dataesr/scientific-paragraphs-categorization huggingface dataset:
Empirical reference dataset We generated an empirical reference phylogenetic dataset, adapted from Wong's collection  (Wong et al. , 2008)  consisting of 1502 oneto-one orthologous datasets estimated using seven yeast complete genomes with the phylogeny extending back >100 Ma. Each dataset comes along with seven alternative MSAs and their corresponding PAUP Maximum Likelihood tree. It is therefore unclear which fraction of Wong's datasets can be considered to reflect the Tree of Life (ToL) history. One can nonetheless address this issue by selecting Wong's collection in datasets more likely to be ToL compliant. We did so by selecting the 853 datasets  (Chang et al. , 2014)  for which at least one of the seven aligners used by Wong yields the established yeast ToL (the complete gene list in Supplementary List).
Topology measure based on loss functions Holder et al. modeled the phylogenetic inference problem under a decision theory framework  (Holder et al. , 2008)  and proposed two loss functions accounting for differences between a tree and its reference. We measure discrimination power of bootstrap according to those two loss functions (Fig. 1c ). • All-or-Nothing loss function: All-or-nothing approach would assign a loss of 1 if the inference tree is not identical to the reference tree and a loss of 0 if it is identical to the true tree. Performances were estimated by the Area Under the Curve (AUC) while using bootstrap values as a predicted confidence with the ROCR R package  (Sing et al. , 2005 ). • Per Branch loss function: This function is based on the Robinson-Foulds (RF) metric  (Robinson and Foulds, 1981)  and penalizes both the splits in the true tree that are missing in the target tree and the clade in the target tree that are missing in the reference (detailed information in Supplementary Section 1).
SBOOT outperforms individual aligners We first analyzed the relationship between bootstrap support and alignment uncertainty. To that effect, we used the 1502 Wong's datasets made of seven one-to-one yeast orthologues and binned them by the number of different PAUP Maximum Likelihood tree topologies recovered by the authors. We then used Wong's methodology to estimate a global bootstrap for each tree and boxed-plotted the distribution by topological bin (Supplementary Fig. S1 ). This re-analysis of Wong's data clearly shows an inverse relationship between bootstrap values and aligner's induced tree topological disagreements. In short, datasets on which aligners disagree tend to have lower bootstrap support, even when this bootstrap is estimated on one aligner at a time. This rather intuitive finding indicates the existence of a relationship between MSA instability (i.
e. alternative MSAs yielding different tree topologies) and low bootstrap support. High bootstrap values might not be direct indicators of phylogenetic accuracy. Sometimes, they reflect the homogeneity of the sampling revealed by the MSA. We first tried to quantify the capacity of bootstrap strategies to discriminate between correct and incorrect topologies. For the All-or-Nothing loss function (Supplementary Table  S1 ), we found individual aligners to be very comparable in terms of overall accuracy. They all manage to reconstruct a similar number of trees having the ToL topology, with MAFFT and PRANK being the most accurate method (665 ToLs, true positive:TP) and T-Coffee the least accurate (620). Noticeably, the concatenated methods do not generate more ToLs than individual aligners (661), i.
This improvement comes from the sampling across the different aligners not from sample size (i. e. not from the fact the SBOOT has an increased length with respect to individual aligners). However, the main issue with our SBOOT based bootstrap is the bootstrap value range. While on this dataset, most aligners report bootstrap values in the order of 50% [Supplementary Table  S1 , column 'Ave. bootstrap (%)'], SBOOT results in an average bootstrap of 77. 31%. This high value is an artifact intrinsic to the drawing procedure where columns are drawn from non-independent MSAs. As a consequence, similar columns are very likely to be over-sampled thus inducing artificially high bootstrap support values.
pSBOOT and wpSBOOT with compatible bootstrap range The simplest workaround for this problem is to sample a number of columns similar to the average length of concatenated MSAs, using a partial bootstrapping procedure rather than complete bootstrapping. Originally, it was proposed to unbiasedly estimate confidence level [i. e. r will be seven for N r in the 10th line of the second paragraph, page 51  (Zharkikh and Li, 1995) ]. The resulting method (pSBOOT) has an average bootstrap support similar to the one reported on single aligners. It is slightly less discriminative than the full SBOOT but retains a good AUC and a stronger discriminative power than any single aligner alternative methods, especially when accepting only 10 FPs. Partial bootstrap is not, however, satisfyingly addressing the multiple sampling issue arising when alternative MSAs of the same sequences are concatenated.
Supplementary Table  S2  shows an advanced analysis, Per Branch loss function, to distinguish performances between slightly wrong trees and totally nonsense ones. Interestingly, although MAFFT and PRANK are the most effective method in terms of All-or-Nothing loss function, MAFFT performs better than PRANK in Per Branch loss function (Supplementary Table  S2 , e RF column). Super-MSA alternative sampling methods do not yield the lowest overall RF score, but however improve the discrimination (Supplementary Table  S2 , AUC). For instance, the datasets having a wpSBOOT average score higher than 50% are much more topologically correct that datasets selected at the same cutoff with a standard one aligner bootstrap procedure. The distribution of Type I versus Type II error of different thresholds is shown in Figure  3.
Lower Type I error in simulated datasets This empirical analysis has two main limitations: it depends on an ad-hoc benchmark in which the exact fraction of ToL compliant datasets is an unknown quantity. Secondly, individual datasets of seven taxa are not challenging enough to extrapolate these results to real life phylogenies. For this reason, we decided to complete our analysis with larger simulated datasets of up to 64 taxa. This larger dataset gave us a chance to estimate branch support rather than full tree support. Such an analysis is of more practical relevance since trees are often used on a branch-by-branch basis, using bootstrap support to identify unresolved nodes. Table  2  shows that wpSBOOT is consistently one of the methods returning the largest fraction of topologically correct trees.
Its level of accuracy is comparable to SATe, a state-of-the-art phylogeny aware aligner. The most important observation on this table is certainly the good scaling capacities of wpSBOOT. For instance, when accepting 100 FP nodes, wpSBOOT manages to recover more correct branches than any alternative method tested here. By this criterion and considering the three datasets at four cutoff levels (10, 25, 50 and 100 FPs), wpSBOOT ranks #1 on 7 out of 12 tests and is among the two best (usually with SATe) on 3 out of 12. By these measures wpSBOOT is therefore the most trustworthy bootstrap support evaluation method tested here. Table  3  shows that wpSBOOT is also a consistently one of the methods returning the lowest RF score. When considering subset of highly supported trees, wpSBOOT has not only lower RF but also lower Type I error, meaning less false positive-an important practical property.
Conclusion and discussion In this paper, we describe a novel approach able to incorporate MSA uncertainty within phylogenetic trees bootstrap support values. Our method simply requires concatenating several alternative MSAs of the considered sequences into a Super-MSA. This SBOOT is then used to draw bootstrap replicates. We describe and validate two slight variations around this protocol: a partial sampling meant to avoid re-sampling and another one combining partial sampling with a weighting scheme, to incorporate individual MSAs information content. We validated the discriminative power of this new bootstrap-based confidence measure and found it to be more discriminative than similar measures made on single MSAs. We confirmed this observation on both empirical and simulated datasets. Note: >: number of datasets for which the wpSBOOT bootstrap support values were higher than the average measured on single aligners; ¼: identical values; <: number of datasets where the values are inferior.
Overall, wpSBOOT¼ToL: number of datasets for which the wpSBOOT topologies are identical to the ToL. Overall, wpSBOOT!¼ToL: number of datasets for which the wpSBOOT topologies are different from the ToL. wpBS&aveBS>60: similar measures restricted to datasets having a bootstrap support higher than 60, with both methods. wpBS&aveBS<60: smaller. Note: Aligner: considered aligners. Note that wpSBOOT represents the concatenation of ClustalW, MAFFT, ProbCons, PRANK and SATe MSAs. AveBoot(%): average bootstrap support measured on the simulated dataset. AUC: area under the ROC curve of the corresponding aligner. 10FP: number of nodes defining target split properly recovered when accepting 10 incorrect splits on a list of splits ranked by bootstrap support. 25, 50, 100FP: similar. Total: total number of nodes on the considered datasets (16, 32 and 64 tips lines) or total number of correctly recovered nodes.
The three sections labeled 16, 32 and 64 tips correspond to similar analyses done on datasets of different sizes. Best performance in each column is marked in bold. The simulated datasets also made it possible to show that this approach scales better than standard bootstrap support measures and is useful both at the full topology level and at the split (node) level when estimating phylogenetic trees confidence using maximum likelihood. This methodology is important because it addresses a key problem when doing phylogenetic reconstruction: MSAs are not data, they are models. For a long time, phylogenetic textbooks have all but ignored this reality, with MSAs always appearing as idealized ungapped blocks sprinkled with a few strategic mutations. In practice, however, it is as difficult to estimate an MSA, as it is to estimate an evolutionary tree.
We expect such approaches to be especially fruitful when dealing with nucleic acids MSAs that tend to be less stable than their protein counterparts, especially when no constraint exists on secondary structure conservation. Recently, Lemoine et al. establish a new bootstrap version based on transfer distance, which overcomes the low support problem of the original bootstrap in large scale data  (Lemoine et al. , 2018). Applying our alternative sampling approach on the new bootstrap will be investigated in the further. Ashkenazy et al. found the concatenation of alternative MSAs from GUIDENCE2 can improve tree topology accuracy  (Ashkenazy et al. , 2018). Another further extension is to apply alternative bootstrap on GUIDENCE2 Super-MSA. We expect enriching evolutionary signal not only tree topology but also bootstrap discrimination power.
Overall, the SBOOT should provide a good correction of well-known MSA reconstruction artifacts such as dynamic programing tiebreaks or guide tree influence  (Lo ¨ytynoja and Goldman, 2009). In an ideal world, all alternative MSAs would be used and weighted using some informative posterior probability. A possible model can be the approximately unbiased (AU) test by multi-scale bootstrap technique  (Shimodaira, 2002). pSBOOT is adapted from the idea of Zharkikh and Li's work  (Zharkikh and Li, 1995) , which has been shown similar to AU test  (Shimodaira, 2002). Both ZL and AU tests aim to estimate unbiased P-value by bootstrap values from different scales. Our approach is one special case of multi-scale bootstrap for r 1 ¼ 0. 14 and K ¼ 1. However, we do not use the information of bootstrap values from different scales (K ! 2).
Funding This work was supported by the  Spanish Ministry of Science Plan Nacional  [ BFU2008-00419  to P. D. T. and C. N. ]; the  Wellcome Trust  [ WT095908  to P. F. ]; the  INCEPTION  project  [PIA/ANR-16-CONV-0005  to O. G. ]; the  Taiwan Ministry of Science and Technology  [ 106-2221-E-004-011-MY2  to J. -M. C. ]. We acknowledge support of the  European Molecular Biology Laboratory , the  Spanish Ministry of Economy and Competitiveness , " Centro de Excelencia Severo Ochoa 2013-2017" and "The Human Project from Mind, Brain and Learning" of NCCU  from the Higher Education Sprout Project by the  Ministry of Education in Taiwan.
Results The neuroblastoma lincRNome. To establish the NB lincRNome, we reanalyzed RNA seq data from 497 primary tumors, established by Zhang and colleagues as part of the SEQC study (further referred to as the SEQC dataset)  32, 33. Because the RNA sequencing data was unstranded (i. e. does not contain strand orientation information), we focused our analysis on lncRNAs classified as intergenic (lincRNAs) in Ensembl. This highly curated catalogue contains 7821 lincRNAs, of which 3295 were robustly expressed in NB tumors and hence define the Included data sets and analyses in the study. The SEQC, CCLE and TARGET data sets were used to determine the NB lincRNome. The abundance of the robustly expressed lincRNAs in the lincRNome was used to compare expression between neuroblast and NB samples, primary tumor samples containing CNVs and samples without gains or deletions and NB tumors with and without mutations, amplifications or associations with NB driver genes.
The regulation of lincRNAs correlated with NB driver genes was assessed in cellular perturbation models. To determine the involvement of lincRNAs in modulating the effect or regulation of these driver genes, we made use of a state-of-the-art algorithm called LongHorn. Combined, these analyses allow us to arrive at a core set of predicted NB associated lincRNAs. NB lincRNome (Fig. 2A ). The NB lincRNome was further validated in an independent RNA seq dataset of 148 primary tumors, generated by the TARGET consortium  34  , confirming expression for 3290 of the 3295 lincRNAs (Fig. 2A ). To asses independent transcription of the lncRNAs, we've integrated publicly available CAGE seq (Cap Analysis Gene Expression) data from NB cell lines. Through CAGE seq, the 5′ end of a capped RNA molecule is sequenced, revealing the transcription start site of the transcript.
By integrating this CAGE seq data using the Zipper plot  35  , we uncovered an enrichment of CAGE peaks at lincRNA transcription start sites (TSS), with 255 lincRNAs having a CAGE peak within +/-5 kb of their TSS (Fig. 2B ). CAGE TSS enrichment is less pronounced compared to protein coding mRNAs, likely due to the fact that lincRNAs are less abundant than mRNAs (Fig. 2B ) and public CAGE seq data is filtered based on a minimal expression cutoff  36. Distribution of histone marks is more similar between lincRNAs and mRNAs, with 2933 and 2918 lincRNAs displaying a H3K4me3 or H3K27ac mark within +/-5 kb of their TSS, respectively. Although the majority of lincRNAs is weakly expressed, several are highly abundant. Up to 20% of all lincRNA reads are consumed by only 5 highly abundant lincRNAs in each of the investigated datasets (Fig.
Detailed validation of the samples will be described elsewhere but importantly, well-established neuronal and chromaffin markers including TH, CHGA, BCL2 and HNK1 were expressed in all samples (Fig. 3A ). To further validate the RNA seq data, we analyzed mRNA gene sets that were previously reported to be differentially expressed between neuroblasts and high-risk NB tumors  46. Gene set enrichment analysis demonstrated a strong and significant enrichment of these signatures among up-and downregulated mRNAs between neuroblasts and high-risk NB tumors, supporting the validity of the expression dataset (Fig. 3B ). We identified 2859 lincRNAs expressed in the neuroblast samples (Fig. 3C ). The neuroblast lincRNome largely overlapped the NB lincRNome, with 2638 lincRNAs in common. Differential expression analysis revealed 774 and 912 lincRNAs that were significantly up-and downregulated in high-risk NB tumors compared to neuroblasts (Fig.
1 in the SEQC dataset were considered. LongHorn uncovered 25 and 36 lincRNAs that were predicted to modulate PHOX2B and MYCN activity, respectively (Fig. 7A , Supplemental Table  9 ). Importantly, none of these lincRNAs correlate with MYCN or PHOX2B expression levels directly, excluding the possibility that such correlations may confound the results. LincRNAs predicted to modulate MYCN activity included -amongst others -MIAT, TSIX and MEG3. Of note, 7 lincRNAs were found to modulate both MYCN and PHOX2B activity. Targets that were predicted to be affected by lincRNA modulation with MYCN or PHOX2B as effectors were subsequently evaluated for enrichment of hallmark gene sets. We observed enrichments for, amongst others, the TP53 pathway (8 lincRNAs), STAT signaling (24 lincR-NAs), KRAS signaling (14 lincRNAs) and the apoptosis pathway (9 lincRNAs) (Fig.
Material and Methods Annotation and quantification. The TARGET fastq files were downloaded from the Genomics Data Commons Data portal. Kallisto (v0. 42. 4) was used to quantify gene expression in the samples, using the hg38 human assembly (GRCh38. p10), encompassing 37,297 lincRNA and 180,869 protein coding transcripts. Only long intergenic non-coding RNAs were considered, as RNA seq data for both data sets was unstranded. The transcripts were classified according to the Ensembl biotype annotation (GRCh38. p10). Cancer cell line encyclopedia. RNA sequencing data of the Cancer Cell Line Encyclopedia was reprocessed using Kallisto with the hg38 human genome assembly (GRCh38. p10). using this RNA seq data set to filter out lincRNAs not expressed in NB cell lines (i. e. tumor specific). Uberon and cell types assigned to lincRNAs were downloaded from FANTOM5  3.
RNA sequencing data based mutation analysis. The SEQC and TARGET data set were aligned to the human hg19 assembly, using TopHat (v2. 10). Mutations were identified by means of the Genome Analysis ToolKit (v3. 2-2) using the RNA-seq best practices workflow. Only mutations in protein coding genes deemed damaging or possibly damaging by means of Polyphen and Sift, were retained. Variants with a prevalence of more than 0. 1% according to gnomAD, having a total read-depth below 5 or a read-depth for the mutant allele below 3, were filtered out.
Differential expression analysis. Limma voom (v3. 36. 5) was used to assess differential expression between neuroblast and high-risk neuroblastoma samples, mutated and non-mutated samples for ALK, and amplified and non-amplified samples with INSS stage 4 for MYCN and gain/deletions and wild type samples for the CNVs, for genes expressed in at least half of the SEQC samples. Genes were classified as differentially expressed based on their adjusted p-value (q < 0. 05). For a more robust differential expression analysis for ALK and MYCN differentially expressed genes, the SEQC data set was divided into two subgroups, having an equal number of mutated or amplified samples. Differential expression analysis was performed for both subgroups and genes differentially expressed in both groups were identified (q < 0.
05). This workflow was repeated 100 times, and only genes differentially expressed in more than 80 of the repeats were classified as being truly differentially expressed genes. Differential expression in the model systems was calculated using limma voom, with a threshold of p < 0. 05 (non-adjusted p-value). Gene set enrichment analysis. Gene set enrichment analysis  77  was performed using the Java GSEA application of the Molecular Signatures Database (1000 permutations, classic analysis). To validate differentially expressed genes between high-risk NB samples and neuroblasts, a list of mRNAs was analyzed through pre-ranked GSEA, using gene sets built out of known differentially expressed genes between these two groups of samples  46. GSEA was also performed on mRNA lists resulting from differential expression analysis between MYCN amplified and MYCN single copy tumors (INSS stage 4) and mRNA list from treated and untreated MYCN model systems, using all curated gene sets in the Molecular Signatures Database (Supplemental Figure  11 ).
Clinical information systems (CISs) in some hospitals streamline the data management from data warehouses. These warehouses contain heterogeneous information from all medical specialties that offer patient care services. It is increasingly difficult to manage large volumes of data in a specific clinical context such as quality coding of medical services. The document-based not only SQL (NoSQL) model can provide an accessible, extensive, and robust coding data management framework while maintaining certain flexibility. This paper focuses on the design and implementation of a big data-coding warehouse, and it also defines the rules to convert a conceptual model of coding into a document-oriented logical model. Using that model, we implemented and analyzed a big data-coding warehouse via the MongoDB database and evaluated it using data research mono-and multicriteria and then calculated the precision of our model.
In this context, the establishment of clinical information systems in hospitals can be a key factor in optimizing the coding process of medical information and therefore the expenses of healthcare. The successful establishment of the latter is not done without challenge. In fact, its deployment is based on the data warehouse, which plays an important role in the collection and analysis of large volume of data for decision support. Generally, a data warehouse is often implanted under the relational database management systems (DBMS). The latter obtrudes itself by the richness of their functionality and performance of their request. Nevertheless, they are inadequate to build distributed data warehouses and needful to cope with the scalability of storage space and the increase of hospital stay data  [5].
Methods This section describes the methodology used to design, implement, and evaluate our data model. Generally, there is a need for a semantic data model to define how data will be structured and related in the database  [11] , and it is generally acquired that Unified Modeling Language (UML) meets this requirement  [12]. Therefore, we first used the formalism of UML  [13, 14]  to build a conceptual model describing big data of hospital stays. Then, the corresponding rules were used to convert the conceptual model to NoSQL database. In this paper, we choose to focus on document-oriented NoSQL model, namely, MongoDB. This model developed since 2007 by the company of the same name is considered to be the most efficient in terms of performance, for multi-criteria access queries. Finally, the document-oriented model of the big data warehouse was described in JavaScript Object Notation (JSON) format and implemented in the MongoDB database.
Data source The CIS grouping software for Georges Pompidou European Hospital was used to extract the base of hospital summary report (HSR) from the digestive, oncologic, and orthopedic surgery units. This base of HSR contains a year of hospital stays coded and validated by the department of medical information. Each HSR has medical benefit entities and temporal and administrative patients' data. The entity of medical benefits represented by the medical act and diagnostics appears in three different types of diagnoses: the associated significant diagnosis (ASD), principal diagnosis (PD), and related diagnosis (RD). Moreover, there are various types of acts such as a surgery act and medical technical act. Using Artificial Intelligence and Big Data-Based Documents to Optimize Medical Coding DOI: http://dx.