dataesr/scientific-paragraphs-categorization huggingface dataset:
APPLICATION ON REAL DATASETS To evaluate the performance of our method on real data, we performed GWAS analysis on datasets made available by  (WTCCC, 2007). The WTCCC data collection contains 17000 genotypes, composed of 3000 shared controls and 14000 cases representing 7 common diseases of major public health concern: inflammatory bowel disease (IBD), bipolar disorder (BD), coronary artery disease (CAD), hypertension (HT), rheumatoid arthritis (RA), and Type I (T1D) and Type II (T2D) diabetes. Individuals were genotyped with the Affymetrix GeneChip 500K Mapping Array Set and are represented by about 500,000 SNPs (before the application of quality control filters). In parallel to the analysis of the WTTCC data, we decided to assess our method on another dataset from a different study. The ankylosing spondylitis (AS) dataset consists of the French subset of the large study of the International Genetics of Ankylosing Spondylitis (IGAS) study (International Genetics of Ankylosing Spondylitis Consortium  (IGAS) et al.
, 2013). For this subset, unrelated cases were recruited through the Rheumatology clinic of Ambroise Paré Hospital (Boulogne-Billancourt, France) or through the national self-help patients' association: "Association Francaise des Spondylarthritiques". Population-matched unrelated controls were obtained from the "Centre d'Etude du Polymorphisme Humain", or were recruited as healthy spouses of cases. The dataset contains 408 cases and 358 controls, and each individual was genotyped for 116,513 SNPs with Immunochip technology.
9/15 To remove the bias induced by population stratification in Genome-Wide analysis, we added the first 5 genomic principal components into the regression model as described in  (Price et al. , 2006). Since the methods evaluated here do not deal with missing values, we chose to impute the missing genotypes with the most frequent genotypic value, h j observed for each j SNP. For each each dataset, we filtered the values to keep only those SNPs having a MAF greater than 5%. The minor allele frequencies of each datasets are represented in Figure  4. We applied our cut level algorithm to find relevant clusters of SNPs and we performed single marker analysis on single SNPs (SMA) and on groups of SNPs (SASA, SKATtree, SKATnotree). We then compared the significant associations detected by the different methods to reveal possible new associations with the phenotype.
AUC-ROC curves In this section, we compare the AUC-ROC curves generated by our cut level algorithm for each disease (WTCCC and AS data). Concerning the WTCCC diseases, given that patients were all genotyped using the same GeneChip, their genotypes have same LD structure, and therefore the shapes of the AUC-ROC curves should be very similar between the different diseases. As can be observed in Figure  5  (WTCCC diseases), the shape of the AUC-ROC curves are similar, with a chosen cut level located around 100 000 clusters of SNPs, suggesting a shared LD pattern among patients. q q q q q q q q q q q q q q q q q qq qqq q q q q q q q q q q q q q q qqqq q q q q q q qqqqq q q q q q q q q q q q q q q q q q q q 0. 5 In contrast, the AUC-ROC from the AS data (Figure  6 ) behaves differently from the WTCCC data.
Predictive power is substantially improved if aggregated-SNP predictors are used at a fairly high level in the hierarchical tree (7478 optimal clusters identified by the cut level algorithm). It is relevant to note that the pattern we observe on this real dataset is similar to the pattern we observed in the numerical simulations, especially under the clusSNP scenario. q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q 0. 50 As we remarked concerning the WTCCC results, the algorithm identifies a relatively high number of clusters in relation to AS and simulated data. This difference is certainly due to the LD level among the genetic markers in the Affymetrix GeneChip. The correlation levels among SNPs for a given bandwith are similar between the simulated and the AS data, but greater than for the WTCCC data (Table  1  and Figure   7 ).
In this work we propose a four-step method explicitly designed to utilize the linkage disequilibrium in GWAS data. Our method combines, on the one hand, unsupervised learning methods that cluster correlated-SNPs, and on the other hand, supervised learning techniques that identify the optimal number of clusters and reduce the dimension of the predictor matrix. We evaluated the method on numerical simulations and real datasets and compared the results with standard single-marker analysis and group-based approaches (SKATtree and SKATnotree). We remarked that the combination of our aggregating function with a ridge regression model leads to a major improvement in terms of predictive power when the linkage disequilibrium structure is strong enough, hence suggesting the existence of multivariate effects due to the combination of several SNPs.
These results remained consistent across a wide range of real datasets (WTCCC and ankylosing spondylitis datasets). In terms of the identification of associated loci in different simulation scenarios, our method demonstrates its ability to retrieve true causal SNPs and/or clusters of SNPs with substantially higher precision coupled with a good power. On real GWAS data, our method has been able to recover a genomic region associated with ankylosing spondylitis (HLA region on chromosome 6) with a higher precision than standard single-marker analysis. To improve our method further, while taking into account structured input variables in GWAS, there are different avenues that may be explored. One avenue would involve highlighting potential non-linear relationships between aggregated-SNPs and a response phenotype.
Most differences observed between the two networks can be explained, either by the fact that in graphical representations some details are omitted for the sake of clarity, or by the presence of knowledge in the reference network transferred from other networks, mostly from signaling networks triggered by other GPCRs. This tends to show that our expert rules are complete enough to deduce all the main processes and modulations of a given network. We conducted a similar work on the signaling network of the EGFR (Epidermal Growth Factor Receptor). In that case, our reference was the network of the Pathway Interaction Database (PID)  24. Similar to the FSHR case, we extracted the experimental facts from the literature cited in the PID network, and ran the inference. Comparison of the inferred and reference networks showed no significant differences.
Scalability and automation. One weakness of our framework that clearly appeared during this work is the manual entry of experimental and background facts. Going through scientific papers to extract experimental results and formating them for the inference engine is a tedious and error-prone task. Consequently, scalability of the method, apart from pure computational aspects, will also need the automation of this extraction and formating task. This led us to start the development of a method, using natural language processing, to automate this task. We have already shown that we can isolate, within a scientific paper, the sentences relative to the results of an experiment  33. We are now developing cascades within the CasSys system  34  to format these experimental results as facts that could be used directly within the framework described in the present paper.
The first tests have shown that we were able to extract and format successfully both background and experimental facts with good precision (76%) and recall (88%). This work is still preliminary, and will require many more developments. However, we already have a proof of concept that such automated extraction and formatting of background and experimental facts from literature is feasible using natural language processing. Another question is how scalable is the method? Giving a definitive answer to this question would require a very large dataset of experimental facts, which will be available only after completion of the natural language treatment-based extraction and formatting method. However, the computation times observed during our first tests seem to depend linearly on the number of experimental facts (Table  2 ).
Methods Retrieving the experimental facts. Experimental facts relative to both networks have been manually retrieved from the literature. The corpus of scientific papers considered for FSHR signaling was the exact same one used to build a comprehensive literature network  4  , so that the inferred network and the literature network could be compared. This corpus was initially assembled by reviewing the literature. As for the corpus of scientific papers considered for EGFR signaling, it has been found in the PID  24  (data now available through the NDEx database  40 ). Deduction with answer set programming. Deduction was performed by means of answer set programming (ASP) using the clingo software  41. Our expert rules were straightforwardly translated into ASP rules. The ASP program consists of a set of background knowledge and experimental facts, and the translated expert rules.
This program has a unique answer set which is exactly the set of facts that can be deduced from the initial facts and the rules. Traces can be obtained at the same time as sets of deduced facts by using a simple transformation of the ASP rules (see Traces in ASP in Supplementary Note for more details). Computations were made on an Intel(R) Xeon(R) CPU E5-1620v4 @ 3. 50 GHz. Inference of experimental plans with SOLAR. Inference of experimental plans was performed with the software SOLAR  42  , using abductive reasoning. As for deduction with ASP, the expert rules were straightforwardly translated into SOLAR's input language. Experimental plans for a given hypothesis were then computed using the negation of the hypothesis as the top clause.
The anti-phospho (T202/Y204, diluted 1:2,000) and total (diluted 1:6,000) ERK 1/2 antibodies were from Cell Signaling Technology (Beverly, MA) and Santa Cruz (Dallas, TX), respectively. Rabbit anti-phospho-MEK 1/2 (S217/S221, diluted 1:1,000) and mouse anti-total MEK 1/2 (diluted 1:2,000) antibodies were from Cell Signaling Technology (Beverly, MA). Goat anti-rabbit IRDye ® 680 antibody (Licor ® , Lincoln, NE) was used for detection with an Odyssey ® CLx scanner (Licor ® ) and signals were quantified using Image Studio software (Licor ® ). Normalization was applied as ratio of phospho ERK/total ERK signals. Data were analyzed using the GraphPad PRISM software and differences between two groups were assessed by a two-tailed paired t-test. The cut-off for significance is either 0. 05 (⋆) or 0.
The "Type" column indicates whether the conjectured model of the curve is hyperelliptic (H), Picard (P), or a plane quartic with trivial automorphism group (G). The "Curve" column gives a reference to the conjectured model over Q of the curve. The cases 1, 2, 3, 5,. , 20 correspond to the smooth plane quartics X i in Section 5. In the hyperelliptic cases, curves can be reconstructed by applying the Sage-Math  [58]  code of Balakrishnan, Ionica, Lauter and Vincent  [2]  (based on  [63, 3] ) and Magma  [5]  functionality due to Lercier and Ritzenthaler for hyperelliptic reconstruction in genus 3  [37]. Some of these curves were already computed by Weng  [63]. The final cases 4, 25, 26 were found by Balakrishnan, Ionica, Kılıçer, Lauter, Somoza, Streng and Vincent and will appear online soon.
Note that I 27 is the discriminant of X C , which is non-zero. For a ternary quartic form over Q that is equivalent to a ternary quartic form over Q, the tuple I defines a Q-rational point in P d. This is not to say that the entries of I itself are in Q. However, we can achieve this by suitably normalizing this tuple. When I 3 = 0 (as will always be the case for us), we can for instance use the normalization (2. 31) I norm = 1, I 6 I 2 Our program concludes by computing the best rational approximation of (the real part) of the Dixmier-Ohno invariants I norm by using the corresponding (Pari  [4] ) function BestApproximation in Magma at increasing precision until the sequence stabilizes. In practice, this does not take an overly long time: we worked with less than 1000 decimal digits and the denominators involved never exceeded 100 decimal digits.
It was the aim of the EPAA Partners' Forum to provide a comprehensive picture of relevant ongoing activities and to progress the development of predictive toxicokinetics methods aligned with the 3Rs principle and the use of data from such methods within read-across approaches. Discussions revolved around sessions covering the spectrum of stakeholders working on toxicokinetics and read-across: regulators, the regulated from all relevant industry sectors, method developers and downstream users. The proceedings of the EPAA Partners' Forum are presented in this report that serves (i) to provide a comprehensive overview of the discussions; (ii) to build a consensus on the role toxicokinetic data play and should ideally play in read-across; and (iii) to showcase the actions defined during the Forum as the way forward to find synergies for 3Rs on toxicokinetics and read-across.
Cosmetics sector input Rob Taalman (Cosmetics Europe, Belgium) presented the Safety Assessment Framework developed within the research initiative Safety Evaluation Ultimately Replacing Animal Testing (SEURAT-1; http://www. seurat-1. eu/; Gocht et al. , 2015; Berggren et al. , 2017). This framework serves to ensure best possible use of in vitro and in silico data. During Tier 0, all available data are collected and product use scenarios identified. The available data are evaluated to determine applicable thresholds of toxicological concern (TTC). If exposure to the substance under investigation is below the TTC value, the risk can be considered sufficiently low and further toxicological testing is not deemed necessary. Further, the available data are evaluated to identify analogues and opportunities to apply read-across to substantiate the conclusion  (Berggren et al.
With respect to toxicokinetics, testing is likely to begin with dermal penetration studies and the evaluation of substance clearance. Tier 2 testing can be complex if the substance is metabolised and considerable amounts of one or more specific metabolites evolve. In the cosmetics sector, ongoing research projects related to toxicokinetics focus on skin penetration and, secondarily, the kinetics in the respiratory tract as a further potential portal of entry  (Gerstel et al. , 2016; Jaques-Jamin et al. , 2017; Rothe et al. , 2017a). In an ongoing research project assessing skin bioavailability conducted in the context of the Cosmetics Europe LRSS, all available data for this endpoint have been collected and used to assess available in silico models. Two in silico skin penetration models were selected as sufficiently advanced to merit further development.
Such in vitro dosimetry information is pivotal for the interpretation of in vitro effects, i. e. to correlate in vitro concentrations to in vivo concentrations, and to undertake IVIVE  (Wambaugh et al. , 2018). Finally, a multi-organ-chip (skin and liver) model project undertaken by Cosmetics Europe aims at developing static and dynamic 3D skin and liver models to measure the effect of single versus repeated application on substance metabolism and route-to-route differences. While the testing of cosmetic ingredients is focused on dermal effects and dermal penetration, an understanding of effects on the liver is relevant for substances that become systemically available. Together, these projects aim at providing a combination of in silico and in vitro data that will help understand the bioavailability and metabolism of topically applied compounds.
org/products/software/times. aspx -Toxic Hazard Estimation by decision tree approach (ToxTree); http://toxtree. sourceforge. net testing of agrochemicals, a toxicokinetics guidance for this sector is being compiled within the Crop Life International project on exposuredriven human health assessment. A case study was performed to investigate how toxicokinetic data can contribute to the hazard identification, exposure assessment and risk characterisation of agrochemicals (internal report; Dow AgroSciences). The novel herbicide halauxifen-methyl that is rapidly hydrolysed to halauxifen-acid was selected for this case study, and both compounds were assessed in 7-, 28-, and 90-day rat oral toxicity studies. After 7 days of exposure, the rats treated with halauxifen-methyl exhibited increased liver weight and liver hypertrophy, whereas no findings were recorded in the animals exposed to halauxifen-acid.
Upon 28-and 90-day treatment with halauxifen-methyl, the liver was identified as target organ, and a 90-day NOAEL of 10 mg/kg body weight/ day was calculated for both male and female rats. By contrast, upon 28and 90-day treatment with halauxifen-acid, the kidneys were identified as target organ, and a much higher 90-day NOAEL of 250 mg/kg body weight/day was calculated for both male and female rats. Toxicokinetics analyses showed that the liver is exposed to halauxifenmethyl, while post-hepatic systemic exposure was only recorded for halauxifen-acid. Therefore, halauxifen-acid was selected for the subsequent long-term studies, e. g. , multi-generation reproduction toxicity or carcinogenicity tests. Within the case study, toxicokinetic data helped understand the threshold for the MoA for liver toxicity, and further allowed determination of human health reference values, e.
; cf. ECETOC, 2012). Further, only a limited number of the available read-across tools provides easily accessible data on substance identity and composition together with chemical structures and high-quality endpoint data. AMBIT serves as a hub to interconnect other tools and databases applying a hierarchical structure that is meaningful for hazard and risk assessment. AMBIT helps to implement workflows for read-across assessments by supporting the assessor in setting up a readacross (category) approach and in establishing a valid justification for the approach taken. This serves to ensure that all available data are used efficiently, including comparison of data between substances, thereby minimising overall animal testing and resource costs. Large sets of high-quality data are being processed to continuously enhance the predictive power of AMBIT.
Such data are extracted from company-owned data stored in instances of the International Uniform Chemical Information Database (IUCLID), from disseminated non-confidential business information data in 14,570 dossiers of the ECHA IUCLID database, and from other reliable sources. This work has resulted in a re-launch of AMBIT2 that provides new functionalities, stand-alone and web-based versions and an open source Application Programming Interface. AMBIT2 is compatible with the 6th version of IUCLID (https:// iuclid6. echa. europa. eu), and it includes (i) the EFSA OpenFoodTox database (cf. Section 2. 4. 2 and https://www. efsa. europa. eu/en/data/ chemical-hazards-data); (ii) the VEGA platform (courtesy E. Benfenati, Mario Negri Institute, Milano, Italy) that allows accessing a series of QSAR models; (iii) new additions to ToxTree (cf.
Section 2. 3. 3); (iv) the Cramer classification scheme to define a TTC  (Cramer et al. , 1978) ; and (v) specific modules addressing protein binding properties. Work is under way to further improve the platform, i. e. to develop AMBIT3. Dr. Hubesch concluded that, while AMBIT is mainly applicable for lower tier assessments, the predictions made using AMBIT are reliable so that higher tier testing needs are reduced. Finally, CEFIC LRI is continuously engaged in dissemination activities to make both the RVis and AMBIT known and readily accessible to toxicologists and risk assessors  (Maertens et al. , 2016). Future research activities on toxicology in Horizon (2020) will most likely address the topics of human hazard and risk assessment, endocrine disruption, and the human exposome. The exposome concept refers to the totality of environmental exposures from conception onwards.
It is a novel approach to studying the role of the environment in human disease. Using 'omics technologies, the collected exposure data will be linked to biochemical and molecular changes in humans; cf. http://www. exposomicsproject. eu. One of the key topics pursued within IMI-2 (funded in equal shares by the EC and the pharmaceutical industry) is 'electronic translational safety', that encompasses the development of an internationally accepted guideline for data sharing, the collation of large sets of preclinical and high-level clinical data, and a retrospective analysis of these data to assess translation of preclinical to clinical study outcomes (cf. http://ec. europa. eu/research/participants/ portal/desktop/en/opportunities/h2020/topics/imi2-2016-09-04. html). Additionally, future research activities to be funded by the EC will likely be related, e.
If estimates do not converge, further modelling can be necessary to improve the results. 2. For pre-market authorisation of regulated substances (agrochemicals, food additives etc. ), the reference point is often derived from in vivo data in test species as defined in the respective regulatory requirements. 3. In a data-rich situation (e. g. , re-evaluation of a contaminant or a regulated substance), the derivation of a reference point may use a full MoA analysis including PBK modelling between test species and humans, in vivo animal data and even epidemiological data (EFSA Scientific Committee, 2017). Since its creation in 2002, EFSA scientific panels and staff have produced risk assessments for more than 4400 substances in over 1650 scientific opinions, statements and conclusions. In 2017, the first version of OpenFoodTox has been published as a structured database summarising the outcome of hazard characterisation for human health and, depending on the relevant legislation and intended uses, animal health and the environment  (Dorne et al.
, 2017). For each individual substance, the OpenFoodTox data model has been designed on the basis of the OECD Harmonised Template to collect and structure the data in a harmonised manner. OpenFoodTox contains information on the identity and use of the substance, EFSA's opinion on the specific substance or groups of substances, critical toxicity studies, and health-based guidance values (such as acceptable and tolerable daily intakes). Thereby, OpenFoodTox encompasses 10,000 toxicological endpoint studies and 12,000 risk assessment summaries. OpenFoodTox can be searched under the following link using a microstrategy tool: https://dwh. efsa. europa. eu/bi/asp/Main. aspx?rwtrep=400, or it can be downloaded under EFSA's knowledge junction: https://zenodo. org/record/ 344883#. WUDqK_mGPIU which also contains most scientific models used by EFSA in the area of human health, animal health and environmental hazard assessment of chemicals and biologicals.
The data contained in OpenFoodTox have also been used as training and test sets to develop QSAR models, e. g. , to predict sub-chronic toxicity in rats (continuous NOAEL model) and lethal concentrations (LC 50 ) in rainbow trout  (Toropov et al. , 2017; Toropova et al. , 2018). These models are available as web applications on the VEGA hub (https://www. vegahub. eu). Further, EFSA has reviewed the applicability of available methods and tools for toxicokinetics assessments as well as 'omics technologies and in silico tools to investigate toxicity and toxicodynamics during human hazard assessment of chemicals  (EFSA, 2014a). This review yielded a number of conclusions including the need to develop generic toxicokinetics models and tools to support food safety assessments. Since, EFSA has engaged in a multi-agency-academia collaboration to develop such generic toxicokinetics models and tools as user-friendly, open-source models, coded in R (R Core Team, 2013).