dataesr/scientific-paragraphs-categorization huggingface dataset:
They were further ranked according to their expression level, presence of splice junctions and existence of matched expressed sequence tags (EST). In total, we retained 168,163 TUs with abovethreshold expression of 0. 2 quartile of mRNA expression (Class 2) and, within this group, the most robust 2,972 TUs with at least one splice junction and one EST (Class 1) (Fig. 3B ). Globally, newly detected transcripts were as much expressed as GENCODE annotated lncRNAs but lower than mRNAs (Fig. S2A ). Only 0. 33% of Class 1 lncRNAs   1 (  1 ) 165 (  157 ) 14 (  9 ) 12 (  4 ) 2 (2) ctg_23999 (P22) embedded into a novel HoLdUp assembled Class 1 TU antisense to the protein-coding FBXL7 gene (Fig. 3D ). In conclusion, the reference-based assembly protocol HoLdUp is complementary to DEkupl and allows attributing short RNA subsequences to whole transcription units.
However, important discrepancies were observed between expression counts in poly(A)+ RNA-seq TCGA datasets and NanoString or total RNA-seq PAIR datasets. First, P22 (ctg_119680) was detected as DE in TCGA-PRAD, but failed the DE test when measured by NanoString (Fig. 4 , Fig. S5 ). Second, the expression of nine DE-kupl contigs were near the base line in the TCGA dataset, including those showing relatively high expression and low p-values in the PAIR cohort, such as P14 (ctg_61528) antisense to TPO or the intergenic P9 (ctg_9446). Detection of these contigs in TCGA-PRAD was compromised independently of their genomic location (intergenic or antisense) or of the expression level of a sense-paired gene. We hypothesized that it is most likely due to a relatively low RNA-seq coverage and/or to a loss of poorly or non-polyadenylated transcripts during cDNA library preparation in the TCGA experimental setup.
RNA extraction, quantification and cDNA library production Total RNA was extracted using the TRizol reagent (ThermoFisher), according to manufacturer's procedure, quantified and quality controlled using a 2100 Bioanalyzer (Agilent). RNA samples with RNA Integrity Number (RIN) above 6 were depleted for ribosomal RNA and converted into cDNA library using a TruSeq Stranded Total Library Preparation kit (Illumina). cDNA libraries were normalized using an Illumina Duplexspecific Nuclease (DSN) protocol prior to a paired-end sequencing on HiSeq™ 2500 (Illumina). At least 20x coverage per sample was considered as minimum of unique sequences for further data analysis.
RNA-sequencing data Raw paired-end strand-specific RNA-seq data was generated by our laboratory from ribo-depleted total RNA samples of prostate tissues (8 normal and 16 tumor specimens; Supplemental Table  S1 ) and can be retrieved from the gene omnibus portal (GEO), accession number GSE115414. The human genome version hg19 and the GENCODE v14 annotation were used in this study. First, we performed a quality control of all sequencing data by FastQC Babraham Bioinformatics software. Reads were mapped using TopHat 2. 0. 4, allowing 3 mismatches and requesting uniquely mapped reads which were further assembled using the BedTools suite. Overlapping contigs from all libraries were merged and only contigs supported by at least 10 reads in either library were further assembled in segments if mapped in the same strand and separated by less than 100 nucleotides.
We compared segments to the GENCODE v14 annotation to extract antisense and intergenic TU longer than 200 nucleotides. To classify lncRNAs, we applied the following criteria: (i) an expression level above 0. 2 quartile of mRNA expression in at least one condition per tissue (Class 2); (ii) within this class, all TUs containing at least one TopHatidentified exon-exon junction and at least one spliced EST from UCSC mapped contigs were assigned to Class 1. The whole catalog, the R code and Data Tables can be provided upon request.
Differential expression analysis Read counting was performed on the compiled annotation (GENCODE v26, HoLdUp Class 1 and Class 2) for each sample, using featureCounts 1. 6. 0 with the following parameters: -F "SAF" -p -s 2 -O and the DESeq R package  (Love et al. 2014 ). Only RNAs with adjusted p-value below 0. 01 were retained as differentially expressed to constitute the prostate tumor signature.
NanoString nCounter Expression Assay 100 ng of total RNA was used for direct digital detection of 29 target transcripts: 6 housekeeping genes (RPL11, GAPDH, NOL7, GPATCH3, ZNF2 and ZNF346), 23 contigs and the one known PCa-associated lncRNA, PCA3. Each target gene of interest was detected in RNA samples of 144 specimens (9 normal and 135 tumor) of the PAIR cohort (Supplemental Table  S3 ) on NanoString nCounter V2 using reporter and capture probes of 35-to 50-nucleotide targeting sequences. Data was normalized through the use of NanoString's intrinsic positive controls and then contig expression was calculated relative to the average signal of three housekeeping genes (GPATCH3, ZNF2 and ZNF346). Raw and normalized data for each specimen, mean and fold change expression in normal against tumor samples are presented in Supplemental Table  S4  and S5.
Contig expression measurements in TCGA-PRAD datasets DE-kupl provides representative k-mers for each differentially expressed contig. We converted the TCGA-PRAD FASTQ files to k-mer counts using Jellyfish count and counted representative k-mers in each Jellyfish count file using the Jellyfish query command. Counts were normalized by total number of reads in corresponding libraries. To determine whether counts of DE-kupl derived representative k-mer were a reliable proxy for evaluating contig expression, we compared representative k-mer counts to average counts from k-mers sampled along each contig. All individual counts were RNA-seq reads profiling along a locus of interest was performed using our in-house R script VING  (Descrimes et al. 2015). The normal samples were assigned to the group "controls" and the tumor specimensto the group "cases", with the assumption that the "cases" should have higher values than "controls".
Variable selection using the LASSO penalized logistic regression and external validation of signatures Signature inference was performed in R using the normalized Selection Set (23 probes in 144 observations) as a variable selection dataset and contigs counts table of the Validation Set (23 probes in 557 observations) as an external validation dataset (R Core Team). First, we performed penalized logistic regression using the glmnet R package to select probes predicting the tumor status on the Selection Set upsampled to correct the imbalance class distribution (9 normal versus 135 tumor specimens)  (Friedman et al. 2010). Selection was performed using all probes (signature_mixed including PCA3) or using only new-lnc RNA contigs only (signature_new-lnc) (Fig. S7 ). Second, we built predictors using the boosted logistic regression from the caTools and caret packages  (Tuszynski 2008) ,  (Kuhn 2008).
lncRNAs, but using proper masking and filtering criteria defined by the investigator, other variant transcripts including single nucleotide variations (SNV), novel splice events, gene fusion, circular RNAs or exogenous viral RNAs could be probed. The workflow can be applied to any RNA-seq datasets of any clinical origin to generate a probe panel that may be implemented as a multiplex platform for simultaneous detection of RNAs in clinical samples. Moreover, different experimental contrasts (normal vs. pathology, low-vs. high-risk grade, treatment resistant vs. sensitive, etc. ) will define the biomarker usage in diagnosis, prognosis or other clinical applications, hence, providing clinicians and researchers with a simple and highly sensitive tool for genomic and personalized medicine.
FUNDING Constitution of the cancer prostate cohort received financial support through a grant from the  INCa-Ligue-ARC PAIR program  (to Y. A. and A. L-V. ). RNA-seq efforts were supported by a grant from the  ICGex program at Institut Curie  (to A. L-V. , A. M. ) and benefited from the facilities and expertise of the  NGS platform of Institut Curie , supported by  Agence Nationale de la Recherche  ( ANR-10-EQPX-03 ,  ANR10-INBS-09-08 ) and  Canceropôle Ile-de-France. M. D. , Ma. G. , A. M. , M. P. , and Z. S. were supported by  Agence Nationale de la Recherche  ( DNA-Life ) and the  European Research Council  (ERCconsolidator  DARK-616180-ERC-2014 ) attributed to A. M. ; D. G. and N. H. N. were supported by  ITMO Cancer -Systems Biology  ( bio2014-04 ) and  Agence Nationale de la Recherche  " France Génomique " ( ANR-10-INBS-0009 ) attributed to D.
Computational workflow for reference-based ab initio transcripts assembly from total stranded RNA-seq dataset (HoLdUP) Computational workflow for k-mer contigs discovery from total stranded RNA-seq dataset DE-kupl run was performed from (June 2017) with parameters ctg_length 31, min_recurrence 6, min_recurrence_abundance 5, pvalue_threshold 0. 05, lib_type stranded, diff_method DESeq2. K-mer masking was performed against the GENCODE v24 annotation. DE-kupl analysis of the 8 against 16 PAIR RNA-seq prostate libraries yielded 124,809 DE contigs, in total. Contigs were annotated by alignment on the hg19 human genome assembly using the DE-kupl annotate procedure. We further selected contigs of size above 200 nucleotides and classified them into four categories (contiguous, repeat, spliced, unmapped) based on their location and mapping features.
TCGA prostate cancer poly(A)-selected RNA-seq and corresponding clinical data were obtained from publicly available TCGA dataset (http://cancergenome. nih. gov), 557 inputs in total (52 normal and 505 tumors of high-(N=240), intermediate-(N=128) and low-risk (N=132) groups. Among them, 369 patients showed no tumor recurrence, 108 presented a new tumor event (Supplemental Table S7).
Additionally, as successfully demonstrated in antiretroviral therapy  (13) , targeting host instead of viral determinants may confer a broad-spectrum antiviral efficacy, and also reduce the risk of emergence of drug resistance against influenza viruses  (14). As a result, the last decade has witnessed several host-directed experimental approaches against influenza infections, notably nitazoxanide, DAS181 or acetylsalicylic acid  (15) (16) (17). In line with this emerging trend, we previously postulated that host global gene expression profiling can be considered as a "fingerprint" or signature of any specific cell state, including during infection or drug treatment, and hypothesized that the screening of databases for compounds that counteract virogenomic signatures could enable rapid identification of effective antivirals  (18).
Based on this previous proof-of-concept obtained from in vitro gene expression profiles, we further improved our strategy by analyzing paired upper respiratory tract clinical samples collected during the acute infection and after recovery from a cohort of influenza A(H1N1)pdm09infected patients and determined their respective transcriptomic signatures. We then performed an in silico drug screening using Connectivity Map (CMAP), the Broad Institute's publicly available database of more than 7,000 drug-associated gene expression profiles  (19, 20) , and identified a list of candidate bioactive molecules with signatures anti-correlated with those of the patient's acute infection state (Figure  1A ). The potential antiviral properties of selected FDA-approved molecules were firstly validated in vitro, and the most effective compounds were further compared to oseltamivir for the treatment of influenza A(H1N1)pdm09 virus infections in both C57BL/6 mice and 3D reconstituted human airway epithelia.
Clinical Samples A previously published randomized clinical trial (ClinicalTrials. gov identifier NCT00830323) was conducted in Lyon and Paris (France) during the peak circulation of the influenza A(H1N1)pdm09 virus, with the aim to assess the efficacy of oseltamivir-zanamivir combination therapy compared with oseltamivir monotherapy  (21). Briefly, patients tested positive for influenza A infection by the QuickVue rapid antigen kit (Quidel) were randomized in one of the two treatment groups and nasal wash specimens were collected within 2 h of the first visit and every 24 h until 96 h after treatment initiation. Nasal swabs were also performed on days 5 and 7. In voluntary patients, an optional supplementary nasal wash was performed at least 3 months after influenza infection (recovery phase).
Sample Processing, RNA Preparation and Hybridization Nasal wash samples were collected in RNAlater R Stabilization Solution (Thermo Fisher Scientific). Total RNA was extracted using RNeasy Micro kit (Qiagen) following the manufacturer's instructions. RNA quality was assessed using a Bioanalyzer2100 (Agilent technologies, Inc, Palo Alto, CA, USA). To account for samples having low amount and/or partially degraded RNA (RNA Integrity Numbers between 1 and 8), we applied two types of corrections: (i) cRNA labeling was performed after a linear amplification protocol, as previously described (  22 ) and (ii) raw signals obtained after hybridization of labeled cRNA on microarray and data acquisition were processed using the MAXRS algorithm  (23). Labeled cRNA were hybridized on Affymetrix HG-U133plus2 microarrays according to manufacturer's instructions in a GeneChip R Hybridization Oven 640 (Affymetrix) and microarrays were subsequently scanned in an Affymetrix 3000 7G scanner.
Data Normalization and MAXRS Computational Analysis The MAXRS algorithm  (23)  is particularly suited to gene expression analysis under low hybridization conditions. Briefly, this method takes advantage of the specific design of Affymetrix probe sets, which are composed of an average of 11 different probes that target the same locus, and is based on the observation that for most of the probe sets the same probe shows the highest fluorescence intensity in almost all arrays. For each microarray (m = 1. M) and for each probeset (t = 1. T), fluorescence intensity values on microarray m of all probes (p = 1. P t ) belonging to the probeset t are sorted in increasing order. These ranks are denoted as r mtp. Then, we calculated across all microarrays the rank sum (RS tp ) for each probeset t for each probe p belonging to the probeset t.
Finally, for each probeset t, we kept the three probes p with the highest RS tp. The mean intensity of these three probes is attributed to the probeset t. As it is common practice with many modern preprocessing algorithms, and because of the low global fluorescence signal intensity, mismatched probes were excluded from MAXRS analysis. After pre-processing the raw dataset with the MAXRS algorithm, a normalization step was performed using Tukey median-polish algorithm  (24). Differential expression was assessed by applying a Student t-test for each probeset, and multiple testing was corrected using the Benjamini-Hochberg algorithm in the qvalue library  (25). For further downstream analysis, genes were selected according to two criteria: (i) absolute fold change >2, and (ii) corrected p-value < 0.
Regardless of treatment initiation time, all treatments were performed per os (150-µl gavage) once daily for 5 consecutive days (5 drug administrations in total). Mortality, body weight and clinical signs such as lethargy and ruffled fur were daily monitored on 10 animals/group for a total of 14 days. Animals were euthanized if they reached the humane endpoint of >20% weight loss. The remaining 5 animals/group were euthanized on day 5 p. i. to measure lung viral titers (LVTs). Vehicle (saline) or oseltamivir were used as placebo and positive treatment control, respectively. The oseltamivir dose (10 mg/kg/day) was adjusted to confer ∼50% protection in the selected experimental conditions and is considered a good correlate of half the normal dose of 150 mg/day given to humans  (31). The doses of repurposed candidate molecules were selected to be in the non-toxic range for mouse studies, according to published preclinical data for their first therapeutic indication.
bioinformatics. babraham. ac. uk/projects/fastqc) was used for quality controls of the raw data. Reads were trimmed using the Trimmomatic (32) software, with a minimum quality threshold of Q30. Trimmed reads were pseudo-aligned to the Homo sapiens genome (GRCh38. p11) using the Kallisto software  (33). Statistical analysis was performed in R3. 3. 1 with the package EdgeR 3. 14. 0 (34). Differential expression was calculated by comparing each condition to the mock using a linear model. The Benjamini-Hochberg procedure was used to control the false discovery rate (FDR). Transcripts with an absolute fold change >2 and a corrected p-value < 0. 05 were considered to be differentially expressed. Enriched pathways and GO terms were assessed with DAVID 6. 8  (27). For visualization purposes, a heatmap and stacked barplots were constructed in R3.
In silico Cross-Analysis of Chemogenomic vs. Virogenomic Clinical Profiles We then performed an in-silico search for molecules that reverse the virogenomic signature of infection, using the CMAP database (Build 02) as previously described  (18). CMAP is a collection of genome-wide transcriptional expression data from cultured human cells treated with bioactive small molecules. HG-U133plus2 probesets were mapped to the U133A probesets using the Ensembl BioMarts online tool  (36, 37) , and connectivity scores and p-values were obtained using the CMAP algorithm  (19, 20). With the global set of 1,000 most differentially expressed genes as input (Figure  1C , Main List), we obtained a preliminary list of 60 candidate compounds. In parallel, we used two other subsets of genes belonging to significantly enriched Gene Ontology (GO) terms obtained from microarray analyses to introduce functional bias and add more biological significance to our first screening.
Hence, by using 6 Biological Process terms (GO_BP) that shared more than 90% of genes (Figure  1C , Functional cross-analysis #1), a second list of 109 compound candidates was obtained. A third list of 19 compounds was obtained using 3 relevant Cellular Component terms (GO_CC) (Figure  1C , Functional cross-analysis #2). The comparison of the 160 compounds from the three distinct lists (12. 2% of compounds of CMAP, Table  S2 ) highlighted monensin as the only common compound (Figure  1D ). To rationally reduce the number of drug candidates, bioactive drugs were excluded if not compatible with a final use as antiviral, mostly for safety (e. g. , teratogens, intercalating agents), and/or pharmacological (e. g. , documented low bioavailability) reasons, based on clinical data and the PubMed/PubChem databases.
Taking the mock-infected / mock-treated ("mock") as baseline, we initially performed DAVID functional gene enrichment (absolute fold change >2, Benjamini-Hochberg corrected p < 0. 05) on the specific transcriptional signature of diltiazem with the objective of gaining insight on the putative host pathways involved in its antiviral effect. The lists of upregulated (n = 194) and down-regulated (n = 110) transcripts in the mock-infected/diltiazem ("mock + diltiazem") condition were analyzed using DAVID 6. 8 to highlight associations with specific GO terms. Although no enriched BP was identified among down-regulated transcripts, the list of up-regulated transcripts associated with diltiazem treatment highlighted 7 particularly enriched BP. While 4 of these BP (GO:0009615; GO:0045071; GO:0051607; GO:0060337) are directly linked to antiviral response/cellular response to virus, the remaining 3 (GO:0055114; GO:0008299; GO:0006695) are involved in cholesterol biosynthesis/metabolism (Figure  6A ).
Nevertheless, and despite strong putative advantages such as the achievement of broad-spectrum antiviral efficacy and the minimization of viral drug resistance, this approach usually fails to overcome two major limiting factors of classic compound screening. Firstly, it remains target-centered per se, therefore leading to the identification of drugs with limited efficacy due to the complex network and high redundancy of the host cellular pathways. Secondly, the need of high-throughput screenings often entails the measurement of a very limited number of viral parameters, usually in non-physiologically and hence poorly relevant conditions and/or cellular models. Based on our initial proof-of-concept study on the in silico screening of the CMAP database  (19, 20)  with no initial a priori on specific host targets  (18) , we moved our approach up to the clinical trial setting, by determining exploitable and more relevant virogenomic profiles directly from standard clinical samples of influenza-infected patients.
This trial (FLUNEXT TRIAL PHRC #15-0442, ClinicalTrials. gov identifier NCT03212716) is currently ongoing. Finally, our study underscores the high value of clinical specimens and the advantages of exploiting virogenomic and chemogenomic data for the successful systematic repurposing of drugs already available in our modern pharmacopeia as new effective antivirals. We propose that our approach targeting respiratory epithelial cells, the principal influenza infected cell type in the lung, could be extended to other respiratory viruses and eventually to other pathogens involved in acute infections. Importantly, drug repurposing presents several financial and regulatory advantages compared to the development of de novo molecules  (5) , which are of particular interest not only in the context of antimicrobial resistance but also against both emerging or recurrent pathogens for which we are still disarmed.
ACKNOWLEDGMENTS The authors want to thank  Jacques Corbeil  and  Frederic Raymond  (Research Center in Infectious Diseases of the  CHU de Quebec and Laval University, Quebec ) for their help and useful advice. This work was funded by grants from the  French Ministry of Social Affairs and Health (DGOS) ,  Institut National de la Santé et de la Recherche Médicale (INSERM) , the  Université Claude Bernard Lyon 1 , the  Région Auvergne Rhône-Alpes  ( CMIRA N • 14007029 and AccueilPro COOPERA N • 15458  grants), and  Canadian Institutes of Health Research  ( N • 229733  and  230187 ). GB is the holder of the Canada Research Chair on influenza and other respiratory viruses. Funding institutions had no participation in the design of the study, collection, analysis and interpretation of data, or in the writing of the manuscript.
In some cases, to improve the matching, they include ontologies or semantic resources that are expected to ameliorate the similarity between documents, like those shown in Senthil  Kumaran & Sankar (2013)  and  Montuschi et al. (2014). The work that is here presented occurs in the following context. It is the outcome of a collaboration project with a Human Resources enterprise that had a large database of recruitment and selection processes conducted by them previously. The database is divided by job postings 4  in which we can find the applications sent by the interested or directly contacted candidates. Each application is composed, at least, of a résumé and the outcome of the selection process. This database, however, has a particular characteristic, for most of the job postings, neither the job offer nor the person specification is available 5.
This characteristic is due to the software used to store automatically the incoming applications did not provide the option to keep these documents. Due to the fact that it is impossible to apply state-of-the-art's methods for all the database, we decided to explore how to rank résumés without making use of job offers. The result of this exploration are innovative and simple methods that use uniquely the proximity between résumés sent for the same job posting. To this end, we use a similarity measure and Relevance Feedback  (Rocchio, 1971)  applied with methods based on a similarity quotient and a vocabulary scoring. Despite in this work, we do not make use of more complex methods, like deep-learning neural networks, or dense text representations, i. e. , word embeddings, the idea of using them was always present.