dataesr/scientific-paragraphs-categorization huggingface dataset:
Similarly, the knowledge sources may not be produced for the same purpose and may not cover the same subject. Nevertheless, some projects aim to overcome these issues through different data fusion methods. The principle consists of linking the data and knowledge available in the various sources into a vast knowledge resource that is made available to all through a single endpoint. For example, the Linked Open Data cloud initiative brings together open source knowledge bases, linking them via nodes and links. In the area of drug repositioning, a key issue is the availability of these data and knowledge because drug information is not always made available by the companies working on the drugs. It is possible to learn new knowledge and observations about drugs from those already available. Indeed, many studies implement learning algorithms that can, from the available data, determine whether a drug is potentially capable of binding to a new target  8.
The OREGANO project aims to fill the missing gap in terms of computational-based drug repositioning, and performs link prediction on a large knowledge graph of heterogeneous data for discovering missing molecule-target links. Unlike similar studies, the OREGANO knowledge graph emphasizes the integration of natural compounds (i. e. herbal and plant remedies). Indeed, for three decades (1981-2010), more than 60% of drugs were developed from natural products, derivatives or natural product-like compounds  33. Some prominent herbal prescriptions have been transformed into new drugs  34  (e. g. Layla Tab in Korea  35  ). Therefore, we hypothesize that there is a strong possibility to seek new indications for existing herbal compounds, which could lead to the development of new drugs through repurposing strategies.
Methods This section describes the steps involved in building the knowledge graph (Fig. 1 ), i. e. the data selection, the actual construction and the data filtering performed via several stages in this work. The information to be acquired from the knowledge sources was selected using the model that was produced and according to their ability to be integrated (subsection A. ). The first version of the OREGANO knowledge graph  32  was built using the knowledge available in Bio2RDF  12. The utility of this resource resides in its ability to establish links and cross-references among the various knowledge bases it incorporates. Nevertheless, Bio2RDF has not been updated since 2014. Consequently, the information contained in this resource does not align with the content present in the updated iterations of the knowledge bases.
In the second version of the OREGANO Fig. 1  Overall workflow. After selecting the types of nodes which are of interest for drug repositioning, the labels of the links connecting these types are retrieved from the Relation Ontology  73. The nodes and the way there are connected form the model of the knowledge graph. The sources to be integrated are chosen according to the nodes represented in the model and their free online availability. Each knowledge source has its data extracted by a dedicated wrapper. These data are processed by a binder and a manager to populate thereafter the model and thus generate the OREGANO knowledge graph. knowledge graph, we have thus opted to incorporate the data directly from knowledge source repositories. This approach allows us to construct a knowledge graph enriched with up-to-date information.
The integration process follows the Extract, Transform and Load (ETL) principle: the wrappers extract the data (subsection B. ), which are then transformed by the binders (subsection C. ) and managers (subsection D. ). A. Description of the selected knowledge sources. The process of building the OREGANO knowledge graph first required selecting the knowledge sources to be integrated. This choice was made in coherence with the previously designed OREGANO knowledge graph, which was built from data available on the Linked Open Data (LOD)  32. The same set of sources was therefore chosen with the intention of reinforcing the integration process used to develop the first knowledge graph. The difference is that rather than retrieving the sources through the Bio2RDF proxy, we have included them in the current version as they were initially (official repository).
The version used in the current work is dated back April 1, 2023. It contains respectively 16,306 drugs and 4,939 targets. SIDER contains information on drugs and their side effects  40, 41. The most recent version, which was released on October 21, 2015, comprises 139,756 pairs of 1,430 drugs and 5,868 side effects. This information was derived from data from clinical trials, in which participants were observed, and any side effects were noted. Prior to these human testing phases, animal testing also contributed to the data. The resource also lists the disease indications for the included drugs. UniProt is a resource about proteins. It provides in particular their sequences, annotations and information about their functions in the organism  42. The European Bioinformatics Institute (EMBL-EBI), the SIB Swiss Institute of Bioinformatics and the Protein Information Resource (PIR) have collaborated to create and maintain it.
The version we used for OREGANO was released on September 13, 2023 and aggregated information on 570,157 proteins. More specifically, UniProt contains several data sources, including UniProtKB that provides information about proteins. The latter comprises data from Swiss-Prot 43 and TrEMBL  44. Since the TrEMBL protein dataset is not renewed, only the Swiss-Prot data are considered in the integration process. Reactome is a knowledge source containing data on biological pathways  45. Created in 2003, it contains 2,629 human pathways binding 1,114 drugs, 14,277 proteins and 2,004 small molecules through 14,628 reactions. In this work, we used release 85, which was published in June 2023. PharmGKB is a pharmacogenetic resource combining knowledge of genetic variation and drug responses  46, 47.
iii. Natural compound-related source. NPASS is the only source not present in Bio2RDF that was included in the first version of the OREGANO knowledge graph to complete data with knowledge about natural compounds. NPASS contains information on natural compounds and their targets in organisms  52, 53. It comprises respectively 96,481 natural compounds and 7,753 biological targets linked by 958,866 relationships. The last update was on October 4, 2023. iv. Drug-related source. Anatomical Therapeutic Chemical (ATC) classification has been developed by the WHO Collaborating Centre. It categorises active substances according to their chemical properties and their actions in the body. Each compound is assigned a 7-character code consisting of five levels that reflect the compound's place in the classification according to the system in which it acts and its pharmaceutical and therapeutic effects.
v. Additional sources. Unified Medical Language System (UMLS) is a resource that gathers more than 150 knowledge sources (i. e. (bio)medical coding systems and terminologies) composed of terms and relationships between them  54. Since OMIM did not provide external links to connect it to other knowledge sources, UMLS was used to ensure proper integration of OMIM. Orphanet is an online resource about rare diseases  50. Cross-references for these diseases are not available via HPO (in which Orphanet is included), so extraction of data from Orphanet was necessary to ensure optimal HPO integration. vi. Existing cross-references. Finding the mappings that the knowledge sources make available is helpful to facilitate the integration process while building the knowledge graph, which involves combining the data.
In such cases, the use of an additional knowledge source as a pivot was required. As mentioned in the previous subsection, UMLS and Orphanet acted as pivots to enable the integration of OMIM and HPO, respectively. B. Extraction of data from knowledge sources. First, data were extracted from the knowledge sources that have been selected for their potential utility in drug repositioning. Then, their structure was analysed to retrieve the relevant information from each source (Table  1 ). The formats of the sources could differ, but each of them underwent the same overall processing. The wrapper (top part of Fig. 3 ), being the module that handled this step, first scanned the resource and then extracted the data deemed of interest for drug repositioning. Then, the resulting data were formatted into triples of the form (subject, predicate, object).
The formats were quite similar, but the way the data were organised (e. g. columns for TSV or tag types and their structure for XML) and the initial data filters varied, so each knowledge source had its own wrapper. Only information deemed pertinent to drug repositioning was included in the OREGANO knowledge graph, as the knowledge sources are overly large datasets. Since some sources provided information on the quality of their content, these were used to keep only the most relevant relations. The NPASS data were filtered out because the targets come from a multitude of organisms (e. g. Angelica Gigas, Bos taurus or Saccharomyces cerevisiae). Therefore, only those relevant to humans were selected. Only PharmGKB compounds associated with ATC codes with a length of seven digits were retained, as the other ATC codes correspond to drug classes (i.
The external links are used to link the data via a binder that achieves a mapping table for each type of node. It is from this mapping table that the manager converts the triples to be integrated by replacing the source codes by the OREGANO codes. This integration process follows the traditional ETL principle. • Frequent: present in 30% to 79% of cases. • Very Frequent: present in 80% to 99% of cases. • Obligate: present in 100% of cases. Below 30%, the frequency of occurrence of a symptom is thus occasional to rare. We thus made the choice to select only the phenotypes whose frequency of occurrence is greater than or equal to 30% in order to represent the diseases with the symptoms that most often describe these diseases. For the links that did not have such an indicator, the evaluation of the evidence of these links was used.
UniProt is a very large knowledge source about genes and protein targets. Only the links between the nodes already present in the graph were conserved, in order to avoid introducing too many links that were not connected to the nodes of the graph. The triples generated by the wrapper contained both edges of the knowledge graph and cross-references. The relationships were provided as input to the manager, while cross-references were directly transmitted to the binder. C. Fusion of nodes from distinct knowledge sources. Knowledge sources did not necessarily provide mappings to other sources. In this case, a connection had to be established between the knowledge source to be integrated and the others. The connection could be created according to external data or knowledge sources and, if possible, a new source providing this link was added/used.
This was the role of the binder. The binder (middle part of Fig. 3 ) handled the creation and merging of nodes with the addition of new identifiers specific to the OREGANO knowledge graph into a global mapping table. To do this, a cross-reference match was sought to determine whether the node was new to the data. Depending on the cross-references, multiple candidates could exist for the same node. The goal of the binder algorithm was thus to be able to choose the best match for optimal data fusion. The correspondence of nodes was evaluated based on the number of common cross-references between two nodes. In the mapping table, each column corresponds to a knowledge source and each row represents a node with an OREGANO identifier in the first column. The binder also linked any OREGANO identifier to a new node if it was not already in the graph.
D. Constitution of the knowledge graph. The last algorithm of the integration process is performed by the manager algorithm. Indeed, the link manager (bottom part of Fig. 3 ) combined the links extracted by the wrapper and the mappings generated by the binder to format the links. Thus, the links with the original codes in the integrated knowledge sources were replaced by the OREGANO identifiers of the associated node in the mapping table. The seven knowledge sources were integrated in a fully automatic way. The data were merged by evaluating whether the nodes were new or already present in the mapping table based on the cross-references. A node was not integrated if no linkage could be made for it.
However, as noted in criterion 4, scripts can be used to update the knowledge graph information in the future. Table  4. Quality assessment table for the OREGANO knowledge graph, according to the criteria defined in Chen et al. 57. The level of consistency against these criteria is presented and ranked from perfect consistency (+++) to inconsistency (-). Comments specifying the level assigned are provided in the second column. Table  5. Results of the different embedding algorithms. MRR stands for Mean Reciprocal Rank and Hit@N corresponds to the probability that the correct answer will be found in the first N hits.
Knowledge graph Type of nodes Knowledge resources Number of nodes Number of links OREGANO Compound; Target; Gene; Disease; ATC; Phenotype; Pathway; Effect; Activity; Indication; Side-effect DrugBank; UniProt; Human Phenotype Ontology; PharmGKB; NPASS; SIDER; Reactome 88,937 824,231 Cheng et al. 16 Drug; Target KEGG BRITE; BRENDA; SuperTarget; DrugBank 1,921 5,127 Condition and Designated; Gene; Zhu et al. 22 Protein; Drug; Cell; Tissue; DATA; Chemical; Human phenotype; Rare GARD; Orphanet; MONDO; OMIM; HPO 3,819,623 84,223,681 Diseases and 32 different rare disease categories from GARD Ye et al. 27 Drug Meyler's Side Effects of Drugs 15th edition; Side Effects of Drugs Annuals (2007-2012); Citeline Pipeline, Thomson 1,647 17,400 Reuters Partnering and GeneGo; SIDER Entrez Gene; Labeledin; MEDLINE; MeSH; Pathway Hetionet 29,30 Anatomy; Biological process; Cellular component; Compound; Disease; Gene; Molecular function; Pathway; Symptom Pharmacologic Class; Side-effect; Interaction Database; Disease Ontology; DISEASES; DrugCentral; Gene Ontology; GWAS Catalog; Reactome; Incomplete Interactome; Human Interactome Database; DOAF; ehrlink; Evolutionnary Rate Covariation; hetio-dag; LINCS L1000; TISSUES; Uberon; WikiPathways; BindingDB; DisGeNET; DrugBank; MEDI; PREDICT; SIDER; Bgee; 47,031 2,250,197 STAGEO PrimeKG 31 Biological process; Protein; Disease; Phenotype; Anatomy; Molecular Pathway; Exposure function; Drug; Cellular component; The Comparative Toxicogenomics Database; DisGeNET; Disease Ontology; DrugBank; Drug central; Entez Gene; IntAct; CORUM; Reactome; SIDER; Uberon; UMLS MONDO; Orphanet; TRANSFAC; BioGRID; STRING; MINT; Gene Ontology; Human Phenotype Ontology; Mayo clinic; 129,375 4,050,249 OpenBioLink 74 GO Term; Gene; Disease; Phenotype; Anatomy; Drug; Pathway STRING; GO; DisGenet; Human Phenotype Ontology; UBERON; UniProt; NCBI; PubChem; Reactome; KEGG Bgee; STITCH; CTD; DrugCentral; SIDER; Drug Ontology; 184,732 9,302,547 BioKG 75 Genetic Disorder; Disease; Drug; Pathway; Protein DrugBank; The Human Protein Atlas; Cellosaurus; Intact; CTD; MedGen; MESH; SIDER; InterPro; SMPDB; UniProt; Reactome; KEGG; Hijazi20 76 105,524 2,065,094.
Here, we aim to provide a comparison of four search methods, as well as four clustering algorithms, on test cases by means of the nested_fit program, which is based on the nested sampling algorithm  [1]. We will compare the accuracy and number of likelihood calls of the different methods on several benchmark tests. We will also discuss the choice of the stopping criterion for the specific case of the harmonic potential. We briefly present the nested sampling in Section 2 and the nested_fit program in Section 3, where we also describe new search and clustering algorithms as implemented in nested_fit. We compare the different algorithms on some test examples in Section 4, including the harmonic potential. In the same section, we also compare the performance of nested_fit with other nested sampling programs written in the same programming language (Fortran).
Uniform Search The first search algorithm that was added to nested_fit is the uniform search around the live points. It was introduced in Ref. [18]  and implemented in dynesty code  [4]  with cubes and spheres. The structure of this method is presented in Algorithm 2. d is the dimension of the problem, L = L(θ old ) and θ n = θ new. θ a is the starting point which is chosen randomly for the set of K live points, and σ is the standard deviation of the points belonging in the same cluster as θ a. If no clustering has been performed, σ is the standard deviation of all points. The method to find a new starting point in Line 13 is the same as in the random walk case  [15]. N is the number of points rejected before a new starting point is found and N N × N is the number of points rejected before a cluster analysis is performed (if clustering is used).
Density-Based Spatial Clustering for Applications with Noise Density-based spatial clustering for applications with noise (DBSCAN)  [20]  was recently implemented as a post-processing step to recluster points sampled by MultiNest  [3]  since the clustering methods used in the program (X-means and k-means) were not adapted to a specific problem (microseismic events)  [21]. DBSCAN requires two input parameters that are chosen by the user: a radius and a minimal number of neighbors m. Using these parameters, the points are divided into three categories: • Core points. Those are the points that have at least m points-themselves included- within an distance. • Border points. They are within an distance from l core points with 1 ≤ l ≤ m -1. • Outliers. Those are the other points. (An outlier can be within an distance from a border point.
Comparison with Polychord and MultiNest Finally, we compare the performance results of nested_fit with the ones of two other programs, implementing the nested sampling algorithm and based on the same programming language (Fortran): Polychord  [2, 27]  and MultiNest  [3, 28, 29]. The comparison is conducted on two examples: the eggbox presented in Section 4. 2. 2 in two dimensions, and the Gaussian presented in Section 4. 1. 1 in two, four, eight, sixteen and thirty-two dimensions. For nested_fit, we chose to use slice sampling with w = 1 and 5 bases, that is, the number of steps performed is five times the number of dimensions. The stopping criterion is the one from Equation (8) with log(δ 0 ) = 10 -5. For Polychord, the number of steps was set to five times the number of dimensions. For MultiNest, we did not use the importance nested sampling option.
For both Polychord and Multinest, we set the tolerance to 10 -5 , which is equivalent to the stopping criterion in nested_fit. (In Polychord and Multinest, the stopping criterion used is (I est,tot -I est,m )/I est,m < tol where tol is the tolerance. In Polychord, the remaining part of the evidence is estimated with the mean value of the likelihood while it is estimated with the maximum value of the likelihood in nested_fit. ) We used 1000 live points in all programs. Clustering was used for the eggbox (KNN in the case of nested_fit, the default one for Polychord and Multinest). Eight runs were performed for each program and example to evaluate the evidence uncertainty. Polychord and Multinest were used in pure-Fortran mode. For all three programs, the number of iterations performed were roughly the same.
In Table  5 , we see that nested_fit is able to recover the true value of the integral while Polychord and Multinest slightly overestimate it. The values of the uncertaintiescorresponding to the standard deviation of the eight runs-vary little between the programs. Moreover, MultiNest requires much fewer likelihood calls than the two other programs, and Polychord requires slightly fewer likelihood calls than nested_fit. For the Gaussian functions, in Figure  14  (left), we see that, while Polychord and nested_fit are able to recover the value of the integral for all dimensions, MultiNest fails in 32 dimensions. In Figure  14  (right), we notice that, again, nested_fit requires slightly more likelihood calls than Polychord-around 1. 5 times as many. MultiNest is the program needing the fewest likelihood calls; however, we observe a bias in the values found.
Acknowledgments: Work realised with the support of the  Sorbonne Center for Artificial Intelligence-Sorbonne University -IDEX SUPER  11-IDEX-0004. We thank  Julien Salomon  for reviewing carefully the manuscript and for his valuable suggestions. L. M. thanks  Philippe Depondt  for discussion on parallelisation and Fortran and  Luis Gonzalez-Miret Zaragoza  for discussion on nested_fit. We thank the reviewers for their comments.
, 2019). We then evaluate ELMv1-CNP-simulated N and P pools and fluxes with an observation-based dataset. Realizing that the static benchmarking may not help constrain future model projections, we further evaluate ELMv1-CNP using experimental manipulations of atmospheric CO 2. Finally, we take advantage of the P-enabled capability in ELMv1-CNP to quantify the effect of P dynamics on the simulated ecosystem responses to increasing atmospheric CO 2 , increasing N deposition, LULCC, and climate change on the global scale.
The excess carbon flux, which cannot be allocated due to nutrient limitation, is directed to the nonstructural plant carbon (NSC) pool instead of downregulating GPP. This pool respires to the atmosphere with a given turnover time. Details about the representation of NSC can be found in the supporting information  (Text S1)  The model version used in this study is the publicly released ELMv1 and can be downloaded along with all the parameter files at https://github. com/E3SM-Project/E3SM, last access: 12 September 2019. In this version of the model, the fire module is activated by default. The soil erosion module is not activated. We assume soil C, N, and P cycling can take place up to a 3. 8 m depth as the assumption in CLM4. 5  (Koven et al. , 2013). We also provide the key model parameters in Table  S1  (PFT specific) and Table  S2  (soil order specific).
The observational datasets used for the evaluation of carbon cycle in ILAMB are listed in Table  S4. In order to understand how the implementation of P cycling dynamics affects model performance, we evaluated the performance of both ELMv1-CNP and ELMv1-CN. In order to provide a context in terms of model performance in ILAMB, we provide the ILAMB evaluation of several other land models included in the Land Surface, Snow and Soil Moisture Model Intercomparison Project (LS3MIP) as part of CMIP6 (https://www. wcrp-climate. org/wgcm-cmip/ wgcm-cmip6, last access: 16 July 2020). LS3MIP includes a collection of model experiments including both offline land model experiments and coupled experiments  (van den Hurk et al. , 2016). We used the results from the offline land model experiments. Like our simulations, these experiments were performed at 0.
Evaluations of ELMv1 using ILAMB ILAMB includes many metrics that cover water, energy, and carbon pools and fluxes on both regional and global scales. Figure  1  shows ILAMB benchmarking scores for ELMv1-CNP and ELMv1-CN, along with several other land models in CMIP6, which are provided to contextualize ILAMB scores for ELMv1-CNP. The relative model performance scores are shown in Fig. 1 , indicating which model version performs better with respect to others. The full results produced by the ILAMB package can be found at https://compy-dtn. pnl. gov/yang954/_build/, last access: 24 July 2020. Figure  1  shows that the performance of ELMv1-CNP is comparable to other land models in CMIP6. ELMv1-CNP exhibits performance similar to CLM5 (CESM2) in terms of aggregated scores for carbon cycle metrics, while CLM5 shows better performance with respect to overall functional relationships, mainly due to a better score for the functional relationship of burned area.