dataesr/scientific-paragraphs-categorization huggingface dataset:
Refine filters the results from one tool with any other module of the system and makes it possible to get a list of genes corresponding to very specific criteria. In addition, lists of genes can be analyzed statistically to highlight the features they share using a similar approach to DAVID  (2). Results can be visualized as text or using interactive graphs. Manteia is a very versatile system that can be used to analyze gene lists in many ways including the identification of genes of high biological or medical interest. Refine and Query Builder have been used in several projects to identify new disease genes using a data mining approach  (1, (3) (4) (5). In this new version, these tools are complemented with an entirely automated solution using a machine learning software called Lookalike.
Lookalike is able to predict new disease genes based on their similarities with known causal genes in the different datasets contained in the system. This tool is also designed to analyze groups of diseases in order to highlight their specificities and use them in turn to further increase the quality of predictions. With this new release, we have also updated the gene expression module with RNA-seq and microarray data as well as the statistics module of the system with a set of tools designed to analyze lists of genes to compare their functions and to better understand their properties.
Expression data In this new version of Manteia, the expression data previously based on in situ hybridizations (ISH) and expressed sequence tags (EST) have been replaced by RNA-seq and microarray data. These data originate from the RNA-Seq Atlas  (6). They include gene expression profiles from healthy D708 Nucleic Acids Research, 2017, Vol. 45, Database issue individuals for adipose tissue, colon, heart, hypothalamus, kidney, liver, lung, ovary, skeletal muscle, spleen and testes. The microarray dataset includes expression profiles from cancer cell lines as well. The interface developed to access these data in Manteia is based on the original tool from the RNA-Seq Atlas, offering the possibility to search for genes with a defined expression profile in different tissues and experimental conditions.
Statistics tools The statistics module of Manteia is designed to analyze the annotation of a list of genes in order to highlight its main characteristics in the different OMICs data available in the database. This is achieved by analyzing the distribution of each annotation feature in this list compared to a reference like the genome or a given microarray (1). This analysis is performed for one annotation category at a time. However, it is often more informative to have a global picture of enriched terms for all the annotation categories available. To do this, we have developed Batch statistics, which is a tool that allows the user to combine annotation categories and filters to generate a flat file containing all the most representative annotations of a given set of genes in terms of functional annotations, protein motifs, phenotypes and chromosome locations.
Several results from Batch statistics generated from different sets of genes can be further analyzed using another tool called Class count. Class count lists each annotation feature and reports the gene sets where it has been found. This makes it possible to identify the terms that are specific to a group of genes and list which ones are shared by one or several gene sets. This enrichment statistics approach compares the annotation of a group of genes to a larger dataset from which it originates. In order to compare two independent datasets, we have also developed a module called Dataset comparison. With this new tool, the terms of a given annotation category are listed for two lists of genes. Their respective occurrences are then tested using the Fisher exact test to see if the distribution is significantly different.
Machine learning and disease gene prioritization In many cases, several genes can lead to the same disease or diseases that are closely related. The annotation of these genes can be analyzed to see if some rules can be learned and used in turn to detect new candidate genes. These rules can be about anything like shared biological functions, interac-tions and chromosomal positions. To do this, we have developed a machine learning software called Lookalike. Lookalike uses an aggregation algorithm similar to Endeavour  (7)  and ToppGene  (8). It analyzes the annotation of known genes in several datasets from the system (Gene Ontology, phenotypes, protein motifs, chromosome distribution, sequence homology, interactome and the co-occurrence of genes in PubMed articles) to find new candidates sharing a maximum of similarities.
Lookalike is therefore the perfect upgrade for Manteia because not only this software can utilize most of the datasets and statistics tools already developed for the system but also the aggregation algorithm is known to provide exceptional performances  (9, 10). Lookalike is very easy to use and does not require any bioinformatics or programming knowledge. The user is prompted to enter known disease genes in the training set panel of the graphical interface to run the analysis on the entire genome (Figure  3A ). The search can be restricted to a list of candidates by entering their names in the candidate genes panel. More advanced options make it possible to select specific datasets to use with the algorithm, change the method used to rank candidate genes or even test the accuracy of the tool by entering known targets to see where they rank in the final classification (Figure  3A ).
Advantageously, Lookalike is integrated into a comprehensive data mining environment to evaluate and to select the best training and candidate genes possible. Because Manteia is a multi-species system, it offers the possibility to use data from orthologous genes during the prioritization. This gives the opportunity to use the mouse phenotype dataset, which is the most comprehensive of the database, to prioritize human genes, for example. The result page ranks the best candidates and shows the influence of every selected datasets in the final classification. In addition, an interactive graph allows to visually estimate the quality of a prediction and browse the results (Figure  3B ). A more detailed description of Lookalike and its performances will be published elsewhere.
Combining statistics and data mining approaches to enhance predictions Many specific diseases belong to a more general group like a spinal muscular atrophy belongs to the category of myopathies. The diseases from a same group can be analyzed in Manteia in order to highlight the most specific features of the targeted disease and further increase the prioritization accuracy. This is achieved by using Batch statistics in combination with Class count to identify the annotation elements that are specific to a disease compared to the other ones. Once identified, these elements are given more importance with Lookalike using a specific weight during the prioritization. As an example, we have computed the data corresponding to myopathy genes. We used 141 genes corresponding to six different types of myopathies (muscular dystrophies, congenital myopathies, myotonic syndromes, ion channel muscle diseases, metabolic myopathies and congenital myasthenic syndromes) according to the table of monogenic neuromuscular disorders  (11).
The lists of disease genes, specific features and corresponding P-values obtained from Class count can be displayed from Lookalike's user interface by selecting a disease from the menu (Figure  Figure 1. RNA-seq and microarray data. The RNA-seq and microarray module of Manteia makes it possible to search for genes with a defined expression profile in a mixture of tissues in both a normal and a cancer condition. In addition the radio buttons and the lower panel are designed to search for genes differentially expressed in two given conditions. The upper screenshot exemplifies the search for genes overexpressed in cancer kidney cells compared to healthy kidney cells with a minimum fold change of 2. The lower screenshot shows one of the resulting gene (ODC1) and the corresponding plots representing its expression levels for all the tissues in both RNA-seq and microarray datasets.
DISCUSSION With the continuous addition of new data and tools, Manteia aims to assist scientists in their work by providing state of the art solutions to analyze their data. In this new version of the system, new microarray and RNA-seq data have been included. In addition, new ways to compare gene expression between datasets and new statistical tools are available. For many years, we have developed several approaches to prioritize genes using the information contained in the database with tools like Refine and QueryBuilder. With Lookalike, these tools are complemented with an entirely automated approach able to predict disease genes based on their similarities with known causal genes. However, we tried to keep this tool as open as possible so its predictions can benefit from the investigator's expertise with the possibility to manually select and weight the features of interest.
Like everything else in the system, Lookalike can be used together with every other tools and data available. It makes it possible to constrain the candidate list to genes present on a given chromosomal region or in a given pathway, select the candidates based on a phenotypic feature instead of a disease or to further analyze the resulting candidates by accessing their annotation or using all the data mining capabilities of  The user can use biological names or Ensembl identifiers to enter known disease genes and a set of candidates to be prioritized. If no candidates are provided, the entire genome is analyzed. By default, all the different datasets contained in Manteia are taken into account in the analysis, including the phenotype annotation from mouse orthologs. However the user can select the most relevant options using the interface.
The ranking of genes in individual datasets is displayed on the right hand panel with a gradient color showing their relative importance in the final prioritization. the system. Lookalike's predictions can be further enhanced by searching for the specific elements of a disease using the analyses from Batch statistics and Class count. These data are available for myopathy genes directly from the user interface and can easily be re-used to perform new candidate gene prioritizations for this type of disease. In the future, new diseases will be included in Lookalike to make it easier for different specialists to use this tool and discover new genes of interest. Lookalike and the other predictive tools of the system rely on an abundant and accurate gene annotation to provide the best results possible.
This will perform the best prioritization possible for the selected disease. (B) When a prioritization is performed on the entire genome, the training genes are displayed on the graph, showing their rank distribution. These genes are highlighted in red in the following ranking list. When most training genes are found within the first positions, it means that the prioritization process picked the most relevant terms shared by a maximum of disease genes which ensure the overall quality of the prediction. The ranking of genes in individual datasets is displayed on the right hand panel with a gradient color showing their relative importance in the final prioritization.
ACKNOWLEDGEMENTS I thank  Olivier Pourquié  and the members of his laboratory for their numerous inputs and comments about Manteia and Lookalike. I thank  Stéphane D. Vincent ,  Nacho Molina ,  Ziad Al Tanoury  and  Goncalo Cadete Vilhais Neto  for their careful reading of the manuscript and helpful comments and suggestions. I am also grateful to  Serge Uge  and  Guillaume Seith  for the configuration and maintenance of the servers hosting the system. Last, I thank the authors of the resources used by Manteia for sharing their work and data with the scientific community.
This method for simulation of time series is implemented by Boore in Fortran  [23]. In order to generate time acceleration series for the numerical examples, we implemented the stochastic method in Matlab software  [24]. Parameters of the ground spectrum were taken as suggested values in reference  [22]  and summarized as bellow: -The source E(M 0 , f): is based on the source spectral shape AS00 (Atkinson and Silva (2000) for California). -The path P (R, f ): accounts the effects of geometrical spreading, attenuation and the increase of duration with distance due to wave propagation and scattering. P (R, f ) = Z(R) exp - πf R Q(f )c Q ( 19 ) where c Q = 3. 5, Q(f ) = 180f 0. 45 and Z(R) defined by: Z(R) = ⎧ ⎪ ⎨ ⎪ ⎩ R 0 R if R ≤ R 1 Z(R 1 ) R 1 R p1 if R ≥ R 1 (20) with R 0 = 1, R 1 = 40 and p 1 = 0.
5 cm n d , α = 0. 1, n d = 1 for the Bouc-Wen oscillator; μ = 0. 01 and g = 9. 81 m. s -2 for the Coulomb oscillator. Figure  3  shows an illustration of oscillator responses under seismic excitation, obtained with the Runge-Kutta algorithm using Matlab software  [24]. The failure of an oscillator was verified by comparing displacement x(t) with a displacement limit x 0 i. e. in the definition of Equation (  2  x 0 it was not reached. Three displacement limits were tested: x 0 = 7 cm, x 0 = 10 cm and x 0 = 13 cm. These limits in increasing order can be associated to three damage states: minor, moderate and severe in real applications. Fragility curves obtained by SSI, MLE and PSDM/PSCM methods with N = 5000 ground accelerations generated from Boore's model (M = 7, R = 9 km) are given in Figures  4 5 6 for respectively the linear, Bouc-Wen and Coulomb oscillators.
8-storey frame structure An 8-storey floor shear frame inspired by references  [26, 27]  was tested. Lumped masses are given in Table 2. The non-linear hysteretic model with K y /K 0 = 0. 1 in Figure  8  was considered for the shear stiffness of one storey K. The initial inter-storey stiffness was K 0 = 2. 1 × 10 8 N. m -1 and the yielding inter-storey displacement was Δ y = 0. 01 m. Rayleigh damping was considered as a linear combination of mass matrix M and stiffness matrix K as : C = 0. 01M + 0. 005K. The height of each storey was H = 3. 0 m. The same ground motions generated with the Boore model in the previous numerical example were used for the construction of fragility curves. Structural responses were simulated using ANSYS finite element software  [28]  and are illustrated in Figure  9.
An example of this function is illustrated in Fig. 1. Contrary to MAW [3], the previous linear-time algorithm, in pMAW we do not consider our data structures globally; we rather consider each LCP-interval independently. This important property will allow us to use parallel computations, as shown in Section 3. 3. Overall Complexity. We use arrays SA and LCP, which can be computed in time and space O(n)  [14, 7]. There also exists a representation which uses n + o(n) bits of storage space and supports rank and select on a bit-vector of size n in constant time  [11]. We also use two stacks, LifoPos and LifoSet, where we push and pop O(n) elements, each containing at most σ integers. Thus the whole algorithm requires time and space O(σn). We obtain the following result. The advantages of pMAW over existing works are as follows.
Experimental Results We implemented algorithm pMAW as a programme to compute all minimal absent words of a given sequence. The programme was implemented in the C programming language, using Open Multi-Processing (OpenMP) API for sharedmemory multiprocessing programming, and developed under GNU/Linux operating system. It takes as input arguments a file in (Multi)FASTA format and the minimal and maximal length of minimal absent words to be outputted; and then produces a file with all minimal absent words of length within this range as output. There are additional input parameters; for example, the number t of available processing elements. The implementation is distributed under the GNU General Public License (GPL), and it is available at http://github. com/ solonas13/maw, which is set up for maintaining the source code and the manpage documentation.
The experiments were conducted on a Desktop PC using 1 to 16 cores of 2 Intel Xeon E5-2670V2 Ten-Core CPUs at 2. 50GHz and 256GB of main memory under 64-bit GNU/Linux. To evaluate the efficiency of our implementation, we compared it against the corresponding performance of MAW  [3] , which is currently the fastest available implementation for computing minimal absent words. We generated three ran- Optimal speed-up pMAW speed-up for Homo sapiens genome pMAW speed-up for Mus musculus genome Fig. 4 : Elapsed-time comparison of pMAW and MAW and relative speed-up of pMAW for computing minimal absent words using real DNA sequences dom sequences of length 10Mbp, 100Mbp, and 1Gbp, respectively, by using a uniform frequency distribution of letters of the DNA alphabet. We computed all minimal absent words of length at most 20 for each sequence.
We considered the genomes of Homo sapiens and Mus musculus, obtained from the NCBI database (ftp://ftp. ncbi. nih. gov/genomes/). We computed all minimal absent words of length at most 20 of the complete sequence of the Homo sapiens (2, 937, 639, 113bp) and Mus musculus (2, 647, 521, 431bp) genomes-ignoring unknown bases. We considered both the 5 → 3 and the 3 → 5 DNA strands. Fig. 4a  depicts elapsed-time comparisons of pMAW and MAW, including the sequential part of the algorithm. pMAW becomes the fastest in all cases when t ≥ 2 accelerating the computation by more than a factor of two when t = 16. Notice that the y-axis is on logarithmic scale. The measured relative speed-up of pMAW is illustrated in Fig. 4b. The relative speed-up was calculated as the ratio of the runtime of pMAW on 1 core to the runtime of pMAW on t cores, excluding the sequential part of the algorithm.
In this article, we report on ARIEX (Automated Ranking of Information EXtractors), which is a method to evaluate, compare, and then rank web information extraction proposals. It overcomes the problems that we have found in the literature since it reduces the bias that a researcher can introduce in the results because it is automated; it can easily accommodate new performance measures as they are devised and proven to be adequate in our context because it is open; it does not commit to a particular kind of extractor, but has been designed to rank as many proposals as possible because it is agnostic; it provides a clear guideline regarding how the experimental environment must be set up, with a special emphasis on selecting the most appropriate set of performance measures so that the conclusions are global and unbiased; it provides a method to compute as many evaluation splits as possible out of the datasets available; it provides a method to compute the experimental data that takes into account how matchings are computed and does not neglect unsupervised or heuristic-based proposals; it provides a new statisticallysound method to purge the experimental data, it also takes into account derived measures, and provides a normalisation method that makes it open; and it provides a statistically sound method to compute per-measure rankings and then combine them all taking into account both a researcher's preferences and the deviations of the performance measures.
Description of our method ARIEX consists of the steps that are summarised in Fig. 2. The first step consists in setting up the experimental environment. The second step consists in computing a number of evaluation splits, that is, pairs of learning and testing sets. The third step consists in running the selected proposals on the previous evaluation splits to gather raw experimental data, that is, the values of the selected performance measures as they are computed on the available evaluation splits. The forth step consists in cooking the raw experimental data as follows: first, they are purged, then derived measures are computed, and, finally, the purged data and the derived data are normalised. The fifth step consists in computing a local ranking per performance measure and then a global ranking.
Step 1: Set up the experimental environment The experimental environment consists of the hardware and the software used to evaluate a number of proposals plus some data that must be provided by a researcher, namely: the proposals to be ranked, the datasets on which they must be evaluated, the performance measures to compare them, and the values of the parameters. Typically, the researcher is an author who has devised a new web information extraction proposal and wishes to rank it with respect to others in the literature, or a practitioner who needs to extract information from a web site and has to make an informed decision regarding which of the proposals in the literature is the most appropriate. We provide additional details in the following subsections. Describe the hardware and the software.
The most closely related proposals must be selected because, otherwise, we cannot prove that the conceptual innovations in a new proposal are worth from a practical point of view; but not only must a new proposal beat the most closely-related ones, but also others that are conceptually different but have proven to achieve good performance. Select some datasets. Having standard dataset repositories is very important since they allow to compare different proposals on a corpora that can be carefully selected so that the documents are representative enough of both the regularities and irregularities with which web information extractors have to deal. In other words, such repositories should provide controlled, well-documented datasets that put an emphasis on the many difficult cases with which an information extractor has to deal.
This makes it impossible that a proposal be evaluated only on datasets on which it works very well and, thus, reduces the chances to bias the results. The list of public repositories available includes RISE, TBDW, and TIPSTER 1  ; furthermore, Freitag  [24]  compiled the Seminar Announcement collection, Califf and Mooney  [25]  compiled the Job Posting collection, and Reuters made available the Reuters-2157 collection on company acquisitions 2  ; other authors have assembled their own public repositories  [10, [26] [27] [28]  and some conferences have also published some repositories, chiefly the MUC conferences. Note that not every repository provides adequate datasets for every proposal. For instance, there are repositories that focus on freetext documents and others that focus on semi-structured documents; there are repositories that focus on single-record documents and others that focus on multi-record documents; furthermore, there are many repositories in which each dataset was gathered from a single site and others in which some datasets were gathered from different sites and are then appropriate for open information extractors only.
A researcher must select the repositories and the datasets that are adequate for the proposals that she or he wishes to rank. Regarding the number of datasets, there is not a standard in the literature. Our suggestion is that there should be at least 20-30 datasets available and that each one should provide at least 20-30 documents; these figures are generally considered large enough to draw statistically solid conclusions  [29]. ARIEX requires the datasets to provide the same number of documents, so that it can create appropriate evaluation splits. In cases in which the datasets available do not provide the same number of documents, our suggestion is to discard some documents randomly or to split them into several smaller datasets. The reason why we do not think that the datasets should provide more than 20-30 documents is that they all have to be annotated for evaluation purposes, which is a time-consuming and errorprone task; neither think we that a proposal that requires a large number of documents to achieve good results is useful from a practical point of view.