dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments: The  CRESPI Study Group  involves:  Nicole Le Moual ,  Orianne Dumas ,  Flore Amat ,  Anastasie Eworo Nchama ,  Annabelle Bédard  ( Inserm U1018, CESP, Villejuif ),  Corinne Mandin ,  Pierre Bonnet ,  Isabelle Lacaze ,  Sutharsini Sivanantham ,  Mélanie Nicolas  ( CSTB, Champs sur Marne & Grenoble ),  Barbara Le Bot ,  Gaëlle Raffy  ( EHESP, Rennes ),  Etienne Sévin  ( Epiconcept, Paris ), and  Valérie Siroux  (Inserm  U1209 , Grenoble). The authors would like to thank all  Inserm  and  CSTB  staff who participated in the setting of the CRESPI study and especially air quality technicians, fieldworkers, and people involved in quality control and data management for their heavy work. The authors thank especially the  Inserm  staff:  Clemence Guilhen ,  Samira Medjahed , and  Stéphanie Raoult des Essarts  for their implication in the protocol, the set-up of the survey, the logistic organization, and visits of daycares;  Abderrezack Tafer ,  Jean-Charles Lodin ,  Emilie Pacheco Da Silva ,  Lina Meziani ,  Samy Dehissi ,  Rozenn Delabarre , and  Sarah Emamdjomeh  for their implication in the study and/or visit of daycares during their internship;  Samy Dehissi  and  Rozenn Delabarre  for recording the complete compounds list of cleaning products scanned in the daycares;  Sarah Idrisse Abdou ,  Giovanni Paparella , and  Hadja Ndiaye  for data entry; and  Chloé Landais  for her implication in the study, the checking of data quality, data management, and statistical analysis during her work-linked training (Inserm).
: Various steps of the set-up of the CRESPI study; Figure S2: Databases available in the CRESPI cohort; Table S1: Extract of the cleaning products' composition database. Author Contributions: N. L. M. , O. D. , P. B. , B. L. B. , E. S. , I. P. , V. S. and C. M. contributed to write the initial study protocol, funding proposals, conceptualize the CRESPI study, the data and biological collections. A. E. N. , P. B. , C. M. and N. L. M. were responsible for data curation and of the logistical set-up of the CRESPI study in daycares. E. S. , A. E. N. , O. D. , V. S. and N. L. M. were in charge of the development/improvement of the smartphone application. N. L. M. , O. D. , V. S. and C. M. received funding for various parts of the study. N. L. M. drafted the first version of the manuscript. All authors have read and agreed to the published version of the manuscript.
Funding: The CRESPI study (PI: N Le Moual) is supported by Anses (Grant Anses-PNR-EST-2017-1-101), Ademe (Grant 1762C0021), Fondation de France (Grant 00081167), and ARS Ile de France (Grant 2018-2019). Additional financial support was obtained from ANR (ANR-18-CE36-0002; CRESMINA; PI: O Dumas); Anses (CHIMAIR-METHYL; Grant 'Programme Environnement-Santé-Travail de l'Anses avec le soutien des ministères chargés de l'écologie et du travail' 2019/1/097; PI: V Siroux); and Fondation Sante Environnement from the Mutuelle Familiale, Paris, France (Grant AP-FSE-19-001; CRESPI-EMIS; PI: C Mandin) for the three complementary projects. Institutional Review BoardStatement: The CRESPI study (Inserm C18-05; ID RCB n • 2018-A02657-48) has been approved by the French ethic commitee 'Comité de protection des personnes' (CPP Sud-Est I n • 2019-38; May 2019) and the French Data Protection Authority 'Commission Nationale de l'Informatique et des Libertés" (CNIL n • 919185; October 2019).
Background: In individuals or animals suffering from genetic or acquired diseases, it is important to identify which clinical or phenotypic variables can be used to discriminate between disease and non-disease states, the response to treatments or sexual dimorphism. However, the data often suffers from low number of samples, high number of variables or unbalanced experimental designs. Moreover, several parameters can be recorded in the same test. Thus, correlations should be assessed, and a more complex statistical framework is necessary for the analysis. Packages already exist that provide analysis tools, but they are not found together, rendering the decision method and implementation difficult for non-statisticians.
Conclusions: Gdaphen makes the analysis of phenotypic data much easier for medical or preclinical behavioral researchers, providing an integrated framework to perform: (1) pre-processing steps as data imputation or anonymization; (2) a full statistical assessment to identify which variables are the most important discriminators; and (3) state of the art visualizations ready for publication to support the conclusions of the analyses. Gdaphen is open-source and freely available at https:// github. com/ muniz mom/ gdaph en, together with vignettes, documentation for the functions and examples to guide you in each own implementation.
Background In individuals or animal models suffering from a disease or with another condition, the identification of selective discriminating variables is important to understand the differences between disease and non-disease states, to detect the response to treatments or to discern sexual dimorphism. The current behavioral data are generally complex multifactorial datasets. Those datasets may contain plenty of both qualitative and quantitative information, in some cases structured in groups when several features are measured in the same test. The data recorded can include the performance of each individual in the different tests, or the performance of paired or grouped animals in sociability or mating studies, and can contain extensive medical information about phenotypes, weight, health problems and other molecular records such as gene expression.
This is because highly correlated features do not provide new information and instead will add noise that will weaken the model, and even more when considering low samples sizes. In fact, the coefficient estimates of the features will impact on the dependent variable and will give larger standard errors (increasing the type II error) and reduce the efficiency of the model predictions when applied to a new set of data. In addition, within-test features could belong to different distribution families. Then, it should be considered to apply the appropriate statistical analyses to reach accurate results. Regarding all these core challenges embedded in the experimental data itself, it is clear that a more complex statistical framework is necessary to identify the most discriminative variables among dependent variables and give them reliable and comparable coefficients of importance.
Moreover, in most cases the visualizations produced by existing tools cannot be used in publications as they are difficult to read, explain or customize in both the colors and sizes of the plots. To address these limitations and answer to the community needs, we developed Gdaphen (Genotype discrimination using phenotypic features), a pipeline in R containing a suite of functions capable of dealing with all the key analytical steps. Gdaphen includes, from preprocessing steps such as data anonymization or imputation of missing values, to feature selection where a new modeling method is implemented based on selecting features contributing more than 30% to explain the variance in the data, and visualization. Gdaphen is freely available on GitHub (https:// github. com/ muniz mom/ gdaph en; https:// github.
Next, to overcome the curse of dimensionality in the data imputation, Gdaphen uses another machine learning bootstrapping-based algorithm aregImpute. Gdaphen allows to perform the discrimination analysis using three different models of features. First, Gdaphen considers all the features in a dataset and perform two methods of features selection (1) eliminating the highly correlated variables (2) selecting the variables accounts for more than a certain percentage of the variability in the dataset. Thanks to these multimodal analyses, Gdaphen can provide an in-depth analysis of all the features, focus on the identification of multicollinearity to decipher its biological relevance and assess the necessity, efficiency, and suitability of employing feature selection methods considering the data at hand.
Finally, in the visualization module, Gdaphen allows to resize or choose the colors assigned to each feature or group of features for each model analyzed. The plots produced can be used directly in the publications with good quality and readability. Moreover, as the color representing thoses variables can be chosen, the comparison of results between your different publications and analyses is made easier. Overall, Gdaphen main goal is to provide the guided computational framework to answer the following questions. 1. Assess the efficiency reached by each classifier (GLM or RF), on the three different input datasets, the original dataset carrying highly correlated features and the datasets ready for the analysis generated by performing the two feature selection methods. The feature selection methods help to denoise the data and reach statistically reliable results.
2. Assess feature correlation and provide the statistical assessment for each found correlated pair. 3. Identify the grouped/non-grouped features that are more relevant to discriminate between genotypes, sexes, mouse line backgrounds or treatments effects. And in the case of gene or treatment dosages, identify the features following a linear dosage effect. In this paper we give an overview of Gdaphen main features (Fig. 1 ; Table  1 ) and show two examples of the application of Gdaphen to phenotypic datasets of mouse models of neuropathic pain. The datasets show increased complexity and some results of the application of GDAPHEN are published as part of the deep statistical analyses performed over the phenotypic characterization of these models  [25, 26].
• Imputation of missing values if they exist There are several methods of imputation of missing values (NAs) widely used in biology. The most common one when only one NA exist, is to impute using the mean or the median for each group of individuals. The group is shaped considering the features selected as conditioning, (like genotype, sex and treatment state). In this case the experimenter can already replace the missing value by his chosen mean/median value. Although we recommend that no imputation method should be used if the number of individuals per group is less than 10 or if the distribution of the values is broad and the standard deviation too high. On these cases, if imputation is really insisted on, it could be better to select a random number considering your sampling size. Table  1  Gdaphen goals and more relevant features described for each module: pre-processing, analysis and visualization.
Visualization module Resulting from the analysis, Gdaphen generates nine plots that are ready to be included in formal publications and where we leave certain aesthetic parameters to be defined by the analyst to assure the good sizing of their plots depending on the number of explanatory variables and number of categories of dependent variables. 1. Grouped features correlation to the three main principal components dimensions partial axes plot. Stored in partialAxes_Plot1 folder 2. Variables contribution/correlation with the three main principal components: grouped-features correlation PCA plot. Stored in groupContribution_Plot2 folder.
Benchmarking Gdaphen: results We have run Gdaphen pipeline on the phenotypic data scored for two novel mouse genetic models showing an increased pain behavior (1) Scn10a G1662S Point Mutation, and (2) Scn9a R185H point mutation. We also tested successfully Gdaphen with RT-qPCR data (data not shown, the sample size was low). A part of the analysis performed on the mouse models for Scn10a  [25]  and Scn9a  [26]  genes is already available in the publications containing the molecular and phenotypic characterization of these models. 1. For the Scn10a G1662S murine model, 112 animals including males and females (57 females and 55 males) and a total of 36 wild-type, 39 heterozygous, 37 homozygous animals were challenged in a phenotypic pipeline and the data from 14 variables was collected for the analysis.
These variables included genotype, sex, and 12 phenotypic traits (Additional file 2: Table  S2 ). None were correlated more than a 75% (Additional file 3: Table  S3 ) and so we considered there was no strong multicollinearity and proceeded to perform the second feature selection. Seven variables plus sex and genotype were contributing to explain the variability of the dataset in more than a 30% and were thus identified as the most discriminative variables for the three genotypes. When examining the cumulative variance considering the first 10 dimensions (plot cumulative variance, Fig. 2A, B ) we observed that feature selection helps to denoise the data. Indeed, the sel30% model shown in Fig. 2B  explained a higher % of the variance of the data (100%) than using the full model containing 14 variables (86%) shown in Fig.
2D ). The classifiers GLM-Net and RF identified von Frey as the main variable for genotype discrimination. In addition, both classifiers also identified acetone test, number of coping reactions and latency time on 50 °C hot plate as key explanatory variables. Tail pressure was only identified by RF. When compared to the full model, the variables highlighted in the sel30 model are emphasized. In addition, tail flick and plantar Hargreaves can help in the discrimination of 2 out of the three genotypes. However, these two variables do not produce a better discrimination analysis considering the variance explained by the models. In support of the classifiers results for the sel30% model, the multi factor analysis (MFA) square loading plot (Fig. 2H ) show that von Frey and number of copying reactions in Hot plate 47 and 50 degrees are the most strongly influencing variables in our dataset in dimensions 1 and 2 and dimensions 2 and 3.
Tail pressure was also contributing weakly to those dimensions for the genotype discrimination. In addition, the number of paw-lifts and jumps on the 5 °C cold plate contributes to the discrimination in dimensions 2 and 3. Coping reactions on the 50 °C and 54 °C hot plates contributed the most in dimension 1 (Fig. 2H ). Moreover, examining the correlation between the location of each vectorized variable with each genotype on the principal components by the cosine similarity distance (Fig. 2I ), we reach the same conclusions. 2. For Scn9a R185H murine model, 73 animals including males and females (34 females and 39 males) and 24 wild-type, 28 heterozygous and 21 homozygous individuals, were challenged in a specific phenotypic pipeline and the data from 18 variables was collected for the analysis.
Conclusions/discussion Taking advantage of the R programming software and the existence of state-of-the-art packages to implement statistical analyses, Gdaphen provides an open-source integrated computational framework for variable discrimination based on phenotypic data that is easily accessible for medical and behavioral researchers. Considering the real challenge and burden the analysis of these data represents for our community, we saw the need to create and distribute this package. Hence Gdaphen, definitively a versatile and adaptable pipeline can become a valuable implementation in the R phenotypic-based statistical research ecosystem. Moreover, Gdaphen gathers functions for the analysis and visualization of the most important predictor qualitative and quantitative variables for the discrimination between groups.
In addition, Gdaphen allows the analysis of grouped variables where the analyst can freely establish the groups. Finally, Gdaphen includes examples to guide you along the pipeline to allow non expert R users to perform their own analyses and take their own decisions depending on their datasets. A certain limitation of this pipeline exists in the fact that we did not introduce phenotypic regression algorithms to predict the expected phenotypes based on the associations between single nucleotide variants (SNVs) identified by genome-wide association studies (GWAS) data, splicing or expression quantitative trait loci (QTLs) and linkage disequilibrium to mention some. Instead, we decided to implement uniquely classification analyses, because our final goal was to identify categorial responses in the dependent variable as for example genotype, sex, treated versus controls, etc.
On these cases we use as explanatory variables data measured from phenotypic analyses, clinical data, histopathological measurements, or genetic information in shape of gene expression measurements coming from RT-qPCR, dd-PCRs or RNA-Seq analyses. Thus, Gdaphen is not suitable in the package current form to address phenotypic regression problems although this can be part of a future implementation. G Categorical variables discrimination component map. The panels show the distribution in 2 dimensions of the categorical variables PCA coordinates calculated in the MFA analysis using the MFAmix function from PCAmixdata R package  [21]. H Square loadings plot with coordinates calculated in the MFA analysis using the MFAmix function from PCAmixdata R package  [21]. I Parallel plot showing the non-scaled results of the most influencing variables to the discrimination colored by genotype and sex showing the mean of the variable per group of genotype and sex.
Implementation Gdaphen is a R pipeline that allows the identification of the most important predictor qualitative and quantitative variables for a specific dependent variable discrimination in animal models of diseases. The dependent variable can be freely setup by the analyst but should be of categorical nature. As an example, Gdaphen was primary developed to identify the variables most important for genotype discrimination or to uncover the variables more influenced by sex or treatments responses. Gdaphen can be run in RStudio or in shell R. Gdaphen pipeline is freely available on GitHub accompanied by one vignette in classical format and another as an RStudio project object (https:// github. com/ muniz mom/ gdaph en; https:// github. com/ YaH44/ GDAPH EN). Gdaphen main features include the availability in the same pipeline of the functionalities and visualizations as summarized in Fig.
Acknowledgements We would like to thank  Drs Yaping Xue  and  Celeste Chidiac  for providing their dataset with the phenotypic information. Thanks to  Dr Arnaud Duchon  for providing several datasets as the RT-qPCR data and phenotypic data for Down syndrome models. Thanks to  Corentin Guioullier  who tried out the Gdaphen scripts and benchmarked it on Jupyter notebook. Thanks to  Gopal Krishna  and  Valerie Herault  for interesting discussions about the working of the package and for testing it.
Funding This work was supported by the  National Centre for Scientific Research (CNRS) , the  French National Institute of Health and Medical Research (INSERM) , the  University of Strasbourg (Unistra) ,  French government funds through the "Agence Nationale de la Recherche " in the framework of the  Investissements d' Avenir program  by  IdEx Unistra  ( ANR-10-IDEX-0002 ), a  SFRI-STRAT ' US  project ( ANR 20-SFRI-0012 ),  EUR IMCBio  ( ANR-17-EURE-0023 ) and  INBS PHENOMIN  ( ANR-10-INBS-07 PHENOMIN  and also provided by the  Joint Programming Initiative Neurodegenerative Diseases (JPND) multinational research  project  HEROES  ( ANR-17-JPCD-0003 ) to YH. This project received funding from the  European Union 's  Horizon 2020 research and innovation program  under grant agreement  GO-DS21 No 848077  to YH.
Availability and requirements Project name: Gdaphen: R pipeline to identify the most important predictor qualitative and quantitative variables from phenotypic data for the discrimination of your variable of interest. Project home page: https:// github. com/ muniz mom/ gdaph en. https:// github. com/ YaH44/ GDAPH EN. Operating system: Mac/Linux/Windows. Programming language: R. Other requirements: Installation of R runtime library R2019a (9. 6) and several R packages as specified in Additional file 1: Table  S1. Can run in bash R or R Studio. License: GNU GPL 3. 0. Any restrictions to use by nonacademics: None.
• Consider qualitative variables with at least two different categories to perform the discriminative analysis. • Standardization of the data by scaling: this step is necessary as each independent variable has a different range of values that are observed. The re-scaling allows to calculate the contribution of each variable in a comparable way as all variables will have the same range of values observed and/or the same variance. Gdaphen module Step Goal Relevant features Pre-processing Pre-process- Prepare the data for the ing analysis [12]ymization of the samples/ individuals in case we work with human datasets. Renaming the identifiers, noted as "Ind" of each animal if there are duplicated when considered per genotype/sex Imputation of NAs using two methods (a) the mean if only one value is mising.
Data collection Twenty recreational runners (11 males and 9 females; mean (SD); age 25  (6)  years; BMI 20. 76 (1. 75) kg/m 2 ; running experience 5. 9 (5. 5) years; maximum aerobic speed 17. 3 (1. 8) km/h) volunteered for the study. Participants were recruited between January and June 2021. This study protocol was approved by the national ethical review board (2019-A03012-55) and was conducted in accordance with the guidelines of the Helsinki World Medical Association Declaration. Following the explanation of any risks and benefits associated with the study, all participants approved the study and gave written informed consent. Data/samples were collected between February and July 2021. The data post-process was conducted between July 2021 and July 2022. Only the authors who participated in data collection (i.
e. , AG and LM) had access to information that could identify individual participants during or after data collection. The individual's self-selected preferred running speed (PRS) was assessed following the method described by  [9, 13]. The participants were asked to run at 80, 90, 100, 110 and 120% of PRS, and to run at 90% of PRS at different treadmill inclinations: ±2, ±5, and ±8%, in a randomized order. Each running condition was held for 10 minutes. An IMU (Movesense, Suunto, Finland) was attached on the runner's thorax with a chest band, and two IMUs were fixed to each instep foot with a clip. An optoelectronic system (Optogait, Microgate, Italy) was used for the optical detection of the running kinematics. The Optogait transmitting and receiving bars were permanently fixed with adhesive-tape on the lateral bands of the treadmill.
Temporal variables values (Optogait) Gold-standard stride frequency and duty factor from the optoelectronic device were directly accessed after exporting the data with the Optogait software. The long-range DFA-α and HG-D were calculated from the stride frequency for all the 20 runners in all running conditions with the Python (3. 8) package antropy. Both DFA-α and HG-D were computed considering the first 10 minutes after the start of the single running condition following best-practice guidelines  [4]. Additional results including DFA and HG-D calculations at different times  (2, 4, 6, 8)  are available in the Additional Material https://drive. google. com/file/d/ 1y32xqVDioUXlzbsaLkgIL1JEVwg1kTXA/view?usp=sharingand in  [28].
Other estimation strategies (IMU on the thorax + machine learning algorithm with short time windows) In sync with the Optogait, the IMU placed on the runner's thorax was used to provide the input for the machine learning algorithm (i. e. , the input variables), whilst the optoelectronic device was used to provide the output (reference values that the neural network must learn to reproduce, i. e. : the output variables). Two datasets including the data from 10 runners each were created: the training dataset and the test dataset. The former was used to train the machine learning algorithm (i. e. , to adjust the parameters of the model), whilst the latter was used to assess the accuracy of the model estimations. The technical details of the machine learning algorithm have been recently discussed and presented in peer-reviewed conference paper  [28].