dataesr/scientific-paragraphs-categorization huggingface dataset:
NA: non available. Palace predictions were calculated by subtracting the total energy of each complex (E AB ) from the total energy of the individual partners in their bound conformations (E A + E B ), and scaled as follows: ∆. = + -. -. - G k cal mol E E E ( ) 5 3 2 1 (1) dissociation predicted A B AB 1 This correction is based on the high correlation (correlation coefficient equal to 0. 8) between a representative subset of experimental binding affinities  59  and the corresponding PaLaCe predictions (data not shown). Models that ended their minimization far away from the initial configuration (rmsd of the shorter chain greater than 5 Å after superimposition of the longer chain) and models that were predicted as less stable than the isolated monomers (∆G < 0) were rejected from the analysis.
csail. mit. edu/ cb/struct2net/webserver/. The Struct2Net approach relies on structure-based threading and is independent of functional genomic information such as co-expression or cellular localization. Search for Interologues in Interaction Databases. Interologues for protein pairs were retrieved with the following protocol. In a first step, for each protein sequence of the dataset, a maximum of 500 homologues were extracted from the uniprot database (release 2016_08) using the blastpgp software  60  (ncbi-blast package 2. 2. 26) with the default parameters standard (e-value = 0. 002, max number of iterations = 3). Hence, each protein of the dataset is associated with a list of homologous proteins. In the second step, protein-protein interaction data for all the proteins (queries and homologs) were obtained from the Intact 52 , MINT  61  , DIP  62  (psicquic protocol  63  ) and BioGRID  39  (specific REST interface) databases.
Acknowledgements Grant sponsor:  Agence Nationale de la Recherche  (projet MAPPING, Investissement d'avenir); Grant number:  ANR-11-BINF-003. We would like to thank  Jacques van Helden  for useful suggestions on network perturbation,  Richard Lavery  for fruitful discussions throughout this work, and  Olympe Martin Minard  for her support during the redaction and revision of the manuscript.
Background: The modeling of Biological Regulatory Networks (BRNs) relies on background knowledge, deriving either from literature and/or the analysis of biological observations. However, with the development of high-throughput data, there is a growing need for methods that automatically generate admissible models. Methods: Our research aim is to provide a logical approach to infer BRNs based on given time series data and known influences among genes. Results: We propose a new methodology for models expressed through a timed extension of the automata networks (well suited for biological systems). The main purpose is to have a resulting network as consistent as possible with the observed datasets. Conclusion: The originality of our work is three-fold: (i) identifying the sign of the interaction; (ii) the direct integration of quantitative time delays in the learning approach; and (iii) the identification of the qualitative discrete levels that lead to the systems' dynamics.
We propose in Section 5 new refinement methods applied to the generated model. We demonstrate the performance of our algorithm by a running case study example in Section 6. Then, we show in Section 7 the implementation of the modeling method and of all filters in Answer Set Programming (ASP). Finally, we illustrate the merits of our approach in Section 8 by applying it on popular reverse-engineering challenges: the DREAM4 in silico dataset, then on the real data of the breast cancer network from DREAM8 dataset. Additionally, we summarize our contribution in Section 9 and give some perspectives for future works.
Algorithm In this section, we propose an algorithm to build T-AN from time series data. We assume that the latter data observations are provided as a chronogram of size T, with T the maximum number of time points: the value of each variable is given for each time point t ∈ N + , 0 ≤ t ≤ T, through a time interval discretization (see  Definition 11   We denote Γ a a chronogram of the time series data for a component a. Algorithm 1, MoT-AN (Modeling Timed Automata networks), shows the pseudocode of our implemented algorithm. It will generate all possible timed local transitions that can realize each observed change. A change corresponds to a modification of a component expression level. It happens because of an activation or an inhibition of the changed component by a timed local transition.
ASP Syntax In this section, we briefly recapitulate the basic elements of ASP  [36] , a declarative language that proved efficient to address highly computational problems. An answer set program is a finite set of rules of the form: a 0 ← a 1 ,. , a m , not a m+1 ,. , not a n. ( ) 1 where n ≥ m ≥ 0, a 0 is a propositional atom or ⊥; all a 1 ,. , a n are propositional atoms, and the symbol "not" denotes negation as failure. The intuitive reading of such a rule is that whenever a 1 ,. , a m are known to be true and there is no evidence for any of the negated atoms a m+1 ,. , a n to be true, then a 0 has to be true, as well. If a 0 = ⊥, then the rule becomes a constraint (in which case the symbol ⊥ is usually omitted). As ⊥ can never become true, if the right-hand side of a constraint is validated, it invalidates the whole solution.
In the ASP paradigm, the search of solutions consists of computing the answer sets of a given program. An answer set for a program is defined by Gelfond and Lifschitz  [37]  as follows. An interpretation I is a finite set of propositional atoms. A rule r as given in (  1 ) is true under I if and only if: {a 1 ,. , a m } ⊆ I ∧ {a m+1 ,. , a n } ∩ I = ∅ ⇒ a 0 ∈ I. An interpretation I is a model of a program P if each rule r ∈ P is true under I. Finally, I is an answer set of P if I is a minimal (in terms of inclusion) model of P I , where P I is defined as the program that results from P by deleting all rules that contain a negated atom that appears in I and deleting all negated atoms from the remaining rules. Programs can yield no answer set, one answer set or several answer sets. To compute the answer sets of a given program, one needs a grounder (to remove free variables from the rules) and a solver.
To find the average, we divide S by Tot (Davg=S/Tot). The new transition after merging is then transAvgDelay(Y,Valy,X,Val1,Val2,Davg) (lines 83-84). According to the Definition 13, we want to find the more frequent transitions in the models. In ASP, the option "--cautious" computes the cautious consequences (intersection of all answer sets) of a logic program (algorithm implementation in ASP). Therefore, we use this option while executing.
DREAM4 In this section, we assess the efficiency of our algorithm through case studies coming from the DREAM4 challenge. DREAM challenges are annual reverse engineering challenges that provide biological case studies. In this section, we focus on the datasets coming from DREAM4 (available online at https://www. synapse. org/#!Synapse:syn3049712/wiki/74630). It provides data for systems of different size (10 genes on the one hand; 100 genes on the other), allowing us to assess the scalability of our approach. The input data that we tackle here consist of the following: five different systems each composed of 100 genes, all coming from Escherichia coli and yeast networks. For every such system, the available data are the following: (i) 10 time series data with 21 time points, and 1000 is the duration of each time series; (ii) steady state for the wild-type; (iii) steady states after knocking out each gene; (iv) steady states after knocking down each gene (i.
e. , forcing its transcription rate at 50%); (v) steady states after some random multifactorial perturbations. We processed all of the data. Here, we focus on the management of time series data. Each time series includes different perturbations that are maintained all of the time during the first 10 time points and applied to at most 30% of the genes. In this setting, a perturbation means a significant increase or decrease of the gene expression. In the raw data of the time series, gene expression values are given as real numbers between zero and one. The discretization is a crucial part for the abstraction of the data. However, in this article, we do not develop the method of the discretization, and we suppose this as already chosen. There are many ways to discretize those data; using biological databases and statistical methods, we could identify gene expression thresholds.
Training data come from experiments on four breast cancer cell lines stimulated with various ligands. The data comprise protein abundance time courses under inhibitor perturbations. Training data are provided for each of the 32 biological contexts defined by the combination of cell line and growth condition (stimulus). These data comprise time courses for about 45 phosphoproteins under a vehicle control (DMSO) and under inhibitor perturbations of network nodes, as shown in Figure  8  (Figure  8  is from https://www. synapse. org/#!Synapse:syn1720047/wiki/56061). Using these training data, participants are asked to build dynamical models that can predict phosphoprotein trajectories specific to each of the 32 biological contexts (32 cell line/stimulus pairs) and under the inhibition of phosphoproteins that were not perturbed in the training data.
Results To evaluate their precision, predicted trajectories are compared with experimental test data obtained in each of the 32 cell line/stimulus contexts and following the inhibition of phosphoprotein nodes by test inhibitors. These experiments are over the same time period as the training data (up to 4 h). To get our precision score, we used dreamtools  [43] , an open-source Python package for evaluating DREAM challenge scoring metrics. The scoring metric of this sub-challenge is based on multiple metrics; so far, we focused on the mean RMSE (Root Mean Squared Error) provided by dreamtools. In those experiments, we chose to abstract the data into a fixed number of discrete levels as for the DREAM4 experiment (see  Section 8. 1). By varying the number of level of discretization (see Table  3 ), our best score was obtained with five levels of discretization.
We illustrated the merits of this method by applying it on large real biological systems (DREAM4 and DREAM8 challenges). As a result, we obtain in a few seconds models that are proven to be relevant (this relevance is qualified in terms of mean square error using dream tools) This algorithm is implemented using ASP  [36, 44] , thus providing the exhaustive enumeration of all models. The main limit of the approach presented in this paper is the fact that the topology of the network is considered as granted. As discussed in the Introduction of the paper, there is a wide range of algorithms designed to address this issue  [41, 45]. Furthermore, such interaction graphs could be deduced from the available reliable databases of biological networks. Some examples of databases for human regulatory knowledge are: the Pathways Interaction Database  [46] , the Human Integrated Pathway DB  [47]  and the Causal Biological Network Database  [48].
Conclusions and Perspectives In this paper, we propose an approach that takes the background knowledge under the form of regulation graph and time series data as the input. The originality of our work is three-fold: (i) the identification of the sign of the interactions; (ii) the direct integration of quantitative time delays in the learning approach; and (iii) the identification of the qualitative discrete levels that lead to the systems dynamics. As a result, we produce a set of T-AN that explains the biological network evolution. Algorithm 1 is implemented in ASP. We illustrated the applicability and limits of the proposed method through benchmarks from DREAM4. We also improved our method and made it more robust against noisy and scarce data by proposing some refinements. Then, we applied it on the DREAM8 dataset: Heritage-DREAM breast cancer network.
Statistical analyses All analyses were performed using packages implemented in the R software (R 3. 0. 1; R Development Core Team 2012). The canonical correspondence analysis was performed using the vegan package. Linear and generalized linear mixed effect models were performed by the functions lmer and glmer of the R package lme4. The significance of the fixed effects was tested by model comparison by successively dropping each independent variable from the full model (drop1 function). When a qualitative fixed effect (factor) with more than two levels was significant according to the model comparison procedure, pairwise multiple comparisons by Tukey's test (function glht, R package multcomp) were performed to quantify its effect.
Acknowledgments This work was funded by the  Centre Technique Interprofessionnel des Fruits et Légumes  (CTIFL). ML's PhD thesis was supported by  ONEMA (FRB-Ecophyto, RéPARe). We thank  Deborah Loubet  and  Claude Tronel  (CEHM experimental farm) and  Tom Mormentyn  ( La Morinière experimental farm ) for allowing access to their orchards and for their help in sampling the spiders and monitoring the aphids. We thank  Catherine Boreau de Roincé  for the design of D. plantaginea primers.
Metabolomics Comparison of HCC to NTT We compared the overall metabolic profile of HCC to NTT from aqueous and lipid extracts. Tissue spectra of HCC and NTT groups were separated by OPLS-DA with aqueous extract data and lipid extract data (Figure  1A ,C respectively). Multivariate analysis showed that HCC tissue is characterized by high level of lactate (Lac) (p (corr) > 0. 7), phosphocholine (PC), phosphoethanolamine (PE), glutamine (Gln) (p (corr) > 0. 5) and low level of glucose (Glc) and monounsaturated fatty acids (MUFA) (p (corr) > 0. 7) (Figure  1B, D ). Forty-five identified metabolites were quantified from aqueous and lipid extracts, according a technique derived from  [13]. Univariate analysis showed that 23 metabolites had a significant variation (Table  2 ). OPLS-DA was performed with the quantified metabolites (data no shown).
Advantage and Limitation of Using Tissue Extract Instead of Intact Tissue in this NMR Metabolomics Study We performed our metabolic analysis on tissue extracts. Some NMR-based metabolomic studies have used intact tissue and magic angle spinning techniques  [14, 15]. In these studies, both lipid and However, we did not find a significant correlation between GS expression (H-score) and stage of differentiation (well or moderately) (R2 = 0. 44, p-value = 0. 070, Spearman test) (data not shown).
1 H-NMR Spectroscopy Spectroscopy was performed at room temperature using a Brucker Advance 400 spectrometer operating at 400. 13 MHz. The dried extracts were rehydrated in 500 µL of D 2 O containing Phosphate Buffer (1%) (Aqueous extract) or 500 µL chloroform-d/methanol-d (2:1, v:v) (lipid extract) in 5 mm diameter NMR tubes. For all samples, a one-dimensional 1 H-NMR spectrum was acquired using a ZGPRESAT sequence with water signal suppression at low power and the following parameters: 8 µs -90 • pulse length, 10 s relaxation delay, 10 ppm spectral width, 128 transients and 32 K complex data points. Resonance assignment was carried out according to chemical shift values reported in the literature  [35]  and the free access database Human Metabolome Database (HMDB). We performed 1 H-1 H COSY and 1 H- 13 C HSQC 2 dimensions NMR experiments in a few samples to definitely assign signals corresponding to phosphoethanolamine (PE) and succinate (Suc).
Data Processing A line broadening factor of 0. 3 Hz was applied to Free Induction Decay (FID) before Fourier transformation. 1 H-NMR spectra were manually corrected for phase and baseline using MestReNova (Mestrelab Research chemistry software solutions). Peak referencing was done on the signal of creatine at 3. 035 ppm for aqueous extracts and phosphatidylcholine (Ptcho) at 3. 26 ppm for lipid extracts. The spectra were binned into 0. 02 ppm width data samples (from 0 to 9 ppm for the aqueous phase spectra and from 0. 2 to 4. 4 ppm for the lipid phase spectra), then normalized to the total area under spectrum, after removing spectral regions containing solvent (water, methanol, chloroform) resonances.
There are several limitations of the proposed approach. First, the present analysis is restricted to a 10-kb resolution because of the quadratic complexity of Hi-C data. Second, our analysis is limited by the amount of higher-order interaction variable parameters that can be learned within the same model (full model) using current parameter learning programs. Most notably, all possible interaction cofactor variables cannot be included in the same model because of the cubic complexity of such model, and hence they are learned separately instead (using models (4) and (  5 )). In addition, although generalized linear models can include interactions of any order involving large protein complexes (for instance, complexes of more than 4 proteins), parameter learning is limited by the availability of data and computational resources.
Hi-C data We used publicly available high-throughput chromatin conformation capture (Hi-C) data from Gene Expression Omnibus (GEO) accession GSE62904  [21]. Hi-C experiments have been done for Drosophila melanogaster wild-type and Rad21 knock-down Kc167 cells with DpnII restriction enzyme. Hi-C data were binned at 1 and 10 kb resolutions. For human data analysis, we used publicly available Hi-C data of lymphoblastoid cells GM12878 cells from Gene Expression Omnibus (GEO) accession GSE63525  [10]. We used Hi-C data binned at 10 kb resolution.
ChIP-seq data For Drosophila analysis, we used publicly available binding profiles of chromatin proteins of Drosophila melanogaster wild-type embryonic Kc167 cells. ChIP-seq data for CP190, Su(Hw), dCTCF and BEAF-32 were obtained from GEO accession GSE30740  [60]. ChIP-seq data for Barren (condensin I), Cap-H2 (condensin II), Chromator, Rad21 (cohesin), GAF and dTFIIIC were obtained from GEO accession GSE54529  [9]. ChIP-seq data for DREF and L(3)Mbt were obtainted from GEO accession GSE62904  [21]. ChIP-seq data for Fs(1)h-L and Fs(1)h-LS were obtained from GEO accession GSE42086  [25]. Peak calling was done using MACS 2. 1. 0 (https://github. com/taoliu/MACS). For human analysis, we used publicly available binding peaks of 73 chromatin proteins (RAD21, CTCF, YY1, ZBTB33, MAZ, JUND, ZNF143, EZH2, ATF2, ATF3, BATF, BCL11A, BCL3, BCLAF1, BHLHE40, BRCA1, CEBPB, CFOS, CHD1, CHD2, CMYC, COREST, E2F4, EBF1, EGR1, ELF1, ELK1, FOXM1, GABP, IKZF1, IRF4, MAX, MEF2C, MTA3, MXI1, NFATC1, NFE2, NFIC, NFKB, NFYA, NFYB, NRF1, NRSF, P300, PAX5, PBX3, PML, POL2, POL3, POU2F2, RFX5, RUNX3, RXRA, SIN3A, SIX5, SMC3, SP1, SPI1, SRF, STAT1, STAT3, STAT5, TBLR1, TBP, TCF12, TCF3, TR4, USF1, USF2, WHIP, ZEB1, ZNF274, ZZZ3) and histone marks (H3K27me3, H3K36me3, H3K4me2, H3K4me3, H3K9ac, H3K9me3, H4K20me1) of GM12878 cells from ENCODE  [61].
injection time 120 ms, CE 30, and dynamic exclusion of 30 s. Data analysis Protein identification was performed using MaxQuant (version 1. 5. 2. 8) with the following parameters: reporter ion MS2 (iodoTMT 6plex), trypsin specific (with up to two missed cleavages), database E. coli (Uniprot August 2015, 4305 protein sequences), methionine oxidation, and cysteine carbamidomethylation were selected as variable modifications, first search peptide tolerance 20 ppm, main search peptide tolerance 6 ppm, and MS/MS match tolerance 20 ppm. Quantitative data mining was performed using Perseus 1. 5. 1. 6. Significantly changed protein expression profiles were determined using the outlier identification tool, significance A, which calculated the probability (p value) of obtaining a log ratio of at least this magnitude under the null hypothesis that the distribution of log ratios has normal upper and lower tails.
Gels were stained with Coomassie Brilliant Blue, and band intensities were estimated using ImageJ image processing software. It should be noted that, whereas OxiTMT provides accurate oxidized ratios for each cysteine individually, gel analyses reflect only the average oxidized ratio combination for all the cysteines in a given protein as widely reported in redox biology  [7]. Providing distinct results for each cysteine using the OxiTMT protocol should reflect the overall trend observed by non-reducing SDS-PAGE. In Fig. 4 , we report the results obtained for catalase. OxiTMT was able to provide the oxidized ratios for each of the four cysteines of the protein; these ratios ranged between 15 and 30%. Estimation of the oxidized fraction by non-reducing SDS-PAGE showed the protein to be 25% in the overall oxidized form in accordance with the findings by OxiTMT.
E. coli redox proteomics analysis by OxiTMT As a proof of concept for our quantitative redox strategy, we compared E. coli cells treated with 1 mM H 2 O 2 for 30 min with E. coli-untreated cells. Total cysteine and oxidized cysteine fractions in each culture were labeled as explained above, and the proteome extracts were analyzed by LC-MS. In order to investigate the changes in the proteome and the redox state of the treated E. coli cultures, the data obtained from the MaxQuant quantification results were reprocessed by Perseus. Having two sets of information, i. e. , the oxidized thiol fraction and the entire thiol content, the data can be processed to yield different sets of information. The ratio of the oxidized cysteine reporter ions between two conditions (iodoTMT2/iodoTMT1 in Fig. 1 ) reflects the change in the absolute quantity of the oxidized fraction of a given cysteine.
1 ) gives access to the change in protein expression profile between samples 1 and 2, in the same way classic ICAT or iodoTMT quantitative experiments do. In practice, to obtain these different sets of data, we calculated the ratios in each analytical replicate. The values were then averaged, and the coefficients of variation (CV) were determined. Since the two fractions of each sample received a different treatment, the samples were individually checked by labelfree LC-MS analysis to ensure that equivalent overall protein contents (data not shown) were mixed. No reporter ion intensity correction was deemed necessary. Search results allowed the identification of 1229 iodoTMT-labeled cysteines in total, associated to 580 proteins. Since the specificity of anti-TMT stationary phase is relatively low, we decided to take into account both bound and unbound fractions.
Only cysteine containing peptides labeled with iodoTMT with reporter quantification in at least two technical replicates were considered for quantitative data mining. Profile expression ratios were determined as described above, and the values (log2 scale) were submitted to Perseus software. Significantly changed protein expression profiles were determined using the outlier identification tool, significance A  [8] , as described above. The test allowed the identification of 25 significantly changed expression levels (all up-regulated) in H 2 O 2 -treated cells (see ESM S2, Table  S1 ). Up-regulated proteins belonged mostly to the oxidation reduction and generation of precursor metabolites and energy pathways (GOTERM_BP p value of 10 -5 and 10 -4 , respectively, calculated with DAVID Bioinfomatic Tool  [9] ).
However, the authors did not assess protein expression profiles so the data cannot link the generally reduced state of the proteome to the up-regulation of thioredoxin pathway proteins. We further tested the observation of a generally reduced environment following an oxidative treatment by submitting an E. coli proteome extract to a kinetic study. A 1-mM final concentration of H 2 O 2 was added to 100 μg extracts. The reaction was stopped at different times by TCA precipitation. The quantity of free thiols was then estimated by Dylight 550 maleimide sulfhydryl-reactive dye labeling. Twenty micrograms of each time condition were separated by SDS-PAGE and revealed by fluorescence and Coomassie staining. The estimated free thiol quantity was corrected by the total protein amount in each lane in order to draw the kinetic plot (Fig.