dataesr/scientific-paragraphs-categorization huggingface dataset:
Database construction and website implementation MyoMiner was constructed in several steps using various tools and processes (Fig. 1 ). We developed an HTML5 website that allows querying and visualizing for the requested gene correlations. The interface was developed using the Bootstrap responsive framework. Scatterplots and correlation networks are visualized with the Nvd3 and D 3  [65]  JavaScript libraries respectively. All Spearman's ρ and p-value pairwise matrices, and metadata are stored on a relational MySQL database which runs under an Apache web server. Dynamic content is processed by the PHP programming language: data retrieval, ρ to Z transformations and CI calculations. The front and back-end is powered by Okeanos  [66]  cloud services. Complete listings of data series IDs and sample numbers are provided in Additional file 2: Table  S7  and Additional file 3: Table  S8.
Query results and features The MyoMiner interface was designed to enable users to search and quickly retrieve the transcriptional coexpression of any expressed gene pair in muscle tissue and cells. All categories are presented as buttons on the main page (Fig. 2a ). When selecting a category, the options that are not relevant to it are deactivated, thereby constraining the search to (and indicating to the user) only those options which remain available. MyoMiner supports queries using HGNC gene symbols (e. g. DYSF), Ensembl IDs (e. g. ENSG00000135636), Entrez gene IDs (e. g. 8291) and Uniprot accession numbers (e. g. O75923). The table output retrieves the correlation values for all expressed gene pairs in the selected category (Fig. 2b ) sorted by ρ-value. The first column comprises the paired gene symbols which can also be clicked to search for its list of correlated genes.
The link threshold bar can be used to remove edges below a certain correlation value, creating sub networks in the process. The blue colored node is used to point the queried gene, the light blue depicts the 1st shell connected nodes and orange the 2nd shell nodes. Users can pan and zoom by clickdragging on an empty space of the interactive network area and using the mouse wheel, respectively. The nodes are interactive and can be moved to any space of the network area. Users can also double-click a node to highlight its immediate connected nodes. Since correlation networks can grow quite large, including thousands of nodes and many more edges, it could take several minutes to retrieve the values for large networks from the database. For this reason, we decided that network construction will be a client side task, using the D3 Java-Script library.
Before correction, no strong correlation is observed between DYSF and SYNPO2L: ρ = -0. 05 (Table  2 , also shown with Pearson's correlation coefficients). Clustering and PCA plots show that the samples are grouped by series, which may indicate bias (Fig. 3a, b left ). The DYSF and SYNPO2L gene expression scatterplot reveal the extent of the batch effect: even though individual series (different colors) have clear positive correlation the overall correlation is canceled out when combined (Fig. 3c ). In detail, the selected category is comprised of three series. Individual series Spearman correlation is GSE47881 ρ = 0. 6, GSE48278 ρ = 0. 3 and GSE28422 ρ = 0. 67. We can also average the correlation values using ρto-Z Fisher's transformation (Additional file 1: Table  S6 , Eq. S1) to convert the non additive ρ-values to Z scores, then average the Z scores and finally convert the mean Z back to ρ-value (Additional file 1: Table  S6 , Eq.
S4). DYSF-SYNPO2L average ρ-value for the category is 0. 54. After we treated the samples with ComBat which reduced the aforementioned bias (Fig. 3  a, b, c right) the correlation value increased to 0. 62 which could indicate a possible functional association between DYSF and SYNPO2L  [72]. In another example between DYSF and Synaptopodin (SYNPO), which may be modulating actin-based shape and mobility of dendritic spines, we find that batch effect correction reduces the bias-inflated correlation ρ = 0. 62. 3  Example of batch effects treatment. The adult human quadriceps resistance exercise category is constructed from three series: GSE47881 (olive green), GSE28422 (pink) and GSE48278 (turquoise) that include 45 samples in total. GSE9103 (magenta) series, from sedentary individuals, is used as a visual control.
c The expression values of DYSF and SYNPO2L are grouped by series resulting in a correlation value r = -0. 05. After batch correction the samples are mixed with ρ = 0. 62. d Inversely, in the example of DYSF and SYNPO where the ρ value is artificially high, before the treatment (ρ = 0. 62), the correction reduces it to ρ = 0. 36 Individual series correlation is as follows: GSE47881 ρ = 0. 31, GSE48278 ρ = -0. 4 and GSE28422 ρ = 0. 64. The scatterplot also reveals that the series have mixed correlations (Fig. 3d  left) and the overall ρ is biased when we combined the series. The average correlation of the three series is 0. 21. After removing the bias (Fig. 3d  right) the correlation is reduced from 0. 62 to 0. 36. Gene pairs that had reduced correlation after batch treatment were more common, suggesting that batch correction reduced the number of false positive correlations.
Validation of correlation values We sought to validate the correlation values generated for MyoMiner, by comparison to two existing databases of gene co-expression (MEM and SEEK), and to the GTEx RNA-Seq data compendium. We did this to the extent possible given the limited muscle-specificity and lack of muscle condition sub-categorization of those databases. For this comparison, a panel of 20 muscle-relevant genes (Additional file 4: Table  S9 ) were chosen based on muscle-relevant annotations (Entrez Gene, GeneCards), and on their frequency of representation in consensus lists of the Muscle Gene Sets collection  [32]. All 190 pair-wise Pearson correlation values were obtained from MyoMiner for these 20 muscle-relevant genes from MyoMiner's healthy human whole muscle category (Human|Both gen-ders|All Ages|Skeletal muscle|Normal).
The MyoMiner values were compared against similar correlation values for the same pairs of genes given by MEM and SEEK for the closest relevant datasets that we could identify in the MEM and SEEK databases. Pearson correlation could be obtained directly from SEEK, whereas MEM returns a p-value for the strength of correlation that is not directly comparable to Pearson, so for MEM we ranked the 190 pair-wise correlations and compared rankings between the two tools. A text search for "Skeletal Muscle" in the MEM tool enabled extraction of correlation values for a dataset that combined 25 muscle-relevant studies on the Affymetrix HG U133 Plus 2. 0 array. For SEEK, the 'Muscle (Non-cancer)' dataset was chosen, which combines 87 mostly musclerelated data series from several gene expression platforms.
Conclusions In this work we retrieved and analyzed striated muscle pertinent microarray samples and combined them effectively for the construction of a muscle-tissue-specific coexpression database. MyoMiner provides a simple, effective and easy way to identify co-expressed gene pairs under a vast number of experimental conditions. This was not available in any existing co-expression database. Thus, MyoMiner represents a powerful tool for muscle researchers, helping them to delineate gene function and key regulators. For MyoMiner we chose to use the Spearman correlation coefficient, despite the fact that Pearson correlation seems to be more popular in other correlation databases. We did not use the Pearson correlation because it is sensitive to outliers and because of the assumptions that need to be met, in order to calculate adjusted p-values: every gene would have to be normally Table  2  Examples of gene pairs correlation changes after batch correction.
Following batch correction this value is reduced distributed, while gene pairs have to be normally distributed. On the other hand, Spearman correlation is robust to outliers and does not require assumptions of linearity. To determine the strength of the correlation we have provided the adjusted p-value and the confidence intervals. It is noteworthy that the most correlated partners for a driver gene may vary significantly between coexpression databases. This could be attributed to different transcriptomic platforms, although most of these databases use GPL570 and GPL1261 platforms as we did. Moreover, different pre-processing methods, batch effect correction methods or the lack thereof, tissue-and cellspecific expression, variable cell states, different correlation coefficients, and other factors, add to the differences found between co-expression databases.
An investigation of the inconsistencies between coexpression databases could identify common gene characteristics or the key factors that contribute to those differences. Our comparison of MyoMiner to the MEM and SEEK databases suggests that there is reasonable agreement of these resources in terms of co-expressed gene pairs in healthy muscle, validating the approach used in MyoMiner, and supporting the trustworthiness of MyoMiner's correlation values for muscle diseases and other muscle conditions. One caveat of gene expression correlation is that it can be driven by other factors. For example, a transcription factor (TF), when upregulated, drives the expression of gene X and Y. In this scenario, TF with X and TF with Y will be highly correlated. However, X and Y will be highly correlated as well, since both are upregulated from the same TF.
88) and MEM (ρ = 0. 74), which use mainly microarray data, compared with the moderate agreement with correlation values obtained from the GTEx RNA-Seq dataset (r = 0. 66). For these reasons, it may be useful in future work to cross-reference common co-expression patterns found in MyoMiner against those found in RNA-Seq data in order to identify whether any consistent disparities are present that may be resulting from technical biases in the microarray chipsuch instances could then be filtered out of MyoMiner findings for specific experimental categories. Published RNA-Seq datasets are becoming more numerous in the neuromuscular field but remain limited in number especially when considering specific experimental or pathological conditions. A major reason for creating MyoMiner is to be able to compare gene co-expression networks between conditionsi.
e. to identify cases were the co-expression of a given gene pair is lost, gained, or inverted, as a result of, for example, a pathological genetic mutation or an environmental change. For individual gene pairs, this is currently facilitated by the MyoMiner web interface, and the MyoMiner database itself opens up the possibility of a systematic analysis in the future. Clearly, such analysis is not possible without measuring co-expression separately in the conditions that are to be compared. However, a caveat of this approach is that a highly specific phenotype might lack biological variation to an extent that gene co-expression becomes overly noisy. Our comparisons to healthy skeletal muscle sub-sets of the SEEK and MEM databases suggest that there is consistency of identified co-expressed gene pairs for this biological condition, indicating that noise does not dominate, and boding well for other conditions.
However, care should be taken and high confidence p-values should be sought when comparing between very precise biological conditions in MyoMiner, especially for murine samples, in which inter-individual variation could be relatively minor. Finally, it could be interesting in future iterations of MyoMiner to examine the relationship of gene coexpression with other types of gene and protein functional association (e. g. [82] [83] [84] ) especially for the identification of functional modules  [85]. The MyoMiner database is a powerful tool for muscle researchers to investigate gene function, based on tissue specific co-expression, and new disease mechanics, based on changes in co-expression between normal and pathological tissues. in GTEx and MEM, for healthy human whole muscle tissue. Table  S1.
Availability of data and materials The transcriptomic data that support the findings of this study are available from ArrayExpress, https://www. ebi. ac. uk/arrayexpress/, and the Gene Expression Omnibus, https://www. ncbi. nlm. nih. gov/geo/. Complete listings of data series IDs and sample numbers are provided in Additional file 2: Table  S7  and Additional file 3: Table  S8. All the data generated for MyoMiner are available at https://sys-myo. com/myominer/.
To evaluate selective constraints, we calculated the rates of nonsynonymous (amino-acid altering, dN) and synonymous (silent, dS) substitutions across orthologous sequences of all Dnmt3 genes. Dnmt3C displays the highest average pairwise dN/dS of all Dnmt3 genes (0. 88) compared with Dnmt3L (0. 23), Dnmt3B (0. 22), and Dnmt3A (0. 02) (fig. 1B ). Higher dN/dS values could reflect relaxation of selective constraint. Alternatively, these higher values could be the result of diversifying selection acting on Dnmt3C. To distinguish between these possibilities, we used likelihood methods implemented in the PAML package to detect signatures of positive selection  (Yang 1997). Muroideae are an ideal species set for these analyses because they span a short evolutionary time (40 My) with low saturation of dS  (Steppan and Schenk 2017).
Identification of DNMT3 Orthologs To identify Dnmt3 orthologs, we performed TBLASTN searches on the NCBI nonredundant nucleotide database  (Altschul et al. 1990 ; NCBI Resource Coordinators 2016), using reference protein sequences of mouse DNMT3A (NP_031898. 1), DNMT3B (XP_006498745. 1), DNMT3L (NP_001075164. 1) as well as the predicted protein sequence from the Dnmt3C cDNA cloned from male fetal gonads  (Barau et al. 2016). Although most Dnmt3s have predicted sequences in reference databases, Dnmt3C genes are not annotated in most muroid genomes. In these cases, we queried genomes directly using TBLASTN, and predicted gene models from contigs using GeneWise  (Birney et al. 2004 ). CDSs were annotated based on the longest mouse gene model.
Alignments and Phylogenies All sequence alignments are available as Supplementary Material online. Alignments were generated using ClustalW v2. 1 (IUB cost matrix;  Larkin et al. 2007) or MAFFT v7. 388 (Katoh and Standley 2013). Maximum likelihood phylogenies were built using PHyML v3. 0 with 100 bootstraps  (Guindon et al. 2010). Trees were visualized using the software Geneious Prime (Biomatters Ltd). In all cases, we used nucleotide alignments of the CDS and the HKY85 substitution model.
Acknowledgments We would like to thank members of the Bourc'his and Malik laboratories, especially  Joan Barau ,  Tera Levin , and  Janet Young  for technical help and critical reading of this article. This work was supported by a postdoctoral fellowship from the  Damon Runyon Cancer Research Foundation  (to A. M. ),  National Institutes of Health (NIH)  Grant  R01 GM074108  (to H. S. M. ), and an  Howard Hughes Medical Institute (HHMI)   Investigator Award  (to H. S. M. ). The laboratory of D. B. is part of the  Laboratoire d'Excellence (LABEX)  entitled  DEEP  ( 11-LBX0044 ).
In this study, we make use of a newly available snow dataset  (Fayad et al. , 2017b)  to evaluate this new percolation scheme in SnowModel to compute the SWE distribution and its evolution, for the first time, in three basins in the warm Mediterranean mountains of Lebanon. We evaluate the model outputs using both in situ (continuous snow depth records at AWSs and snow course measurements) and remote sensing data (MODIS snow cover area). We show that the improved percolation scheme led to better performance, and we used this model version to estimate the daily evolution of the SWE distribution over three water years  (2013-2014, 2014-2015, and 2015-2016, hereafter W1314, W1415, and W1516, respectively).
The different land classes are needed to account for the snow holding capacities and canopy radiation effects  (Liston and Elder, 2006a) ; however, bare soil is the dominant class in the snow-dominated region of the study area. SnowModel was forced using the half-hourly meteorological data collected at the AWSs (LAQ, MZA, CED) for three snow seasons (1 November to 30 June) between 2013 and 2016. The data were taken from  Fayad et al. (2017c)  and only reformatted to match SnowModel input format. We used the following input meteorological variables: precip- itation (LAQ, MZA), air temperature (LAQ, MZA, CED), air relative humidity (LAQ, MZA, CED), and wind speed and direction (LAQ, MZA, CED). No observations were available for the LAQ AWS during snow season 2013-2014  (Fayad et al. , 2017b).
Model validation The validation dataset includes (1) half-hourly snow height (HS), snow albedo and incoming shortwave radiation collected at the AWS from W1314 to W1516; (2) bi-weekly manual SWE and snow density measurements collected near the AWS during the snow seasons of W1415 and W1516; and (3) daily snow cover area (SCA) observations from MODIS (time period from W1314 to W1516). All these data are fully described in  Fayad et al. (2017b)  and are available as open data in a public repository  (Fayad et al. , 2017c). Nearly continuous snow depth is available for water years W1415 and W1516 at LAQ and for water years W1314, W1415 and W1516 at MZA and CED. Half-hourly snow depth records were averaged to the daily time step. We computed the daily albedo of the surface below AWS from halfhourly upward-and downward-looking pyranometer measurements following  Stroeve et al.
(2013) , i. e. , by computing the ratio of the daily incoming and reflected shortwave radiation totals. Summer albedo values ranged between 0. 2 and 0. 4 depending on the site and the year; hence, we removed albedo values lower than 0. 5 to keep only snow albedo measurements. The calculated daily snow albedo values and the incoming shortwave radiation were averaged to monthly values. The MODIS dataset is a daily cloud-free time series of snow cover maps providing the snow presence and absence at 500 m resolution (binary SCA). The model outputs at 100 m resolution were converted to binary SCA using a threshold of 10 mm w. e. of SWE. We used the snow course locations to approximate the pixels that are representative for each snow course. The simulated SWE values were evaluated by comparing the observed SWE (revisit time ∼ 11 d on average for all snow courses according to  Fayad et al.
, 2017b)  with the simulated SWE value corresponding to the same dates when the in situ observations were taken. Five snow courses located near the three stations are used to showcase the evolution of SWE across different elevation bands (snow courses near the three AWS have elevations at 2823 and 2834 m a. s. l. for CED, 2297 and 2301 m a. s. l. for MZA, and 1843 m a. s. l. for LAQ). To further investigate the effect of the liquid water percolation scheme on the internal properties of the snowpack, we used the wet snow detection from Sentinel-1 C-band Synthetic Aperture Radar data. We use these data as a proxy of melting conditions that we compared to the simulated release of liquid water from the snowpack. The wet snow detection was done using Sentinel-1 observations over the period October 2014 to June 2016.
SWE estimation We used the model outputs to estimate the evolution of the SWE over the three basins (Fig. 1 ). The basin surface boundaries were used to estimate the SWE contribution for each basin by computing the mean daily SWE for all pixels within each basin. To account for the hydrological contribution of SWE from the snow-dominated regions, the daily distributed SWE was spatially integrated for elevations above 1200 m a. s. l. From the temporal evolution of these basinscale SWE time series we derived the following key indicators: date and value of the peak SWE (maximum SWE during a water year), snow melt-out date (the first day of the calendar year on which SWE gets below 1 mm w. e. ), and distributed SWE for the peak SWE data (this map is important from a hydrological perspective as it shows the amount of snow available for melt at the end of the snowfall season for each hydrological year).
The existence of a large number of sinkholes in the region act as a trap for blowing snow. Figure  11  shows an example where snow depth varies from zero to more than 2. 5 m over distances as short as a few hundred meters due to snow transport by wind. Previous work showed that the blowing-snow and snow redistribution module in SnowModel improves the accuracy of the simulations (see  Gascoin et al. , 2013). Hence, we chose not to focus on this aspect in this study and assume that it contributes to a realistic representation of the snowpack. Capturing snow redistribution of snow in the future would probably require focusing on a smaller model domain with an accurate fine-scale elevation DEM that is currently not available in this region. A proper representation of the snow redistribution could also help in better explaining the role of advective heat fluxes and improve the model performance in capturing SWE.
Code and data availability. The SnowModel code is archived at https://github. com/jupflug/SnowModel (last access: 12 July 2019)  (Pflug, 2019). Model forcing and validation data are available at https://doi. org/10. 5281/zenodo. 583733 (Fayad et al. , 2017c). Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/hess-24-1527-2020-supplement. Author contributions. Both AF and SG worked on the conceptualization of the research goals and the design of the methodology as well as analyzing the model outputs. AF and SG contributed equally to the writing and editing of the paper.
Background: It is of utmost importance to investigate novel therapies for cancer, as it is a major cause of death. In recent years, immunotherapies, especially those against immune checkpoints, have been developed and brought significant improvement in cancer management. However, on the other hand, immune checkpoints blockade (ICB) by monoclonal antiboties may cause common and severe adverse reactions (ADRs), the cause of which remains largely undetermined. We hypothesize that ICB-agents may induce adverse reactions through off-target protein interactions, similar to the ADR-causing off-target effects of small molecules. In this study, we propose a hybrid phenotype mining approach which integrates molecular level information and provides new mechanistic insights for ICB-associated ADRs. Methods: We trained a conditional random fields model on the TAC 2017 benchmark training data, then used it to extract all drug-centric phenotypes for the five anti-PD-1/PD-L1 drugs from the drug labels of the DailyMed database.
Proteins with structure similar to the drugs were obtained by using BlastP, and the gene targets of drugs were obtained from the STRING database. The target-centric phenotypes were extracted from the human phenotype ontology database. Finally, a screening module was designed to investigate off-target proteins, by making use of gene ontology analysis and pathway analysis. Results: Eventually, through the cross-analysis of the drug and target gene phenotypes, the off-target effect caused by the mutation of gene BTK was found, and the candidate side-effect off-target site was analyzed. Conclusions: This research provided a hybrid method of biomedical natural language processing and bioinformatics to investigate the off-target-based mechanism of ICB treatment. The method can also be applied for the investigation of ADRs related to other large molecule drugs.