing caspo-ts in first place in comparison with the top scores reported in the HPN-DREAM challenge. Various choices made during this study may have an impact on the final score. The caspo-ts method allowed us to remove noisy and faulty experiments, leaving us with the reliable experimental settings only. Here, we made the choice to use only the reliable experiments of the learning dataset instead of using all experimental settings. Also, we did not observe all 45 proteins as we could not find connections in our PKN for all the studied proteins, leaving us with approximately 23 proteins for each cell line. Nonetheless, the obtained results are quite promising, making caspo-ts a good candidate computational method for learning models given time series datasets and a prior knowledge network.
In addition, caspo-ts can be used to pinpoint the errors in the experimental data. In particular, we discovered four experiments where the protein AKT was inhibited and had a dynamic behavior as a readout protein. Our work therefore provides a novel approach to show erroneous experiments which is crucial and complementary to current approaches. Finally, the HPN-DREAM dataset contained some noisy readings of experiments. Noisy experimental data reduces the efficiency of computational methods by increasing the variability among constructed Boolean models. To overcome this, we suggest to build automated methods to filter out the noisy experiments. This approach provides a step forward in building context dependent networks in the case of phosphoproteomic data.
Data acquisition The DREAM portal provides unrestricted access to complex, pre-tested data to encourage the development of computational methods. In this study, we are focused on the HPN-DREAM challenge, which was motivated by the fact that the same perturbation may lead to different signaling behaviors in different backgrounds, making it necessary to build a model which can perform unseen predictions (absent from the learning data). The main goal of the HPN-DREAM challenge is to learn signaling networks efficiently and effectively to predict the dynamics of breast cancer  [19]. Learning data. Reverse Phase Protein Array (RPPA) quantitative proteomics technology was used for generating the dataset of this challenge. The measurements focus on short term changes on up to 45 proteins and their phosphorylation over 0 to 4 hours.
The HPN-DREAM dataset includes temporal changes in phosphorylated proteins at seven different time points (t 1 = 0min, t 2 = 5min, t 3 = 15min, t 4 = 30min, t 5 = 60min, t 6 = 120min, t 7 = 240 min). The learning data consists of four cancer cell lines (BT20, BT549, MCF7 and UACC812) under different perturbations (�8 stimuli and �3 inhibitors). The number of perturbations varies from 24 to 32 depending on the cell line. In each cancer cell line approximately 45 phosphorylated proteins are measured against different sets of perturbations over multiple time scales. After removing perturbations with inconsistent behaviors or incomplete time series, we had 15, 13, 13 and 18 perturbations for MCF7, BT20, BT549 and UACC812 cell lines respectively measuring 23 readouts. Testing data. Test data is available for assessing the performance of networks learned from the learning data.
The HPN-DREAM portal provides testing data for four cancer cell lines (BT20, BT549, MCF7 and UACC812) under different perturbations (8 stimuli and 1 inhibitor). They contain gold standard datasets of time series predictions of up to 45 proteins having the same time scale as learning data  [19] [20] [21]. The number of perturbations varies from 7 to 8 depending on the cell line. This data is used to test the quality of the BNs given by caspo-ts. Normalization. The protein measurements were ranging over variable ranges. Maximum value based normalization was used to set the measurements between a common scale, i. e. , 0 and 1 in order to assign activation or inactivation values to variables or species of the BN. Eq (5) describes the formula used for the normalization. Given time series T P , we obtain time series T 0P : ðt 0 P j Þ i ¼ ðt P j Þ i maxfðt Q l Þ i j Q 2 P; 1 � l � kg ð5Þ where i 2 {1,.
Prior knowledge network derivation PKNs are available in different databases such as Reactome, PID, and KEGG among others  [26, [34] [35] [36] [37] [38] [39] [40] [41] [42] [43] [44] [45]. We can construct a PKN through a tool such as ReactomeFIViz  [25]  which is available as a Cytoscape  [46]  plugin. A PKN alone cannot be used to build reliable dynamical models or to explain underlying biological behaviors  [16, 47] , especially in the case of multiple perturbations data because of the need of specificity. In order to overcome this issue, methods have been proposed which take into account both literature based knowledge and experimental data to build logic models  [3, 7, 11, 12, 16].
nodeðvÞ: for v 2 V edgeðu; v; sÞ: for ðu; vÞ 2 E and ððu; vÞ; sÞ 2 s For each perturbation P 2 P and phosphoproteomic time series T P , we have the following facts: clampedðP; v; 0Þ: for v 2 P \ I clampedðP; v; 1Þ: for v 2 P \ S obsðP; j; v i ; sÞ:for s ¼ ðt P j Þ i ; 1 � j � k and 1 � i � mAvailable software. The caspo-ts github repository contains the sources as well as detailed user guide with two examples at the following address: https://github. com/ misbahch6/caspo-ts.
This work was supported by  CG 's CNRS chair of excellence, by  Ecole Centrale de Nantes , and by the  Pays de la Loire French Regional   GRIOTE  project. LP acknowledges support from  French Agence Nationale pour la Recherche (ANR)  in the context of the  ANR - FNR  project  AlgoReCell  [ ANR-16-CE12-0034 ]. The funders had no role in scale signaling networks. The software and data are publicly available at https://github. com/misbahch6/caspo-ts.
Epidemiologic Data and Analyses We collected data from the 3 major HPAI H5 epizootics in Europe: HPAI H5N1 in epidemiologic year 2005-06 (2); HPAI H5N8 in 2014-15; and HPAI H5 in 2016-17. For 2016-17, we collected data through July 31, 2017. We obtained epidemiologic data from the Animal Disease Notification System and the Directorate-General for Health and Food Safety, managed by the European Commission, and from country notifications sent to the EU Reference Laboratory for avian influenza (Animal and Plant Health Agency, Weybridge, UK). We conducted analyses to describe each epizootic, examined the geographic and temporal spread (epidemic curves), and assessed differences in clinical illness and death rates. For spatial analysis, we grouped countries into 4 regions (North, South-West, South-East, and Central Europe) on the basis of the broad migration patterns of the major migratory water bird species affected by HPAI (online Technical Appendix Figure  1 , https://wwwnc.
Viruses' Sequence Data and Phylogenetic Analyses We obtained virus HA gene sequence data from countries' submissions to the EU Reference Laboratory and from GISAID (http://platform. gisaid. org. ) We performed phylogenetic analyses on HA sequence data from each epizootic separately. We used IQ-TREE version 1. 5. 5 software  (15)  to infer maximum-likelihood trees with approximate likelihood ratio test (1,000 replicates) and bootstrap (100 replicates) support values for branches. We down-sampled each dataset using Cluster Database at High Identity with Tolerance to remove sequences with >99. 9% sequence identity  (16). We performed root-totip regression analyses using Tempest version 1. 5 on the downsampled datasets  (17). Then, we inferred Bayesian phylogenetic trees from each downsampled dataset using BEAST version 1.
Temporal Spread We determined the epidemiologic curves of the 3 epizootics (Figure  2 , panels A-C). In 2016-17, H5 was first detected in Europe in a mute swan in Hungary; the first outbreak in  †It is uncertain if for some outbreaks only the number of poultry in one farm building or if the poultry population in the area of the farm were reported. This estimate should be used as an approximation and indicator of impact. ‡One observation contained 600,000 birds, representing the overall population of backyard flocks affected in Romania. This number is an approximation. §These countries did not submit data to the Animal Disease Notification System in 2005-06; however, there is other evidence of H5N1 incursion in the period. § § § - - - - 1 1 1 148 Denmark 1 26 102 - - - - 1 49 1 69 FYROM § § § - - - - 1 1 - 438 Belgium - - - - - - - 2 3 13 4,047 Finland - - - - - - - - 15 2 - Ireland - - - - - - - - 10 - - Lithuania - - - - - - - - 5 - - Portugal - - - - - - - - 1 - - Slovenia - 28 - - - - - - 41 - - Switzerland - 9 - - - - - - 87 - - Luxembourg - - - - - - - - - 4 Highly Pathogenic Avian Influenza H5 in Europe poultry was detected 11 days later in a turkey farm, also in Hungary.
Phylogenetic Analysis Genetic analysis of the HA gene for the 2014-15 and 2016-17 epizootics shows the involvement of H5 clade 2. 3. 4. 4 in all cases where data were available (Figure  6 ). Patterns found in maximum-likelihood trees are largely in agreement with the Bayesian analysis; however, a greater proportion of the clades remain unresolved in the maximum-likelihood trees (Figure  6 ; online Technical Appendix Figure  8 ). The  This finding potentially indicates 2 major incursion pathways via wild birds. We also found smaller clusters and singleton sequences including sequences from European viruses; viruses from 2014-15 form 1 subclade, estimated to have emerged in January-February 2014 (Figure  6 ; online Technical Appendix Figure  8 ). The 2005-06 data show viruses in several subclades, but the branching pattern in  this dataset is generally less distinct and many sequences remain unresolved.
In addition, the 2016-17 epizootic demonstrated a much expanded wild bird host range compared with previous outbreaks. In light of these results, we recommend a review of the target species for avian influenza surveillance (5) to improve sensitivity of surveillance. Clarifying the precise origins of the current epizootic viruses from reported wild bird mortality data is problematic, because these data do not allow distinction between migratory carrier species and resident sentinel species. Many of the reported species are not migratory (e. g. , mute swan or little grebes) and so might play a role as regional amplifiers of viruses but not in long-distance spread  (23). Highly Pathogenic Avian Influenza H5 in Europe Epidemic curves for the 3 epizootics were significantly different. The incidence values in order of magnitude were 2016-17 > 2005-06 > 2014-15.
Molecular docking studies Molecular docking simulations were performed using AutoDock Vina  [27]  to investigate potential binding modes of a series of aryl a-aminophosphonates in the active site of TcTS and to predict the impact of substitutions to the aromatic rings of groups X-and Y-, designed to bind to the sialic acid and lactose binding sites, respectively (Table  1 ). The crystallised structure of TcTS chosen to perform the docking studies on was 1S0I (PDB code)  [17]  which is a complex (soaked) of trans-sialidase with the substrate a-(2,3)-sialyl lactose. It was considered important to use a structure representing the Michaelis complex, where residue Tyr119 has rotated into the 'closed' binding conformation, in order to evaluate potential p-p stacking interactions in the lactose binding site.
Molecular docking A structure of TcTS obtained by X-ray diffraction with the ligand a(2,3)-sialyl lactose bound (PDB code: 1S0I) was used for all docking studies. A model of the structure was prepared using AutoDock tools, which involved removing the bound ligand, mutating residue A59D, adding all hydrogens to the model, setting the lactose binding site residues Y119 and W312 to be flexible, and limiting the region of docking to the catalytic site. 3-D models of aphosphonate ligands were built using Phenix eLBOW  [32]  or CCP4 JLigand  [33]. Molecular docking experiments were run using AutoDock Vina  [27]  to screen for potential binding positions and to calculate the predicted binding energy as the docking score. 4. 3. General procedure for the synthesis of protected aaminophosphonates 8a-8k A mixture of the aldehyde (4.
IC 50 inhibition studies The hydrolytic activity of TcTS was determined using a continuous fluorometric assay by measuring the rate of release of 4methylumbelliferone from the substrate 2'-(4-methylumbelliferyl)a-D-N-acetylneuraminic acid (MuNANA) as described previously  [19]. Fluorescence was measured on a FLUOstar Omega (BMG labtech) using excitation and emission filters at 360 and 460 nm, respectively. All assays were run in 20 mM Tris-HCl and 50 mM NaCl at pH 7. 5, containing 0. 01% Triton X100 at 35 C. TcTS (50 mg/mL) was pre-incubated with each compound over at least seven inhibitor concentrations (ranging from 1 mM to 15 mM) for 10 min at 35 C, then the assay initiated by the addition of MuNANA (200 mM) to give a final reaction volume of 100 mL. The fluorescence was analysed using MARs data analysis software (BMG labtech), where a linear region of the raw data curve was selected and the slope calculated (RFU/min) which represents hydrolytic activity of the enzyme.
Mode of inhibition studies The K i determinations were performed by measuring the residual activity of TcTS in the presence of several different concentrations of MuNANA (0e400 mM) at specified inhibitor concentrations. Residual hydrolytic activity of TcTS was determined using the assay method described above. Nonlinear fitting was performed by GraphPad Prism software and K m /V max of different concentrations of inhibitors was calculated. Further Ki and its standard error were calculated using competitive, non-competitive, uncompetitive and mixed inhibition models, the best fitting model was selected as the inhibition mode of the inhibitors. Lineweaver-Burk plots were generated from the reciprocals of both substrate concentration and activity at each inhibitor concentration to present the mode of inhibition.
ACKNOWLEDGMENTS We thank  Pascal Kessler ,  William Magnant , and  Olivia Wendling  for excellent technical assistance and  Julie Johnston  ( Penn Vector Core at University of Pennsylvania ) for providing pAAV2/9 plasmid. This study was supported by  ANR-10-LABX-0030-INRT , a French state fund managed by the  Agence Nationale de la Recherche  under the  frame program Investissements d'Avenir   ANR-10-IDEX-0002-02 ,  ANR-14-CE12-0009 ,  Myotubular Trust and Sparks the Children's Medical Research Charity, Association Française contre les Myopathies  ( AFM 20323 ), and the  SATT Alsace Conectus. H. T. was supported by a  MRT  fellowship.