dataesr/scientific-paragraphs-categorization huggingface dataset:
In the future, we intend to extend it with complementary algorithms developed by other groups, including algorithms to combine the strengths of module network methods with gene-based methods  [18] , to incorporate physical protein-protein or protein-DNA interactions as a prior in the regulator assignment procedure  [13]  or to infer module networks from multiple species simultaneously  [19]. A document detailing guidelines to implement new functions in the Lemon-Tree Java codebase is available on the project wiki.
Benchmark between Lemon-Tree and CONEXIC We downloaded gene expression and copy number glioblastoma datasets from the Cancer Genome Atlas (TCGA,  [3] ) data portal and we selected a set of 250 samples that were matched for copy number and gene expression data. We built a matrix of gene expression ratios (normal/disease) and discarded genes having a flat profile (standard deviation <0. 25), keeping a total of 9,367 genes. To build a list of candidate regulators, we applied the program JISTIC on copy-number profiles to determine genes that were significantly amplified or deleted in the samples (with a default q-value cutoff of 0. 25), and we selected the top 1,000 genes for each category as input for the candidate regulators for both CONEXIC and Lemon-Tree. To run CONEXIC, we followed the instructions of the manual and more specifically used the recommended bootstrapping procedure to get robust results.
For the Single Modulator step (initial grouping of genes into modules), we performed 100 bootstrap runs, with 10,000 permutations each. We selected the regulators that appear in at least 90% of the runs for the final Single Modulator run. We also performed 100 bootstrap runs for the Module Network step (learning the modulators that best fit the data and improving the grouping of genes into modules). We selected regulators appearing in at least 40% of the bootstrap files for the final Module Network run. The final network was composed of 281 modules and 6,292 genes. For Lemon-Tree, we generated 150 two-way clustering solutions that were assembled in one robust solution by node clustering (minimum weight 0. 33), resulting in a set of 257 clusters composed of 5,354 genes. Then we assigned the regulators using the same input list as with CONEXIC, with 10 hierarchical trees for each module.
A global score was calculated for each regulator and for each module and we selected the top 1% regulators as the final list. The GO enrichment for the CONEXIC and Lemon-Tree clusters were calculated using the built-in tool of the Lemon-Tree software package, which is based on the BiNGO Java library. The same list of reference genes, GO ontology file and annotation file were used for the two sets (see the latest version for the gene ontology file at http://geneontology. org/page/download-ontology, and the latest version for human gene association file at http://geneontology. org/page/download-annotations). To compare the GO categories between Lemon-Tree and CONEXIC, we built a list of all common categories for a given pvalue threshold and converted the corrected p-values to converted the corrected p-values to log 10 (p-value) scores.
Acknowledgments This research was supported by  Roslin Institute Strategic  Grant funding from the  BBSRC (TM). The research leading to these results has received funding from the  European Communitys Seventh Framework Programme  ( FP7/2007-2013)  under grant agreement number  FP7-HEALTH-2010-259348  ( ASSET  project). EB and LC are members of the team " Computational Systems Biology of Cancer ",  Equipe labellisée par la Ligue Nationale Contre le Cancer. We downloaded all the human protein-protein interactions (PPI) from  Reactome ,  Intact and HPRD through the Pathway Commons portal. The resulting network was composed of 9,599 genes and 168,117 interactions. We calculated the shortest paths between all pairs of genes in the network, using Dijkstra's algorithm from the JUNG library (http://jung.
A large number of genes involved in lateral root (LR) organogenesis have been identified over the last decade using forward and reverse genetic approaches in Arabidopsis thaliana. Nevertheless, how these genes interact to form a LR regulatory network largely remains to be elucidated. In this study, we developed a time-delay correlation algorithm (TDCor) to infer the gene regulatory network (GRN) controlling LR primordium initiation and patterning in Arabidopsis from a time-series transcriptomic data set. The predicted network topology links the very early-activated genes involved in LR initiation to later expressed cell identity markers through a multistep genetic cascade exhibiting both positive and negative feedback loops. The predictions were tested for the key transcriptional regulator AUXIN RESPONSE FACTOR7 node, and over 70% of its targets were validated experimentally.
TDCor was run on a subset of ;100 genes known to be involved in auxin biosynthesis, transport, or signaling pathways, plus lateral root initiation, patterning, or root apical meristem (RAM) maintenance. The predicted network topology links the very early activated genes involved in LR initiation down to the late activated cell identity markers through a multistep genetic cascade exhibiting both positive and negative feedback loops. We experimentally validated TDCor predictions for the key regulator ARF7 using an ARF7-GR transcriptomic data set and chromatin immunoprecipitation-PCR. Interestingly, TDCor predicts that the selected genes cluster in two major groups. The first group is predicted to be positively regulated by ARF7 and ARF19 and to repress the second group. The second group is predicted to be positively regulated by MP, ARF6, and ARF8 and to repress the first group.
(C) Gene regulatory network inference algorithm often make the assumption that if a gene X regulates a target Y positively, Y expression profile is similar to X expression profile but shifted forward in time. Conversely, if X regulates Z negatively, the profile of Z is expected to resemble the inverted profile of X. Here, we show that this principle stands in the case of heterogeneous cell populations when the interaction is linear (Supplemental Methods 1). (D) Two expression profiles from the LR data set (of the SHR transcription factor and its direct target SCR), which follows the rule illustrated in (C). statistical dependency between Xðt 2 dÞ and YðtÞ, the null hypothesis that X regulates Y following the assumptions shown in Supplemental Table  1  can be rejected. We next considered a heterogeneous cell population made of two homogeneous groups of cells in constant proportions, which could be for instance a very simple nongrowing and nondeveloping organ made of only two cell types.
As predicted, we observed a strong relationship in the individual groups of cells in both cases (Figures  2C  and 2D , blue curves), although this relationship was linear in the first case while non-linear in the second case. However, at the wholeorgan scale, a relationship existed between transcripts level only when f was linear (Figures  2C  and 2D , blue curves). These results were then generalized to the case where the organ is composed of n homogeneous groups of cells in changing proportions (Supplemental Methods 1). In brief, GRN inference from timeseries transcriptomic data obtained from heterogeneous cell populations like roots is possible, but limited to linear interactions, or more realistically nearly linear interactions. TDCor, an Algorithm Based on Time-Delay Correlation to Infer Linear Regulatory Interactions from Time-Series Transcriptomic Data Taking advantage of our ability to synchronize Arabidopsis LR development following a root gravitropic stimulus  (Ditengou et al.
, 2008; Laskowski et al. , 2008; Lucas et al. , 2008; Péret et al. , 2012) , a time-series transcriptomic data set (herein after referred to as the LR data set) was generated encompassing every LR developmental stage from preinitiation to postemergence  (Voß et al. , 2015; Figures 1A and 1B). In our experimental conditions, stage I LR primordia are detected in Columbia-0 (Col-0) ;15 h after gravistimulation (hag) and LRs emerged around 42 hag. Root bends were microdissected every 3 h from 6 to 54 hag and RNA extracted to cover the entire process of LR development. At time point 0, a mature root segment located between the bend and the shoot was harvested at 9 h after an inductive gravitropic stimulation. An initial study of well-characterized regulators and their targets within the LR data set revealed that, as expected, the expression profile of the target is often highly similar to the expression profile of its regulator and shifted in time.
For example, the target of the SHORT-ROOT (SHR) transcriptional regulator termed SCARECROW (SCR)  (Di Laurenzio et al. , 1996; Helariutta et al. , 2000; Sozzani et al. , 2010)  closely followed the dynamic changes in SHR mRNA abundance within the LR data set with a time delay estimated around 2 h (Figure  1D ). Similar relationships were identified for other well-known regulators and their direct targets, such as ARF19 and LBD16  (Okushima et al. , 2005a (Okushima et al. , , 2007)). Hence, we concluded that a GRN inference approach based on the LR data set could potentially be employed to reconstruct the GRN controlling LR formation. We developed the TDCor algorithm specifically to identify linear interactions, as only linear (as opposed to nonlinear) interactions may be inferred from the LR data set (as described above).
A detailed description of TDCor can be found in Supplemental Methods 2. TDCor reconstructs the network topology from a subset of expression profiles (typically around 120 genes) in four main steps (Figure  3 ). First, TDCor requires data preprocessing from the LR data set to obtain expression profiles of the genes selected. For each gene, an exact interpolating cubic spline function of the normalized expression profile is generated. Second, TDCor builds a list of plausible interactions. This is achieved by performing an extensive pairwise and bidirectional testing of whether a sufficient linear model with time delay exists between the expression (A) Schematic representation of the system that was modeled. We considered a simple noncombinatorial interaction between a regulator X and its positive target Y occurring in an organ made of two homogeneous groups of cells in equal proportion.
The interaction was modeled using the ODE in equation (E). (B) Two profiles were taken from the LR data set to be used as template for gene X expression in the two groups of cells. The transcript accumulation profile of gene X in the whole organ was obtained by averaging the transcript accumulation profiles in the two groups of cells. (C) The X-Y interaction was first modeled using a linear model (linear regulation function shown on the left of the panel). The profile of gene Y in each of the two homogeneous tissues was obtained from the corresponding profiles of gene X in these two tissues (shown in  [B] ) by solving equation (E) while taking the linear regulation function as function f. The profile of gene Y in the whole organ was obtained by averaging the computed profiles of gene Y in the two groups of cells.
To assess the relative performance of the three algorithms on the lateral root data set, we determined how many of 23 already TDCor uses a double bootstrap protocol. The parameter values are randomly chosen within a user-defined parameter space at the beginning of each big iterative loop. Node order analysis is randomized before each pruning filter and the last part of the pruning procedure is itself repeated several times with the same parameter values (but different order of nodes analysis). MRST, master regulator and signal transducer, i. e. , a gene that could be posttranslationally activated in response to the stimulus and could start the whole genetic cascade. TPI, triangle pruning index; DPI, diamond pruning index. These two indices are numerical topology predictors for respectively some specific three-and four-gene motifs (Supplemental Methods 2).
experimentally validated regulatory interactions were predicted (Supplemental Table  7 ). A surprising low number of interactions was predicted by VBSSM and TDARACNE. VBSSM was able to correctly predict only one interaction (BBM self-regulation) out of 23. In most cases, these direct interactions were misinterpreted by VBSSM as coregulation of low significance. TDARACNE consistently predicted five interactions out of 23 (22%), while seven others were misinterpreted as coregulation in the topology obtained with low stringency parameters (Supplemental Table  7 ). This suggests that the LR data set was not suited for these algorithms. On the other hand, TDCor was able to correctly predict 14 out of the 23 interactions (60%) described in the literature, flagged another one as coregulation, and failed to predict the remaining eight (Supplemental Table  7 ).
All five were validated as ARF7 primary targets using the ARF7-GR data set, although ARF4 is potentially both a primary and secondary target of ARF7 (Figure  4B ). Consistently these genes display between one and four canonical ARF motifs termed AuxRE in their promoters (Supplemental Table  10 ). Furthermore, binding of ARF7 to the target promoters was confirmed by chromatin immunoprecipitation-quantitative PCR (ChIP-qPCR) for ARF19, LBD16, LBD29, and IAA19 (Supplemental Table  10 ; Figure  4C ). TDCor also correctly predicted the previously documented double regulation of LBD16 by ARF7 and ARF19 (feed-forward loop). GATA23, LBD17, PIN7, IAA11, and AFB3 are located one edge downstream of ARF7, but their index of directness predicts they are likely to be indirect targets of ARF7 (Figure  4A ).
GATA23, LBD17, PIN7, and IAA11 are validated by the ARF7-GR data as positive secondary targets of ARF7 (Figure  4B ). AFB3 was not validated as negative secondary target of ARF7 (Figure  4B ). The estimated delay between ARF7 and AFB3 is 6 h, which would be too long to test using the ARF7-GR data (which was harvested 4 h after treatments). In total, eight out of the 10 genes located one edge downstream of (A) Topology of the network predicted by TDCor corresponding to genes located up to two edges downstream of ARF7 (bootstrap > 10%). The TMO5 gene is predicted to be a negative target of ARF4 and to downregulate several of ARF7 targets. For clarity, this node was removed from the network. TDCor also predicted that the activity of the ARF7 protein could be regulated posttranslationally upon gravistimulus application to start a transcriptional cascade (denoted by the blue "Stimulus" node).
However, the ARF7-GR suggests that these two genes are primary targets of ARF7. The four predicted positive targets that were not validated were AFB2, SHP1, KRP2, and ARF1. None of the four predicted negative secondary targets of ARF7 could be validated by the ARF7-GR data. ARF9 is an indirect (A) Whole-network topology predicted by TDCor. The layout shows two groups of genes featuring different sets of positive ARFs (yellow nodes) and negative ARFs (green nodes). Within each group, genes regulate each other positively (red edges), while each group regulates the other negatively (blue edges). SHP1 is the only exception to the rule as it is predicted to be positively regulated by members of the two groups. (B) Expression profile of PLT5 (black) and PHV (red) in the LR data set. PLT5 is predicted to be part of the group featuring ARF7-ARF19, while PHV is predicted to be part of the other group, which features ARF6-ARF8-MP.
The high similarity that can be observed between the two profiles during the first 30 h suggests that PLT5 might be indirectly positively regulating PHV. (C) Expression profiles of MYB52 (black) and MYB56 (red) in the LR data set. These profiles could be compatible with these two genes acting as intermediary links between PLT5 and PHV. target of ARF7 but is predicted three edges downstream of ARF7. Eleven other genes are positive indirect targets of ARF7 according to the ARF7-GR data (CRF2, CRF3, PIN1, LBD4, IAA2, EXPA20, LAX3, MEL1, MAB4, GH3. 6, and CLE44) but were not predicted as such by TDCor. Hence, comparison of the ARF7-GR data with TDCor predictions from the LR data set validated most of the predicted ARF7 targets. The genes that are the more strongly and specifically regulated by ARF7 in the ARF7-GR data set were correctly predicted as ARF7 targets.
TDCor was also able to successfully differentiate between primary and secondary targets. Ninety percent (9/10) of the genes located one edge downstream of ARF7 could be validated as such, and for all of them but one, the predicted directness was correct. The validation rates of the genes located up to two edges downstream, which would correspond to two regulatory steps downstream or more, is very good though lower (20/28; 71%). It needs to should be stressed that the ARF7-GR data set was designed to detect targets regulated only 4 h after treatment, which may be sufficient for two regulatory steps. Overall, TDCor recovered >65% (21/31) of ARF7 targets. Besides the ARF7 node, a number of predictions from TDCor were consistent with published data. For instance, most ARF7 targets are predicted as repressed by the chromatin-remodeling factor PICKLE (PKL)  (Fukaki et al.
We noted that the genes predicted to be at the bottom of the Genes annotated as transcription factors displaying an expression profile in the LR data set showing both high correlation (>0. 8) with the profiles of ARF8 shifted forward in time by 3 h and a high correlation (>0. 8) with the expression profiles of PLT1 shifted backward in time by 3 h. Some additional criteria have been taken into account to narrow down the list of missing link candidates, in particular, the value of the estimated time shifts between the profiles of these genes and those of ARF8 and PLT1 (Supplemental Methods 5). ARF7/ARF19-activated cascade (e. g. , PLT5 and PLT7) have relatively similar expression profiles to the genes predicted to be at the top of the second cascade (e. g. , PHV and ANT), particularly during the first 35 h of the LR time series.
However, the time shift between these genes (around 8 to 10 h) exceeds the TDCor upper limit (Figure  5B ). This suggested that the predicted network topology could be artificially cleaved into two parts simply because the genes establishing the link between the two blocks could be missing from our list. For this reason, we searched for genes that could potentially be acting as missing links between PLT5-PLT7 and ANT-PHV. We searched in a list of ;1060 transcription factors from the AtTFDB database  (Davuluri et al. , 2003; Palaniswamy et al. , 2006)  for genes displaying an expression pattern with high correlation (>0. 8) and +3 h time delay with PLT5/PLT7 and high correlation (>0. 8) with 23 h time delay with ANT/PHV in the LR data set (Table  1 , Figure  5C ). We also noted that PLT1 has many predicted targets but no regulators.
Here, we performed GRN inference on a LR time-series transcriptomic data set  (Voß et al. , 2015). The LR data set was generated from tissues composed of multiple cell types rather than a homogenous cell population. Our analysis revealed that only linear interactions or more realistically close-to-linear interactions could be inferred from such data. We developed an algorithm called TDCor specifically designed to infer close-tolinear interactions from a time-series transcriptomic data such as the LR data set. While designing TDCor, we paid careful attention to two points. First, in order to obtain accurate estimates of the delay between expression profiles, which is crucial information for network reconstruction, we combined four methods of delay estimation. Second, to limit the number of systematic errors made by our algorithm, we developed novel filtering methods based on modeling of small network motifs.
e. , excessive pruning). From the combined analysis of the TDCor predictions, the transcript profiles in the LR data set, and our ARF7-GR data, the following model can be proposed for the functioning of the LR GRN. Before the gravistimulus (t = 0 h), according to our transcriptomic data, very few genes related to LR organogenesis are expressed at high level in the root. One of them is ARF7. TDCor predicts that at t = 0, the ARF7 protein is in an inactive state and subsequently is activated during the first 6 h after gravistimulation. The most likely hypothesis is that ARF7 is bound to Aux/IAAs repressors. Upon gravistimulation (0 to 6 h), auxin redistribution  (Ditengou et al. , 2008)  triggers Aux/IAAs degradation, enabling ARF7 to activate its targets. Among the latter targets are the auxin transporters PIN7, PIN3, PIN1, and AUX1 (according to TDCor predictions, our ARF7-GR data, and previous studies;  Laskowski et al.
Lateral Root Data Set To generate this data set, 3-d-old Col-0 plants were gravistimulated at t = 0 as described  (Lucas et al. , 2008). The root bends subsequently formed by gravity-induced downwards reorientation of the root apices were cut every 3 h from 6 to 54 hag and used for microarray analysis with the Affymetrix ATH1 array. For the first time point corresponding to t = 0, young unbent mature root material from the 9-h time point seedlings was used. For each time point, four independent biological replicates were generated  (Voß et al. , 2015).
TDCor The TDCor algorithm was run in R with the parameters indicated in Supplemental Table  13. This set of parameters, which leads to a "soft" pruning of the network, was determined by manual optimization. The performance of the algorithm was estimated based on interactions reported in the literature and plausibility of the predictions given the available knowledge (Supplemental Table  7 ). Network predictions were visualized and analyzed using Cyctoscape software  (Shannon et al. , 2003; Cline et al. , 2007; Smoot et al. , 2011). The code has been made available in the form of an R package called "TDCor" that can be downloaded from the CRAN repository (http://cran. r-project. org/) and directly installed in R version $ 3. 1. 2. In complement to the package documentation a step-bystep tutorial is provided in Supplemental Methods 4.
ACKNOWLEDGMENTS We thank  Masao Tasaka  for providing us with the seeds of the PRO PUCHI : GFP-PUCHI line and  Ben Scheres  for providing us with the seeds of the PLT-YFP lines. We thank  Sayaka Isu  ( The University of Tokyo ) for technical support. J. L. was the recipient of a PhD grant from the  French Ministry for Research and Higher Education. We acknowledge the support of the  Région Languedoc-Roussillon  (" Chercheur d'Avenir " grant to S. G. and L. L. ), an  Agropolis Fondation "Rhizopolis" Federative  grant (J. L. , S. G. , M. L. , and L. L. ), the  Japanese Society for the Promotion of Science (Research Fellowship  to Young Scientists; T. G. ), a  Biological and Biotechnology Science Research Council (BBSRC)  responsive mode grant ( BB/H020314/1 ; U. V. and M. J. B. ), a  BBSRC Professorial Research Fellowship award  ( BB/G023972/1 ; K.
In our proposed platform, agents are necessary to initiate and drive the execution of processes. Based on key concepts described previously, we design our system components based on three main entities: Actors, Events and Objects. Pools in H-BPM should be designed as 'Roles' entities. And agents indicates 'Actors', Objects are designed by 'Objects' and finally events are defined as 'Events' for agents' input/output flows. Actions performed by actors in H-BPM are implemented in 'Roles' (Fig. 5. ). Based on this presented model H-BPM, we baptize a software toolkit BP-EMC² as 'Business Process -Evolutive Management by Collaborative agents in the Cloud'. The remaining part in the next section is devoted to the description of this toolkit.