dataesr/scientific-paragraphs-categorization huggingface dataset:
More precisely, this Construct query augments the initial RDF graph with new triples leveraging the OntoVIP ontology. They state (i) the nature (type) and the location (is-stored-in-file) of the input parameters and output data, (ii) the nature and relations of the produced medical images with respect to the input organ model and the simulation workflow (derives-from-model, is-a-result-of-at, etc. ), and (iii) the nature and global parameters of the simulation workflow (uses-as-model-in-simulation, etc. ). Being concise and domain-specific, these statements semantically annotate the produced raw data, and helps e-scientists interpret data produced along their experimental campaigns and link the simulation parameters and components to the produced data. • Lines 13 to 27 represent the new statements resulting from the application of this rule.
They only involve domain-specific entities (vip-model, vipsimulation and vip-simulated-data prefixes of the OntoVIP ontology) whereas the Where clause of the query only involves OPM-O entities. These new statements semantically describe the nature of input parameters (medical-image-simulationobject-model, simulation-parameter-set) and simulated output medical images (PET-sinogram). In addition, these new annotations represent the nature of the simulation (i. e. Positron Emission Tomography) through the use of the PET-simulation class, and the relation between the medical object imaged and the produced simulated image (derives-from-model property). They are particularly useful because they involve medical imaging concepts and relations forming part of the OntoVIP ontology. • Lines 29 to 33 identify a process execution, its corresponding service description through an ?agent instance, and the achieved class of action through the iec:refers-to property.
Experimental setup The VIP simulation platform hosts a semantic catalog of organ models which associates the set of raw source files with the set of semantic annotations describing each model. It leverages the OntoVIP medical image simulation ontology and enables advanced querying on available models. VIP consumes organ models through the MOTEUR data-intensive workflow manager  [27]  coupled to the European Grid distributed computing Infrastructure (EGI  12  ) to produce simulated data. It keeps records of all running and completed simulations, enabling simulated data search, post-analysis and reuse. More details on the VIP platform are available in  [6]. Through the work presented in this paper, simulated data entities are annotated with OntoVIP-based semantic information linking them to (i) an input organ model, (ii) an input parameter sets, and (iii) a brief description of the overall simulation workflow.
Semantic experiment summaries for scalable e-Science data annotation New statements resulting from production rules provide high-level and concise "semantic experiment summaries". We consider experiment summaries as highlevel descriptions since they only involve domainspecific classes and properties defined in the OntoVIP ontology, compared to the generic and technical entities provided by the OPM provenance ontology. We also consider them concise since for Sorteo, only 12 statements might be produced, compared to thousands of OPM statements produced through our provenanceinstrumented workflow engine. Figure  3  illustrates the experiment summary resulting from the execution of the Sorteo medical imaging workflow. Green ellipses represent input or output data, the blue ellipse represents the Sorteo workflow shown 13 Corese/KGRAM: http://wimmics.
Another quantitative query shows that the "organes. pegs4dat" organ model has been used in all the 31 CT simulations, which represents 22% of the overall simulations. It shows that this organ model has been intensively used in the VIP platform to perform CT simulations. Performance. In terms of performance, both the capture of raw provenance and the production of experiment summaries is negligible compared to the processing time of raw data on a dedicated computing infrastructure. Over 233 simulation workflow runs, we measured a mean summary production time of 2 seconds with a standard deviation of 1. 1 seconds. The mean ratio of summary production time over workflow execution time is 0. 76%. We pay special attention to scalable data production through (i) the materialization of few produced statements into a long-term simulated data catalog, and (ii) short-term fine-grained OPM provenance datasets stored independently and available for workflow design and debug concerns.
Semantic experiment summaries exploitation 6. 3. 1. Linked Data querying Based on provenance information, our approach automates the annotation of e-Science workflow results with domain-specific concepts, finally assembled, following Link Data  [1]  principles, into semantic experiment summaries. These summaries can be combined with external data sources such as FMA  [14] , the Foundational Model of Anatomy. We exemplify in Listing 5 a SPARQL query leveraging three kinds of interlinked data, VIP simulated data, VIP organ models, and FMA anatomical concepts. This query aims at retrieving simulated data from VIP organ models which contain brain white matter, as defined in the FMA ontology. PREFIX mo: <http://vip. cosinus. anr. fr/vip-model. owl#> PREFIX partic: <http://www. irisa. fr/visages/team/farooq/ontologies/ particular-owl-lite.
owl#> PREFIX iec: <http://www. irisa. fr/visages/team/farooq/ontologies/iec-owllite. owl#> PREFIX fma: <http://sig. uw. edu/fma#> SELECT ?dataFile ?dataClass ?anat WHERE { ?organModel rdf:type mo:medical-image-simulation-object-model. ?organModel partic:has-for-proper-part-during ?x. ?x rdf:type mo:anatomical-object-layer. ?x partic:has-for-proper-part-during ?y. ?y rdf:type mo:anatomical-object-layer-part. ?y iec:refers-to ?anat. ?anat rdf:type fma:White matter of neuraxis. ?dataArtifact mo:derives-from-model ?model. ?dataArtifact mo:is-stored-in-file ?dataFile. ?dataArtifact rdf:type ?dataClass. FILTER (CONTAINS(?organModel,?model)) } Listing 5: SPARQL query exploiting both the VIP organ model catalog and the newly populated simulated data catalog and FMA concepts The first six triple patterns appearing in the WHERE clause aim at searching for VIP organ models (medicalimage-simulation-object-model), and their anatomical constituents (anatomical-object-layer and anatomicalobject-layer-part).
Reference anatomical concepts are retrieved through the refers-to property and the ?anat variable. Then, the next triple pattern specifies the FMA anatomical concept to be matched: brain white matter (White mater of neuraxis). Finally, the last three triple patterns aim at retrieving, from the simulated data catalog, data files (is-stored-in-file) and their associated medical image modality simulated from organ models (derives-from-model) including white matter.
In addition, PROV-O extends OPM-O with some classes and properties especially useful in the context of e-Science workflows. For instance, PROV-O introduces the notion of Plan to describe the context of execution of an Activity, which can be seen as a set of instructions, as a recipe, or a workflow. Another interesting extension is the alternateOf property aiming at representing several aspects of the same thing. For instance, in medical imaging, it would be well adapted to link several datasets resulting from data conversion tools. 14 http://www. w3. org/TR/prov-primer 15 see the example of Listing 2 Madougou and coworkers propose in  [35]  a provenance-based approach aimed at analyzing the e-BioInfra e-Science platform usage and identifying the causes of application failures. From a post mortem analysis of the MOTEUR workflow enactor logs, the proposed system populates a relational SQL backend with OPM provenance statements, queried through HQL (Hibernate Query Language).
The natural graph representation of provenance is buried into a relational representation, and the system cannot benefit from graphbased querying languages such as SPARQL. The system addresses the technical characterization of workflow executions through a statistical analysis of fine-grained domain-agnostic provenance. We rather focus on result interpretation from the e-scientist perspective by leveraging domain-specific ontologies and preserving the underlying graph structure of provenance, thus allowing for graph-based querying and reasoning through Semantic Web technologies. The Wings/Pegasus environment  [36]  addresses through semantic reasoning on application-level constraints, the generation of valid and executionindependant workflows, to be enacted over distributed computing infrastructures.
The system proposed is able to produce both application-level and execution provenance. Wings/Pegasus uses a proper OWL ontology to model application-level provenance data and uses a provenance tracking catalog, based on a relational database, to record execution provenance. Two languages are thus required to query provenance data, SPARQL for design-time application-level (and thus domain-specific) provenance, and SQL for run-time domain-agnostic execution provenance. Rather than using two representations for executionlevel and application-level provenance, we rely on RDF, for a graph-based representation of these two levels. Wings/Pegasus also attaches domain knowledge to workflow templates which is an interesting perspective to reduce the design complexity of our production rules. In Janus,  [37]  introduce semantic provenance as technical provenance graphs coupled with domain knowledge.
To answer provenance queries, a specific transitive closure implementation was proposed based on low-cost SPARQL Ask queries. Janus is definitely the closest approach to our proposal for generating e-Science experiment summaries. The main differences are the use of OPM-O and the medical imaging ontologies OntoVIP grounded to DOLCE in our work, compared to Provenir and biomedical ontologies in Janus. To address scalability issues, we propose to make a clear distinction between short-term fine-grained domain-agnostic provenance and produced long-term domain-specific provenance through semantic experiment summaries. Janus extends domain-agnostic provenance with domain specific statements, which requires to manage in a single dataset the large amount of fine-grained provenance. Also addressing the exploitation of e-Science workflows from an end-user perspective, Alper and coworkers analyze in  [40]  why raw provenance traces are difficult to exploit and share in the context of data publication.
It would also bring valuable inputs on how e-scientists search for their simulated data, and if the proposed approach foster sharing of simulation data/models. Currently, these summaries are used to populate the simulated data catalog exposed to endusers through the VIP web portal. Platform logs show that for the last 6 months (December 2013 -April 2014), 137 experiment summaries were produced and the simulated data catalog has been viewed 68 times. Sharing of experiment summaries. To tackle the interpretation of possibly massive data production in the context of e-Science workflows, we automate the generation of semantic experiment summaries. We produce these summaries from OPM-O provenance datasets. These experiment summaries represent new concise domain-specific statements in the sense that we associate the produced data to concepts and relation of the OntoVIP domain ontology.
Acknowledgments This work is funded by the  French National Research Agency (ANR)  under grant  ANR-09-COSI-03  and the  CNRS interdisciplinary mission MASTODONS  under program CrEDIBLE. We thank the  European Grid Initiative  and " France-Grilles ". We would also like to thank  Olivier Corby  and  Catherine Faron Zucker  for their support and advises regarding the Corese/KGRAM Semantic Web engine.
microRNAs (miRNAs) are a class of ∼22nt noncoding RNAs that potentially regulate over 60% of human protein-coding genes. miRNA activity is highly specific, differing between cell types, developmental stages and environmental conditions, so the identification of active miRNAs in a given sample is of great interest. Here we present a novel computational approach for analyzing both mRNA sequence and gene expression data, called MixMir. Our method corrects for 3' UTR background sequence similarity between transcripts, which is known to correlate with mRNA transcript abundance. We demonstrate that after accounting for kmer sequence similarities in 3' UTRs, a statistical linear model based on motif presence/absence can effectively discover active miRNAs in a sample. MixMir utilizes fast software implementations for solving mixed linear models, which are widely used in genome-wide association studies (GWASs).
Most computational target prediction methods make use of exact Watson-Crick pairing of the seed region, as well as other features such as evolutionary conservation  (10, 11) , co-occurrence of miRNA-binding sites  (12)  and RNA-binding protein binding sites  (13) , messenger RNA (mRNA), miRNA, and Argonaute expression levels (5-7,9,13), 3' UTR sequence composition and other mRNA sequence features  (5, 6, 12, 13) , and protein interaction data  (14). A commonly-used computer program for analyzing miRNAs from both gene expression and sequence data is miReduce  (7) , which is based on the REDUCE algorithm for predicting transcription factor motifs  (15). Two other published programs, Sylamer  (5)  and cWords  (6) , also solve the same problem while explicitly correcting for background sequence composition.
17 PAGE 2 OF 13 methods from the genome-wide association study (GWAS) field for efficiently solving large systems of MLM equations. Our method uses the MLM to correct for similarities between 3' UTR sequences, analogous to the way MLMs are used to correct for cryptic relatedness between individuals in GWASs, where such artifacts can lead to high falsepositive associations  (16) (17) (18). We demonstrate the utility of MixMir on a gene expression dataset in mouse wild-type (WT) and Dicer-knockout (KO) CD4+25-T-cells (hereafter called T conv cells). We found that MixMir performed better than miReduce, Sylamer and cWords at finding miRNAs annotated in the miRBase database  (19)  and highly expressed miRNAs in this cell type. We confirmed our results on two other mouse Dicer-KO experiments from embryonic stem (ES) cells and adrenal gland, and miRNA transfection experiments in human cell lines, both for quantitative proteomics data and microarray data  (20).
Experimental methods for obtaining mRNA and miRNA expression data for Dicer-KO T conv cells Mice carrying a floxed Dicer allele in combination with CD4Cre transgene on a mixed C57BL/129 background  (21)  were maintained under specific pathogen-free conditions. Peripheral CD4+CD25-T cells were sorted on a FACS ARIA (Becton Dickinson) from 6-8-week-old mice and RNA extracted using RNAbee (AMSBio) according to the manufacturer's instructions. Hundred nanograms of RNA was used to interrogate the GeneChip Mouse Gene 1. 0 ST Array (Affymetrix). We obtained log fold changes in gene expression for 24 601 mRNA transcripts between WT and Dicer-KO mouse CD4+ CD25-T cells. We obtained two datasets of miRNA expression for CD4+CD25-T cells from independent sources using different technologies. First, from the same cells from which we obtained our mRNA microarray expression data, we also obtained comparative miRNA expression data between CD4+CD25+ T-cells and CD4+25-T -cells from Cobb et al.
(22) , who studied the differences in miRNA expression profiles for the two types of cells. The authors performed an miRNA microarray analysis with probes for 173 miRNAs from miRBase. Of these, we take the top 20 differentially expressed to be true, 'active' miRNAs, as reported by Cobb et al. in Figure  2. To corroborate these results, we also used miRNA expression data from C57BL/6 mice determined by the nCounter miRNA expression assay kit (Nanostring Technologies), from Sommers et al. (23). The authors validated the Nanostring nCounter expression results with Exiqon microarrays and Taqman reverse transcription-quantitative-polymerase chain reaction (PCR) assays. Ninety two probes corresponding to 86 miRNAs in miRBase were evaluated. Of these, we took the top 21 highly expressed for experimental validation of our predictions, corresponding to the most highly expressed miRNAs presented by Sommers et al.
pSILAC and microarray data for miRNA transfection experiments In addition to our Dicer-KO data, we also tested MixMir on pSILAC and mRNA microarray data from miRNA trans-  fection experiments from Selbach et al. (20). The authors performed transfections by synthetic miRNAs and mock transfections in human HeLa cells for miRNAs let-7b, miR-1, miR-155, miR-16 and miR-30a. The amount of protein synthesis was given by the log of the ratio of protein synthesized in the miRNA-transfected cells divided by the mock transfection between 8 and 32 h post transfection. Microarray analyses were performed with the Affymetrix Human Genome U133 Plus 2. 0 chip. We mapped the pSILAC Uniprot protein IDs to Refseq transcript IDs by downloading an ID mapping table from the Uniprot website. For the different transfection experiments, there were slightly different numbers of proteins with expression values, resulting in a range of the number of protein expression data points with corresponding 3' UTR sequences from ∼3000 to 3600 across all the transfection experiments.
mRNA and miRNA expression data for Dicer-KO mouse adrenal cortex cells We also obtained mRNA and miRNA expression data for Dicer-KO adrenal cortex tissue from mouse embryos at stages E15. 5 and E16. 5 from a study by  Krill et al. (25). Sf1-Cre mice were crossed with mice carrying a floxed Dicer allele to produce Sf1-Cre/Dicer lox/lox mice. Embryos were harvested at E15. 5 and E16. 5 and the adrenals from each were collected. A total of four control and four Dicer-KO biological replicates were obtained for each time point. Affymetrix Mouse 430 v2. 0 gene expression arrays were used for hybridization. ABI miRNA OpenArray was used for miRNA expression analysis. These arrays are able to target 750 miRNAs. We took the top 25 highly expressed miRNAs for E15. 5, the top 35 highly expressed miRNAs for E16.
Processing of the 3' UTR and miRNA sequence data We downloaded all 26 845 mouse RefSeq gene 3' UTR sequences and 40 571 human 3' UTR sequences from the UCSC Genome Browser (version mm10 and hg19, respectively)  (26, 27). We removed all 3' UTRs of length <10 nt and retained the longest isoform if there were multiple 3'UTR isoforms. In total, we were able to associate 17 988 unique UTR sequences to their microarray expression values for the mouse Dicer-KO dataset, and 22 266 unique UTR sequences to their microarray expression values for each of the Selbach et al. miRNA transfection experiments. We downloaded 1908 mature mouse miRNA sequences corresponding to ∼1200 distinct 6mer seeds and 2578 mature human miRNA sequences corresponding to ∼1500 distinct 6mer seeds from the miRBase database (release 20)  (19).
We ran Sylamer via its web-based implementation, Sylarray  (5, 31) , because the Sylamer code did not collapse low complexity and redundant sequences, and preprocess 3' UTR transcript variants which are implemented in Sylarray and are important components of the overall Sylamer method. Given a gene list of N genes ranked in descending order of differential gene expression, the Sylamer algorithm computes over-and under-representation of motifs in the top T genes versus the remaining N -T in the list, as T is incremented in bin sizes of b. The hypergeometric distribution is used to determine the significance of the enrichment or depletion of a particular motif m in the top T genes, compared to the rest of the gene list, given that the total number of genes containing the motif in its 3' UTR is K m.
Sylamer corrects for background sequence composition by estimating expected motif counts based on the sequence composition of shorter motifs within each bin and using these values in place of K m  (5). We use the default parameters for Sylarray, except we select the 'all words' option, which searches the space of all possible 6-, 7-, and 8mer motifs instead of just those corresponding to known miRNAs. To obtain a full list of ranked motifs, we downloaded the enrichment table for each analysis and ranked motifs by their most sig-nificant P-value across all bins (see Sylamer manuscript for a full description of Sylamer defines statistical significance). cWords (  6 ) corrects for background sequence composition using a kth order Markov model. cWords estimates the expected probability of seeing a particular word based on the probability of seeing shorter words within the same UTR sequence, instead of looking through bins of motifs.
The probability that a motif is enriched is calculated using the binomial distribution and the negative log of the probabilities is plotted to show enrichment across all ranked genes (genes ranked by increasing differential expression). Background enrichment values are computed as a sum of all such log probabilities, called the 'running sum', and enriched motifs are those that have statistically higher sums than the expected maximum value. We downloaded cWords from https://github. com/simras/cWords and ran it with word length = 6 and order of Markov background nucleotide model in the range 2-6. The Markov model in cWords can correct for kmers of length up to the motif length being analyzed. Thus, as we are analyzing hexamers, we can use this model to correct for background kmers with k ≤ 6.
In principle, higher values of k should give more correction, but in practice the amount of data available for estimating the Markov model is limited by the length of the 3' UTR sequences (see Results, Supplementary Table  S2 ). Thus, we found that correcting for a background kmer of length k = 2 produced the best results, which is entirely consistent with the authors' recommendation to set k between 1 and 3. Both Sylamer and cWords return results separated into those motifs being either enriched or depleted. For both methods, we pooled the results to be more comparable with the other models, as we are looking for any significant motifs regardless of the direction of effect. We reserve the direction of the effect as an independent test of the accuracy of the methods later.
Comparison of different MixMir and cWords parameters on the T conv microarray data We started by testing five different settings of the kmer length in MixMir on the mouse Dicer-KO T conv microarray dataset (Methods). MixMir uses a linear model of miRNA targeting, similar to previous models such as miReduce, but adds an additional relationship matrix in a MLM framework to correct for background sequence composition of 3' UTRs (Methods). The kmer length determines the construction of the relationship matrix in MixMir (Methods). We tested values of k from 2 to 6 and refer to these models as MixMir2 to MixMir6. For values of k above 6, the estimate of the correlation matrix became inaccurate because of the limited total amount of 3' UTR sequence in the genome and the running time of the implementation was slow, so we did not consider higher values of k further (Discussion).
To verify this, we performed 20 randomizations of the sequence and expression data, and ran MixMir on the randomized data for k = 2. 6. P-values obtained from the randomized data exactly followed the diagonal line expected under the null hypothesis line, suggesting that our P-values are indeed properly estimated (Supplementary Figure  S1 ). We initially tested k = 2-6 for cWords to perform model selection as we did with MixMir, which we refer to as cWords2 to cWords6. The authors recommended setting k = 1-3 for cWords, presumably because of the limited amount of sequence in 3' UTRs (Methods). The resulting PP plots showed that there was a significant discrepancy between observed and expected P-values, similar to the simple linear models, suggesting a relatively high false-positive rate for cWords on this dataset, or perhaps that P-values are incorrectly estimated.