dataesr/scientific-paragraphs-categorization huggingface dataset:
Introduction and Motivation Nowadays, Linked Data is increasingly used in a variety of contexts  1. Thus, an increasing number of people in the modern knowledge society get in contact with it. It is no longer exclusively used by domain experts but also by inexperienced users. Data verification, exploration and sensemaking are of great importance in the field of Linked Data 2. However, it is difficult for humans to manually analyze and explore data  1. This problem becomes worse with casual users  1. Visualization of Linked Data helps to overcome this hurdle  1, 3, 4. It provides users an intuitive way to explore the content  2, 5  , identify more easily errors  3  , infer correlations and causalities  2  and give expert users new perspectives.
Introduction and Motivation Nowadays, Linked Data is increasingly used in a variety of contexts  1. Thus, an increasing number of people in the modern knowledge society get in contact with it. It is no longer exclusively used by domain experts but also by inexperienced users. Data verification, exploration and sensemaking are of great importance in the field of Linked Data 2. However, it is difficult for humans to manually analyze and explore data  1. This problem becomes worse with casual users  1. Visualization of Linked Data helps to overcome this hurdle  1, 3, 4. It provides users an intuitive way to explore the content  2, 5  , identify more easily errors  3  , infer correlations and causalities  2  and give expert users new perspectives. The interweaving of the Linked Data into the Web of Data is regularly done at large-scale  3.
As a consequence, visualizing large-scale datasets has become a major research challenge, of which readability and understandability are vital requirements  2, 3, 6. Several visualizations for Linked Data datasets have been proposed in the last decade  1, 5, 6, 7. However, many studies indicated a low level of user satisfaction concerning (1) the readability of large-scale Linked Data visualizations  3, 5  and (2) the lack of useful mechanisms for "content-level visualization", including support for visual descriptive summaries, overviews and selective visualization of Linked Data dataset parts  2, 3, 5. These issues are especially problematic for casual users. This requires a new generation of user-friendly visualization tools that offer readable visualizations of Linked Data datasets for all users  3, 5  and provide visual descriptive summaries of the large-scale ones  3, 5, 8.
State of the Art: Linked Data Visualization Approaches Dadzie and Rowe proposed the most comprehensive survey of existing Linked Data visualization approaches  3. They divided the approaches into 2 categories: browsers with visualization options and text-based. They concluded that the majority of the implemented approaches are designed to be used only by domain expert users and do not provide overviews on the dataset. We organize Linked Data visualization approaches into 2 categories: "Approaches proposed specifically for visualizing Linked Data" and "Approaches proposed mainly for visualizing RDF ontologies supporting the specific requirements of Linked Data". Table1 summarizes the characteristics of some Linked Data visualization approaches. Most available Linked Data visualization approaches are designed to be used only by expert users  1, 3, 7.
The first method consists on suggesting a reasonable workflow for datasets exploration: "1-Overview, 2-Filter, 3-Visualize" e. g. , Rhizomer and RDF Gravity. The second method uses the clustered graph navigation paradigm. All text-based approaches do not handle scalability  1. All scalable graphbased approaches do not offer an efficient readable visualization  1, 6. DBpedia Viewer manages the scalability issue. However, it visualizes only one Linked Data source -DBpedia. All approaches surveyed do not provide descriptive summary of Linked Data sources. LD-VOWL is based on knowledge base summary approach. However, it visualizes only the schema information of the Linked Data sources and do not display the Assertional Box (ABox). Graph-based approaches help in obtaining a better understanding of the data structure  1, 6.
Role relations between the related nodes are represented using labeled edges. Memo Graph extracts and visualizes the schema information of the Linked Data dataset. Class nodes are slightly larger than instance nodes. The layout of the user interface is designed to be simple as possible. The interface is divided into 3 parts: The "MEMO GRAPH Viewer" displaying the visualization, the "MEMO GRAPH Details" listing details about a selected graph node, and the "MEMO GRAPH Search" providing a keyword search option.
Structure Visualization of a Large-scale Linked Open Data Dataset (DBpedia) with Memo Graph Memo Graph is tested on DBpedia, large-scale endpoint which is a semantic knowledge base built from structured and extracted information from Wikipedia. Do date; DBpedia covers 685 classes described by 2,795 datatype and object properties and about 4,233,000 instances. Fig. 3  shows a screenshot of Memo Graph that it is utilized for visualizing N 1 = 20 "instance best descriptors" of the "Sport" class. Fig. 3. Screenshot of Memo Graph that it is utilized for visualizing 20 "instance best descriptors" of the "Sport" class (DBpedia).
Conclusion In this paper, we presented an extension of Memo Graph that proposes a comprehensible and legible visualization of Linked Data datasets. Memo Graph was designed to be used by both expert and non-expert users. It tends to avoid the problems of scalability by limiting the number of visible nodes. To do that, Memo Graph is based on an incremental knowledge base summarization algorithm, IBDE, which automatically extract the "most important" concepts in the knowledge source by generating a "graph summary" of the Linked Data dataset, while hiding away the "less important" ones. At the beginning, we elaborated a state of art, focusing on approaches for visualizing Linked Data. This study showed that the majority of the approaches target expert users and do not offer an understandable graph, when visualizing large-scale inputs.
Approaches proposed mainly for visualizing RDF ontologies supporting the specific requirements of Linked Data" RDF Gravity  ‡ ‡ ‡ Graph. * * * * * Java application IsaViz  § § § Graph * * * * Java application † http://www. simile-widgets. org/exhibit  ‡ http://dataviewer. zitgist. com § inkeddata. uriburner. com ** http://sig. ma † † http://code-research. eu/ ‡ ‡ http://www4. wiwiss. fu-berlin. de/bizer/ng4j/disco § § http://ode. openlinksw. com/ *** http://api. talis. com/stores/iand-dev1/items/dipper. htm † † † mes. github. io/marbles ‡ ‡ ‡ http://semweb. salzburgresearch. at/apps/rdf-gravity/ § § § https://www. w3. org/2001/11/IsaViz/overview. html.
Methods Heart "OMics" in AGEing Database The HOMAGE study database is constructed and maintained at the Studies Coordinating Centre in Leuven, Belgium. By sending anonymized data, the contributing partners confirmed that their study complies with good clinical practice (Helsinki Declaration), that all participants provided written informed consent, and that at the time of its conduct the study conformed to national regulations on clinical research in humans and on the protection of privacy. A detailed description of the database is available elsewhere. 6 The database used for this analysis was locked on January 16, 2016.
Cohorts Analyzed The entire HOMAGE database consists of 21 studies (n=46 134). Studies were eligible for inclusion in this analysis if information was available on fatal and/or nonfatal HF, with at least 20 events. We excluded 4 HF cohorts (n=4312), as well as 9 studies (n=7565) without information on HF outcome, and 4 studies with <20 HF events (n=4120). Among the 4 included studies (n=30 137), persons with a history of HF at baseline (n=223), those with missing information on incident HF (n=144), or those lost to followup (n=279) were excluded. Figure  1  gives a detailed overview of the selection of the studies. Table  1  describes the 4 cohorts included in this analysis.
Statistical Analysis For database management and statistical analyses, SAS software version 9. 3 (SAS Institute, Cary, NC, USA) was used. Continuous variables were expressed as meanAESD and categorical variables as proportions. Missing values for BMI, systolic blood pressure, diastolic blood pressure, heart rate, glucose, cholesterol, and creatinine were imputated from the regression slope on age after stratification for cohort and sex. For comparison of means and proportions, the large sample Z-test and chi-square test were used, respectively. Statistical significance was a 2-sided P value of <0. 05. Time-to-event analysis was conducted using the Cox proportional hazard model. Follow-up time in the Health ABC study was limited to the maximum follow-up time of the other 2 included cohorts, ie, 5.
This test measures the level of agreement between observed versus predicted number of events, which were visually plotted across deciles of the predicted risk. SAS macros developed in the ARIC (Atherosclerosis Risk in Communities) study  17  were used for these calculations. To internally validate our models, 100 bootstrap samples were generated, sampling subjects with replacement. We adjusted our AUC obtained from the internal validation for optimism. The external validation of our model was done in the ASCOT cohort.
Model Assessment The AUC for discriminating HF risk based on our Cox model was 0. 706. After bootstrapping, we corrected the AUC for overoptimism and obtained an AUC of 0. 702. Observed versus expected numbers of HF according to deciles of the 5year risk show good agreement (Grønnesby-Borgan v 2 7. 9; P=0. 54). Figure  3  shows the calibration of the Cox model by plotting deciles of HF risk. We assessed calibration among the individual studies. Grønnesby-Borgan v 2 statistics for PREDICTOR, Health ABC, and PROSPER were 2. 9 (P=0. 97), 5. 1 (P=0. 83), and 16. 2 (P=0. 06), respectively. Figure  4  shows the observed versus expected number of events for each individual study. The risk function was externally validated using data from ASCOT. In 13 121 participants (mean age 63. 3 years, 23. 0% women), 191 HF events (HF hospitalization or death) occurred over a median follow-up of 5.
Sources of Funding This work is supported by the  European Union :  HEALTH-F7-305507 HOMAGE (EU FP7 305507  http://www. homage-hf. e u). The  European Research Council Advanced Researcher  Grant-2011-294713-EPLORE and the  Fonds voor Wetenschappelijk Onderzoek Vlaanderen, Ministry of the Flemish Community, Brussels, Belgium  ( G. 0881. 13  and  G. 088013 ), currently support the  Studies Coordinating Centre in Leuven.
Whole transcriptome sequencing (RNA-seq) has become a standard for cataloguing and monitoring RNA populations. One of the main bottlenecks, however, is to correctly identify the different classes of RNAs among the plethora of reconstructed transcripts, particularly those that will be translated (mRNAs) from the class of long non-coding RNAs (lncRNAs). Here, we present FEELnc (FlExible Extraction of LncR-NAs), an alignment-free program that accurately annotates lncRNAs based on a Random Forest model trained with general features such as multi k-mer frequencies and relaxed open reading frames. Benchmarking versus five state-of-the-art tools shows that FEELnc achieves similar or better classification performance on GENCODE and NONCODE data sets. The program also provides specific modules that enable the user to fine-tune classification accuracy, to formalize the annotation of lncRNA classes and to identify lncRNAs even in the absence of a training set of non-coding RNAs.
We used FEELnc on a real data set comprising 20 canine RNA-seq samples produced by the European LUPA consortium to sub-stantially expand the canine genome annotation to include 10 374 novel lncRNAs and 58 640 mRNA transcripts. FEELnc moves beyond conventional coding potential classifiers by providing a standardized and complete solution for annotating lncRNAs and is freely available at https://github. com/tderrien/ FEELnc.
45, No. 8  PAGE 2 OF 12 as gene expression regulation, control of translation or imprinting. Following RNA sequencing, the computational reconstruction of transcripts models either by genome-guided  (11, 12)  or de novo assembly  (13)  usually produces tens of thousands of known and novel transcript models. Among this wealth of assembled transcripts, it remains crucial to annotate the different classes of RNAs and especially to distinguish protein-coding from non-coding RNAs. To this aim, several bioinformatic tools have been developed in order to compute a coding potential score (hereafter termed CPS) used to discriminate the coding status of the RNA gene models. Broadly, they can be divided into programs using sequence alignments, either between species  (14)  or alignments to protein databases  (15) , and alignment-free software  (16) (17) (18).
However, the definition of the longest ORF can vary between programs, especially when it involves the strict inclusion of either or both start and stop codons. This is particularly important to model since some transcripts from reference annotations and/or newly assembled transcripts are not fulllength. For instance, the number of protein-coding transcripts in the human EnsEMBL (v83) annotation  (22)  lacking a start codon or a stop codon is 7677 (∼10%) and 16 649 (∼25%), respectively. A complementary feature to discriminate mRNAs from non-coding RNAs is the relative frequency of oligonucleotides or k-mer (where k denotes the size of the oligonucleotide). Some tools already use kmer frequencies but are often limited to one and/or small k-mers (generally k ≤ 6), whereas longer k-mers could help resolve ambiguities by taking into account lncRNA-specific repeats or spatial information  (23, 24).
We benchmarked FEELnc and five existing programs (PhyloCSF  (14) , CPC  (15) , CPAT  (16) , PLEK  (17)  and CNCI (18)) using known sets of lncRNAs annotated in multiple organisms (GENCODE for human and mouse  (27)  and NONCODE for other species (28)), and showed that FEELnc performance metrics outperformed or are similar to state-of-the-art programs. We developed FEELnc to be used on 'non-model' organisms for which no set of lncRNAs is available by deriving species-specific lncRNA models from mRNA sequences and automatically computing the CPS cut-off that maximizes classification performances. FEELnc also allows users to provide their own specificity thresholds in order to annotate high-confidence sets of lncRNAs and mRNAs and to define a class of transcripts with ambiguous status. Finally, as part of the LUPA consortium  (29) , we produced 20 RNA sequencing data sets from 16 different canine tissues and applied FEELnc on the reconstructed models to annotate 10 374 lncRNA and 58 640 mRNA new transcripts from known and novel loci.
Data set For the sake of reproducibility, all data sets and scripts used to generate the benchmark files are available in Supplementary Data. Human long non-coding and protein-coding genes were obtained from the manually curated GENCODE version 24 annotation (EnsEMBL v83 corresponding to the GRCh38 human genome assembly) selecting the long noncoding gene biotypes 'lincRNA' and 'antisense', and 'protein coding' for coding genes. From each of this set, 10 000 transcripts were extracted and further divided into two sets of 5000 transcripts, that are used for the learning and the testing steps, denoted HL and HT data sets, respectively. Importantly, only one transcript per locus was extracted for all biotypes in order not to create a bias by introducing two isoforms of the same gene in both the HL and HT sets.
For mouse, we used the GENCODE version M4 annotation (EnsEMBL v79) and derived the learning and testing sets in the same way as for human (denoted ML and MT). Due to the lower number of GENCODE lncRNAs annotated in mouse compared to human, each file contains ∼2000 lncRNAs and 5000 mRNAs. For 'non-model organisms', lncRNAs belonging to the lincRNA and antisense classes (NONCODE codes 0001 and 1000, respectively) were downloaded from the latest version of the NONCODE database (NONCODE 2016) (28) while mRNAs were retrieved from the EnsEMBL database (v84). A summary of the number of mRNAs/lncRNAs per species is available in Supplementary Table  S1. Whole transcriptome sequencing of dog RNA samples (n = 20) was performed by the LUPA consortium. These biological samples, corresponding to 16 unique tissues PAGE 3 OF 12 Nucleic Acids Research, 2017, Vol.
FEELnc coding potential module (FEELnc codpot ) FEELnc codpot predictors. The second FEELnc module aims at computing a coding potential score given the assembled sequences following transcriptome reconstruction. To deal with the incompleteness of ORF annotation where both the reference and the reconstructed transcripts may not be full-length, FEELnc computes all ORFs and annotates five ORF types from the stricter 'type 0' which corresponds to the longest ORF having both a start and a stop codon, to the more relaxed 'type 4' that is the whole input RNA sequence (see Supplementary Data for detailed description of the ORF types). Because the size of the proteincoding ORF is generally correlated with the length of the input RNA sequence, we used the ORF coverage, i. e. the proportion of the transcript size covered by an ORF, as the first predictor to discriminate mRNAs/lncRNAs in the FEELnc model.
The second predictor of FEELnc relies on the computation of the multi k-mer frequencies between mRNAs and lncRNAs. Biases in nucleotide frequencies and codon usage have already been described in the literature as important discriminative features between coding and non-coding RNAs  (21, 31). Within the framework of FEELnc, we developed an extremely fast and exact k-mer counter called KIS (for K-mer in short) that relies on the open-source GATB library  (32). For example, KIS can compute all 6-mers (hexamers) and 12-mers of the human GRCh38 genome assembly (∼3 billions k-mers) in ∼2 min and 2 min 50 s, respectively (on a linux RedHat station with one core Intel(R) Xeon(R) CPU X5550 @ 2. 67GHz). Due to the high speed of KIS, a major contribution of this work was to be able to combine different lists of k-mers, including longer k-mers, in order to better discriminate lncRNAs from mRNAs.
Random forest classification and optimized coding potential cut-offs. The aforementioned predictor scores are incorporated into a machine learning method--Random Forest (RF) (26)--that computes a coding potential score (CPS) for each input training transcripts. As also shown by others with respect to lncRNAs annotation, RF often outperforms other machine learning techniques especially due to the random sampling of features to build the ensemble of trees  (36, 37). In addition, our RF model which is based on the randomForest R package  (38) , can deal with im-PAGE 4 OF 12 balanced training set by down-sampling the majority class (most likely mRNAs in many organisms). In fact, the CPS in our RF model corresponds to the proportion of all trees (500 trees by default) which 'vote' for the input sequence to be coding or non-coding.
A proportion close to 0 will indicate a non-coding RNA and close to 1 an mRNA. To define an optimal CPS cut-off, FEELnc automatically extracts CPS that maximizes both sensitivity (Sn) and specificity (Sp) (see performance section) based on a 10-fold cross-validation. Using the ROCR package  (39) , FEELnc provides users with a two-graph ROC curve in order to display the performances of the model and to visualize the optimal CPS (Figure  1 A ). Even if this approach aims at providing the highest performances, it could sometimes misclassify coding and/or non-coding transcripts whose CPS is closed to the optimized threshold  (40). To take this into account, FEELnc allows fixing two minimal specificity cut-offs for lncRNAs and mRNAs (this approach is termed 'two cut-offs'). This naturally leads to the annotation of two high-confident classes of lncRNAs and mRNAs and also to the definition of a third class of ambiguous transcripts (i.
The shuffle strategy employs a more sophisticated method which is based on the idea that lncRNAs are derived from 'debris' of protein-coding genes  (41, 42) , as exemplified by the Xist lncRNA that emerged from the disruption of the mRNA gene Lnx3  (43). To this end, we shuffled mRNA sequences from the reference annotation using the Ushuffle program  (44) , while preserving a given k-mer frequency of the input sequences. Note, however, that shuffling sequences and preserving frequencies for one fixed k-mer does not constrain the frequencies of the other k-mers. In addition, when k increases, it is possible that Ushuffle could not permute some input sequences because of the constraint to preserve the given k-mer frequencies. Finally, the cross-species strategy makes use of lncRNA sets annotated in other species to extract non-coding predictors and train the RF model.
For the latter strategy, we trained the FEELnc model using human mRNAs and species-specific lncRNAs (NONCODE lncRNA catalogues being available in 13 different species). For all strategies, we assessed the performance on the HT data sets. Performance evaluation. We evaluated the performance of FEELnc and five other state-of-the-art programs: CNCI (version 2 Feb 28, 2014)  (18) , CPC (version 0. 9-r2)  (15) , CPAT (version 1. In addition, we used two complementary metrics in order to capture the global performance of the tools in a single measure  (45) : -F-score = 2 × precision×sensitivity precision+sensitivity , which is a statistic measuring the harmonic mean of precision and sensitivity; - MCC (Matthews Correlation Coefficient) = T P×TN-F P×F N √ (T P+F P)(T P+F N)(TN+F P)(TN+F N) , which is particularly useful when the two classes are of very different sizes (which is often the case for mRNAs and lncRNAs in non-model organisms) and which could be seen as a correlation coefficient between the true classes and the predicted classes  (46).
For each performance metric, we considered lncRNAs as the negative class and mRNAs as the positive class. Note that, as a binary classification, the mRNA specificity corresponds to the lncRNA sensitivity (and conversely). Moreover, for the CPAT and PLEK programs, which allow training their models, we used species-specific set of mRNAs/lncRNAs for training (called CPAT train and PLEK train ). Nevertheless, contrary to FEELnc, PLEK and CPAT required us to a priori extract the CDS of the mRNA input file to learn the coding parameters. For CPAT train , we referred to the optimal CPS cut-off mentioned on their website  (16)  to discriminate between coding and non-coding RNAs. A detailed description of the command lines used to run each program is given in Supplementary Data.
FEELnc classifier module (FEELnc classifier ) Given a known reference annotation, it is essential to classify newly annotated lncRNAs based on their closest annotated transcripts. It will potentially guide researchers towards functional annotation and relationships between lncRNAs and their annotated partners (lncRNA/mRNA pairs for instance). For this purpose, the FEELnc classifier module (FEELnc classifier ) employs a sliding window strategy (whose length is fixed by the user) around each lncRNAs to report all the reference transcripts located within the window. Not only does the FEELnc classifier annotate lincR-NAs and antisense lncRNAs but it also formalizes the definition of lncRNA subclasses with respect to annotated transcripts (Figure  1B ). First, these rules involve the direction (sense or antisense) and the type of interactions (genic or intergenic).