dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments Niek de Klein  was funded by the  Techniekbeurs of the Hogeschool Arnhem Nijmegen. Work in Seung Yon Rhee 's group is funded by grants from the  National Science Foundation  ( MCB-1052348 ,  IOS-1026003 , and  MCB-0820823 ) and  Department of Energy  ( BER65472 ) and a seed grant from the  Association for Independent Plant Research Institutes (AIPI).
set evalue all, evalue small, evalue reblast, DBD domains open transcription factors as input small proteins database, all proteins database, transcription factors database ← initialize BLAST reference databases putative miPs ← new list for transcription factor in transcription factors do TF domains ← InterproScan(transcription factor) subjects all ← BLAST transcription factor against all proteins database for subject in subjects all do if length(subject) ≤ 550 and length(subject) ≤ 1. 1 * length(transcription factor) and evalue of subject ≤ evalue all then subject domains ← InterproScan(subject) if subject domains not in DBD domains and length(subject domains) < length(TF domains) BLAST transcription factors against small protein database for subject in subjects small do and length(intersection(subject domains with TF fomains)) ≥ 1 then putative miPs.
append(subject) end if end if end for subjects small ← putative miPs. append(subject) end if end if end if end for end for return putative miPs Pseudocode 1: Pseudocode of miP3. if length(subject) ≤ 1. 1 * length(transcription factor) and evalue of subject ≤ evalue small then blast results ← BLAST subject against transcription factors database if transcription factor in blast results and evalue of transcription factor ≤ evalue reblast then subject domains ← InterproScan(subject) if subject domains not in DBD domains and length(subject domains) < length(TF domains) and length(intersection(subject domains with TF fomains)) ≥ 1 then.
Lot of efforts have been devoted by ATLAS and CMS teams to improve the quality of LHC events analysis with the Matrix Element Method (MEM). Up to now, very few implementations try to face up the huge computing resources required by this method. We propose here a highly parallel version, combining MPI and OpenCL, which makes the MEM exploitation reachable for the whole CMS datasets with a moderate cost. In the article, we describe the status of two software projects under development, one focused on physics and one focused on computing. We also showcase their preliminary performance obtained with classical multi-core processors, CUDA accelerators and MIC co-processors. This let us extrapolate that with the help of 6 high-end accelerators, we should be able to reprocess the whole LHC run 1 within 10 days, and that we have a satisfying metric for the upcoming run 2.
Computing aspects For each event, one must compute a multi-dimensional integral with a Monte Carlo approach  [3]. In our use-case, the integral has been simplified down to 5 dimensions, and one needs at least 20k points for its Monte-Carlo evaluation. Several different software packages were used as a starting point for our software system: • For the parton distribution function Ps: LHAPDF [4]  [5]. • For the matrix element M Ω : the code generated with MadGraph [6]  [7]. • The transfer function W is specific to the studied channel and the detector features. It requires a custom-made development. • For the Monte-Carlo evaluation of the integral: the GSL  [8]  implementation of the VEGAS  [9] [10]  [11]  algorithm, as provided by ROOT  [12]. The complete run 1 dataset, collected between 2011 and 2012 at a center-of-mass energy of 7-8 TeV, consists of about 200k events, which must be processed twice: once to evaluate the probability Ps assuming that the event is a signal one (H➝ττ), once to evaluate the probability Pb assuming that the event comes from background.
Two lines of development We pursue two developments in parallel, one focused on physics and one focused on parallel programming. Of course, the ultimate goal is to merge them into a single software system. The MEM-MPI pre-production code contains all the physics for the channel H➝ττ, fully interfaced with CMS software and data, but only MPI instrumentation. It is driven by physicists. For this preproduction code, MPI is currently used both to connect multiples nodes, and also to use all the cores within a node (until OpenCL is ready). The MEM-MPI-OCL prototype contains all the machinery to opportunely accumulate any processing hardware available, combining MPI and OpenCL, but only a subset of the physics has been ported to OpenCL today. It is driven by engineers of the team.
The MEM-MPI first results The results of this code have been validated by the physicists of the team (see figure  2 ), and it is under certification with CMS official simulated and real data. For what concerns the computing time, we tried to use an increasing number of simultaneous MPI processes, each time processing a global number of events equal to three times the number of processes, so that each process analyses three events. The Monte Carlo evaluation of the integral was done with 20k points. When less than 16 MPI processes are considered, they are submitted on the cores of a single node. Beyond 16, the processes are distributed over several nodes. As one can see in the figure  3 , we have observed that the performance scales very well from 1 to 16 cores within a single node, and until 4x16 cores with 4 full nodes.
Porting status The major task in this work is the translation to OpenCL of all the code involved (~30k lines of code). This translation is generally going through two steps : 1. From C++ and Fortran to C99. 2. From C99 to OpenCL. The translation status of each big subset of the code is the following: • VEGAS integration, ~2k lines of code: done • LHAPDF, ~1. 6k lines of code: we only lack the final numerical validation. • MadGraph code generation, ~20k lines of code: the first step of translation towards C99 is done, but we still do the second by hand, and it is our priority to make it automatic. Although our priority is yet to complete the OpenCL translation, we could not refrain from running a few preliminary benchmarks with a single of MadGraph matrix element process (uu->h->uu tau tau).
Conclusion and future work In order to tackle the prohibitive consuming of CPU time, we built a MEM application in two steps. The first one is a MEM parallel implementation already used in production mode which can be deployed, thanks to MPI standard, everywhere from local computing clusters to High Performance Computing centers. It offers the whole processing of a set of events in a single job. In addition, mixing the MPI and OpenCL standards, we intend to use GPUs at large scales. In this way, we built an OpenCL preliminary version of MEM, which takes only the most CPU intensive part of the code into account: the integration computation of the matrix elements with the MadGraph generated code for only one sub-process: uu->h->uu tau tau. The first results are very promising. Firstly, on CPUs (without GPUs), the OpenCL implementation offers, thanks to the OpenCL abstract model and the SIMD instructions generation by modern OpenCL compilers, a speed-up greater than 3.
Acknowledgements This work has been funded by the  P2IO LabEx  ( ANR-10-LABX-0038 ) in the framework « Investissements d' Avenir »  ( ANR-11-IDEX-0003-01 ) managed by the  French National Research Agency (ANR). This work has also been partly funded by the grant  ANR-13-JS05-0002-01  ("CHAMPS") managed by the  French National Research Agency (ANR).
Background: Recent advances in the analysis of high-throughput expression data have led to the development of tools that scaled-up their focus from single-gene to gene set level. For example, the popular Gene Set Enrichment Analysis (GSEA) algorithm can detect moderate but coordinated expression changes of groups of presumably related genes between pairs of experimental conditions. This considerably improves extraction of information from high-throughput gene expression data. However, although many gene sets covering a large panel of biological fields are available in public databases, the ability to generate home-made gene sets relevant to one's biological question is crucial but remains a substantial challenge to most biologists lacking statistic or bioinformatic expertise. This is all the more the case when attempting to define a gene set specific of one condition compared to many other ones.
Thus, there is a crucial need for an easy-to-use software for generation of relevant home-made gene sets from complex datasets, their use in GSEA, and the correction of the results when applied to multiple comparisons of many experimental conditions. Result: We developed BubbleGUM (GSEA Unlimited Map), a tool that allows to automatically extract molecular signatures from transcriptomic data and perform exhaustive GSEA with multiple testing correction. One original feature of BubbleGUM notably resides in its capacity to integrate and compare numerous GSEA results into an easy-to-grasp graphical representation. We applied our method to generate transcriptomic fingerprints for murine cell types and to assess their enrichments in human cell types. This analysis allowed us to confirm homologies between mouse and human immunocytes.
Conclusions: BubbleGUM is an open-source software that allows to automatically generate molecular signatures out of complex expression datasets and to assess directly their enrichment by GSEA on independent datasets. Enrichments are displayed in a graphical output that helps interpreting the results. This innovative methodology has recently been used to answer important questions in functional genomics, such as the degree of similarities between microarray datasets from different laboratories or with different experimental models or clinical cohorts. BubbleGUM is executable through an intuitive interface so that both bioinformaticians and biologists can use it. It is available at http://www. ciml.
Several methods have been designed to solve this issue. One of the most commonly used is the Gene Set Enrichment Analysis (GSEA), which allows statistical assessment of coordinated expression changes of a pre-defined set of genes between pairs of phenotypic conditions  [1, 2]. An additional strength of GSEA is that it allows better exploiting the ever increasing knowledge on gene networks and their relationships with biological processes, not only documented contribution to a given biological function as allowed by gene ontology or pathway analyses but also co-expression across a variety of conditions, predicted regulation by a common set of transcription factors, or association with specific diseases as informed by genome wide association studies. Thousands of such gene sets have been carefully curated and regrouped in public databases such as the Molecular Signatures database (MsigDB)  [3, 4]  or the Stanford Microarray Database (SMD)  [5].
Moreover, when GSEA is applied to multiple pairwise comparisons, statistical significance of the enrichments requires to be corrected for multiple testing. Such restriction limits the possibility of interpreting the results in a global context, especially when one deals with many different conditions in a single study, a situation that requires running serially GSEA on all possible pairwise comparisons. Beyond the issues of the time consumed by such large analyses and of the error risk when running many analyses manually, the main difficulty then is to integrate all results together, by inferring "neighbor-to-neighbor" relationships, in order to extract simple and relevant interpretations from the vast amount of enrichments obtained. Moreover, the interpretation of the results being dependent on the biological relevance of the tested gene sets, it is crucial to be able to generate home-made gene sets allowing to rigorously test working hypotheses, for example by being able through the same software suite of extracting relevant, custom gene sets from a first dataset and then of automatically using them for enrichment analysis on a second, independent, dataset.
Generation, integrated interpretation and graphical representation in a simple and intuitive way of multiple GSEA is an innovative methodology that has recently been shown to be critical to help rigorously answer important questions in functional genomics. It allowed to better assess the degree of similarities between complex microarray datasets generated in different laboratories or with different experimental models or clinical cohorts  [7, 17, 22, 23]. It also facilitated comparison of datasets across microarray platforms, including for identifying homologous cell types across species as we have pioneered  [14, [24] [25] [26] [27] [28]. However, this approach has been limited so far to only a few research groups having a dual expertise both in a specific area of biology and in bioinformatics.
The lack of a computational tool allowing to easily and rigorously perform multiple GSEA without minimal knowledge in bioinformatics and statistics has limited the use of this methodology by biologists. To fill this gap, we have developed and present here a stand-alone program freely downloadable and named Bubble GSEA Unlimited Map or BubbleGUM. It encompasses two modules. The first module, named GeneSign, allows automatically extracting the molecular signature associated to sets of specific biological conditions as compared to one another, consisting in the lists of genes more highly expressed in phenotypes of interest as compared to reference phenotypes, out of a microarray-based expression dataset, using various statistical methods. Examples of molecular signatures are the list of genes which are specifically expressed to higher levels in a given cell type, cell state or disease as compared to many other ones.
GeneSign automatically computes molecular signatures and provides the associated heatmaps, through an intuitive easy-to-use interface. The second module allows performing and mining multiple GSEA on all possible pairwise comparisons of an expression dataset. It generates a comprehensive figure called a "BubbleMap", which provides a transversal comparative point of view of all enrichments, in order to gain insight in the interpretation of multiple inter-connected enrichment plots. Interconnection of the comparisons is intrinsic to the fact that one deals with multiple conditions that are compared to each other in a pairwise manner. In other words, if gene set X is enriched in condition B when compared to condition A and in condition C when compared to condition B, thus X is enriched in condition C when compared to condition A.
Hence, this second module displays these neighbor-to-neighbor relationships through an intuitive schematic representation and therefore allows to better interpret multiple pairwise-limited GSEA results by simplifying their visualization. The two modules can be used either separately or as a suite, since BubbleMap can directly use as gene sets the signatures generated with Gene-Sign. Finally, BubbleGUM can also be used more broadly to facilitate multi-Omics analyses, since it basically estimates the degree of correlation between lists of molecules associated with intensity signals of any kind, not only including from mRNA hybridization experiments (microarrays) but also from sequencing assays encompassing epigenetic and RNAseq data as well as mass spectrometry data for proteomics, and it should be applicable to metabolomics.
Murine expression data For mouse immune cell types, our own gene expression dataset was used which included CD8α + cDC (conventional dendritic cells), CD11b + cDC, pDC (plasmacytoïd dendritic cells), B cells, NK (Natural Killer) cells and CD8 + T cells, all purified from steady state mouse spleen  [29]. The hybridization was performed on Affymetrix mouse 430 2. 0 gene chips. Two to three independent replicates were made for each cell type. This dataset was deposited in the Gene Expression Omnibus (GEO) database under reference number GSE9810  [32]. Quality control of the array hybridization was performed through Bioconductor (2. 14)  [33]  in the R statistical environment (version 3. 1. 0) using the affyPLM package. The raw data was normalized using the RMA (Robust Multichip Average) algorithm using the affy package  [34].
Human expression data For human immune cell types, a gene expression dataset was compiled from different public sources as previously described  [29] , in order to include all the cell types known or proposed to be homolog to the mouse cell types under study, namely CD141 + cDC, CD1c + cDC, pDC, B cells, NK cells and CD8 + T cells, as well as neutrophils as a negative control. These data can be retrieved from ArrayExpress (accession number E-TABM-34 for the DC data) and GEO (accession number GSE72642 for the other cells)  [32, 35]. The hybridization was performed on Affymetrix Human Genome U133 Plus 2. 0 gene chips. Quality control of the array data was performed through Bioconductor  (2. 14)  in the R statistical environment (version 3. 1. 0) using the affyPLM package  [33]. The raw data was normalized using the RMA algorithm using the oligo package  [34].
Implementation BubbleGUM is a stand-alone program developed in Java 1. 6. The two modules of BubbleGUM, GeneSign and BubbleMap, can be used either in an independent manner or as a workflow of analyses to assess the enrichment of home-made gene sets and not only of publicly available gene sets. BubbleMap implements the original GSEA algorithm. The file formats used by BubbleGUM and GSEA are fully compatible.
GeneSign: Generating phenotype signatures from a microarray-based expression dataset Starting from a pre-processed normalized expression dataset coming from any type of microarray platform, the user can automatically extract the molecular signatures of samples (cell populations, treatments, pheno-types…) of interest as compared to reference samples, using various statistics (see Additional file 1). In this context, a molecular signature is defined as the list of genes that are more highly expressed in the samples of interest (test samples) as compared to the reference samples, according to user's defined criteria such as the fold change and/or the FDR when applicable (see Additional file 1). GeneSign can extract absolute signatures, using as reference populations all cell samples but the ones for which the signature is computed.
Alternatively, GeneSign can extract relative signatures, using as reference populations a set of samples selected by the user. This is interesting when one wants to characterize a population as compared to another one, or when the absolute signature of a population of interest is empty because no gene is specific of the population of interest when compared to all the other populations. In this situation, one can still characterize the population of interest by extracting the genes more highly expressed in that population when compared to a subset of the remaining populations. GeneSign allows generating transcriptomic signatures by using various statistical methods. The "Min(test) vs Max(ref )" method is applicable to datasets with low replicate numbers, and fast to compute because no permutation procedure is necessary.
BubbleMap: Performing multiple GSEA and displaying enrichment patterns BubbleMap performs GSEA on all possible pairwise comparisons in an expression dataset of interest with the gene sets uploaded by the user, either generated by GeneSign or downloaded from public databases such as MsigDB  [3, 4]  or SMD  [5]. The results are displayed as a figure with colored bubbles of various sizes and color intensities (BubbleMap) (Fig. 1 ). Each bubble is a GSEA result and summarizes the information from the corresponding enrichment plot. The color of the bubble corresponds to the condition from the pairwise comparison in which the gene set is enriched. The bubble area is proportional to the normalized enrichment score (NES) calculated by GSEA. The intensity of the color corresponds to the statistical significance of the enrichment, calculated through the computation of a permutation-based p-value that is corrected for multiple testing across the various pairwise comparisons.
Results and discussion We illustrate the functioning of BubbleGUM in a workflow successively using GeneSign and BubbleMap to seek for homologies between mouse and human Fig. 1  Integrating multiple GSEA into a BubbleMap. GSEA output is a bar code (1) corresponding to the projection of the gene set on the blue-to-red gradient representing all the genes of the chip ranked from high expression in the population on the left to high expression in the population on the right. The more the gene set is regulated, the more the bar code is shifted to one side. This is measured by two parameters. The normalized enrichment score (NES) integrates the number and differential expression intensity of the assessed genes. NES > 0: the gene set is enriched in the population on the left. NES < 0: it is enriched in the population on the right.
The FDR is the likelihood that the gene set enrichment represents a false-positive finding  (2). Here, the FDR is different from the one calculated by GSEA, because it is corrected for multiple testing across the gene sets and across the pairwise comparisons, thus allowing the user to compare all the bubbles in a BubbleMap. Each enrichment is summarized as a bubble, bigger and darker as the enrichment was stronger and more significant, in a color matching the population in which the gene set was enriched (3). Final output is a BubbleMap (4) with legend (5). Gene sets are either generated by GeneSign or retrieved from public gene set databases  (6)  immune cell types based on their sharing of specific molecular signatures/transcriptomic fingerprints  [29, 40]. In brief, we use GeneSign to identify the transcriptomic fingerprints of murine immune cell types and we subsequently use these home-made gene sets in BubbleMap to assess their enrichments in human immune cell types (see the online User guide for a detailed step-by-step procedure of how to repeat this data analysis workflow).
Generation of transcriptomic fingerprints specific for murine immune cell types In the proposed case study, we use a public microarray gene expression dataset for immune cell types purified from mouse spleen and encompassing CD8α + conventional dendritic cells (cDC), CD11b + cDC, plasmacytoid DC (pDC), B cells, NK cells and CD8 + T cells  [29]. After having normalized this dataset, we used GeneSign to extract the absolute transcriptomic fingerprints of each of the murine immune cell types. We generated 3 different absolute fingerprints for each murine immune cell type: one corresponding to the "Min(test) vs Max(ref) > 1. 5x" method, one based on the "Mean(test) vs Mean(ref) > 2x" method with FDR < 0. 05 and one based on the "Minimal (Pairwise [Mean(test) vs Mean(ref)]) > 2x" method with FDR < 0.
In the murine CD8α + c DC fingerprints, we found, among the most significant, genes involved in the functions of CD8α + c DC such as XCR1, TLR3, CXCL9 and CADM1 already reported as being specific of CD8α + c DC  [29]  (Additional file 2). These results thus show that our statistical methods can reliably extract transcriptomic signatures out of an expression dataset. Assessing enrichments of the murine immune cell type-specific transcriptomic fingerprints on expression data from human immune cell types We applied BubbleMap to test the enrichments of the transcriptomic fingerprints of murine immune cell types (Additional file 2) on expression data from human immune cell types encompassing CD8 + T cells, B cells, NK cells, neutrophils, CD1c(BDCA1) + cDC, CD141(BDCA3) + cDC and pDC (see Materials and Methods).