dataesr/scientific-paragraphs-categorization huggingface dataset:
In other words, these pathways should have at least one protein coding gene having coexpression fallen in the top 0. 1% in the scRNA-seq dataset and at least one protein having predicted FI for proteins understudied. As expected, we saw more such pathways for not-dark proteins than dark proteins in the annotated proteins (p-value = 2. 7E-5, Mann-Whitney-Wilcoxon test) and the similar pattern for annotated proteins and not-annotated proteins for the not-dark proteins (p-value = 7. 8E-3) (Figure  4B , right panel). Intriguingly, we don't find any significant difference of numbers for such pathways between dark and not-dark not-annotated proteins and between annotated and not-annotated dark proteins, strengthening the unbiasedness of scRNAseq results that support the predicted interacting pathways.
Validating Interacting Pathway Scores by Analyzing PubMed Abstracts Using Natural Language Processing Technology The blood scRNA-seq data analysis results provide unbiased support evidence to the interacting pathways based on predicted FIs. In this section, we seek more evidence from published literature with caution that understudied proteins have less published literature than well studied proteins. To do this, we developed a natural language processing (NLP) workflow based on the pre-trained BERT (Bidirectional Encoder Representations from Transformers) language model  [8]  to embed abstracts downloaded from PubMed and pathway text summaries manually written in Reactome into numeric vectors. After that, we calculated cosine similarities between embedded abstracts and embedded pathway text summaries to quantify the similarities between abstracts and Reactome pathways.
Manual Literature Annotation Supporting Predicted Interacting Pathways for Dark Proteins To further explore the validity of the interacting pathway predictions, literature and database searches were performed on twenty randomly selected dark proteins to determine whether existing published experimental data supported roles for these proteins in their respective predicted interacting pathways. PubMed searches were performed using both the gene names and UniProt identifiers. GeneCards, UniProt, and GO entries were also searched for functional annotations with direct experimental evidence. The results of this analysis are shown in Table  1  (For more details, see the "manual annotation of interacting pathways for 20 dark proteins. xlsx" file in Supplemental Results). Direct experimental evidence suggesting a function for the dark protein in the interacting pathway was found for twelve of twenty proteins.
Exploring the Interacting Pathways at the Reactome IDG Web Portal To provide the community with a resource to learn biological functions and therapeutic potentials of understudied proteins and proteins that have not been annotated in Reactome, we have developed a web portal. The portal was developed on the foundation of the Reactome web application by implementing a new homepage and enhancing the pathway diagram widget and overlay features. The main entry point of the portal is the homepage, deployed at https://idg. reactome. org, a progressive single-page web app powered by JavaScript widgets, where users may search for interacting pathways for a gene or a protein based on gene symbol or UniProt accession number, respectively. The homepage presents multiple views for users to explore the found interacting pathways.
Clicking the stable id link for an interacting pathway opens a new browser tab showing Reactome IDG's enhanced pathway browser (Figure  8 ) where users can investigate interacting pathways by overlaying tissue-specific gene or protein expression data collected in the TCRD database  [34]  or gene or protein pairwise relationships we collected to train the random forest classifier (Figure  8A ). By default, the numbers of drugs targeting entities rendered in the pathway diagram are shown in the purple circles at the top left corner of entities, which the user may click to bring up the drug-target network view (Figure  8B ). The SBGN-based pathway diagram in the pathway browser can also be switched to the simplified functional interaction view of the pathway by extracting FIs from complexes and reactions annotated in the pathway.
Discussion Reactome is the most comprehensive open source biological pathway knowledgebase that is widely used in the community. Due to biased studies, about one third of human proteins have not been extensively investigated and their therapeutic potentials have not been explored yet. These proteins are called "dark proteins" or "dark targets"  [2]. To infer possible functions for these proteins, we have developed a novel computational framework that integrates a machine learning approach and data from publicly available sources to predict functional interactions between dark proteins and proteins annotated in Reactome and then place dark proteins within the context of Reactome pathways. The correlation analysis results using an independent scRNA-seq dataset and PubMed abstracts based on an NLP workflow support our prediction results.
It can also be applied to any protein that has been annotated in Reactome to study crosstalk between pathways or fill gaps in Reactome's pathway annotation. We have implemented three distinct approaches to evaluate the performance of our interaction pathway predictions for proteins. Our first approach involved examining a blood scRNA-seq dataset, which was not used and is independent of any feature in our random forest training and providing an impartial way to evaluate our predictions. This dataset is unbiased with respect to the knowledge levels of proteins. Our random forest approach integrates features from multiple sources, such as gene expression data that is tissue or cancer specific, resulting in a generalized summary of protein function that is agnostic to cell or tissue type. Despite this generalization, we found a strong positive correlation between our predicted pathway interaction scores and those derived from top co-expression analysis from this scRNA-seq data.
Downloading and Processing Gene Co-Expression Data We collected a dataset of 106 protein/gene pairwise relationship features to train a random forest model for predicting functional interactions of proteins. The majority of these features were obtained from gene coexpressions derived from bulk RNA-seq data obtained from the GTEx and TCGA projects. Tissue-specific gene expression data was downloaded from the GTEx portal, https://www. gtexportal. org/home/datasets. The file we used was the gene read counts file, GTEx_Analysis_2017-06-05_v8_RNASeQCv1. 1. 9_gene_reads. gct. gz. Samples with RIN values less than or equal to 6. 0 and tissues with fewer than 30 samples were filtered out. Cancer-specific RNA-seq gene expression data was acquired in November, 2019 using a customized Python script that utilized the GDC API as described in this document, https://docs.
gdc. cancer. gov/API/Users_Guide/Getting_Started/. The script is available at and BioGrid datasets. To map the model organism PPIs to human, we employed different mapping strategies. For yeast, worm, and fly, we used the panther orthologous mapping file, RefGenoeOrthologs. tar. gz, downloaded from ftp://ftp. pantherdb. org/ortholog/ in March 2019. We filtered this file to extract human genes using the command "grep '^HUMAN' RefGenomeOrthologs > HUMAN_RefGenomeOrthologs". To map mouse PPIs to human PPIs, we utilized the ENSEMBL Compara protein families (Release 98, downloaded in February 2020) to maximize the number of mappable PPIs between the two species.
Downloading and Processing Gene Similarity Data, Protein Domain Interactions and Go Annotation Gene similarity data was downloaded from Harmonizome, https://maayanlab. cloud/Harmonizome/download. To integrate this data into our workflow, we ported the Python script at its download website into Java. We manually selected a subset of datasets that were likely to provide pathway-related information, while excluding datasets related to gene expressions or non-human data. For details on the selected datasets, please refer to the "harmonizome_datasets_annotations_062819. xlsx" file in Supplemental Results. Protein-protein domain interactions were acquired from pFam (release 32. 0, downloaded from https://ftp. ebi. ac. uk/pub/databases/Pfam/releases/Pfam32. 0/ in December, 2018) and gene GO annotations were downloaded from the GO web site (goa_human.
Feature Selection for Random Forest Training To assess the quality of gene or protein pairwise relationships for training the random forest classifier, we utilized functional interactions (FIs) extracted from complexes and reactions in Reactome (Release 71, December 2019) using a method we developed previously to build the Reactome FI network  [42]  and calculated an odds ratio for each dataset. Only datasets with odds ratios greater than 5. 0 were selected as features for training the random forest classifier. For gene similarities obtained from Harmonizome, we employed an adaptive cutoff approach based on a methodology described by Iacono et al  [43]. This approach involved selecting gene pairs with top 1% or 0. 1% gene similarity scores to either increase the odds ratio or retain a higher number of gene pairs.
Configuration details can be found at https://github. com/reactomeidg/fi-network-ml/blob/master/src/main/resources/harmonizome_selected_files. txt. In total we collected 106 gene or protein pairwise relationship features, which encompassed 31 cancer specific gene co-expression features from TCGA, 48 tissue specific co-expression features from GTEx, 20 gene similarity features from Harmonizome, 5 protein-protein interaction features from StringDB, BioGrid, and BioPlex, 1 protein domain-domain interaction feature from pFAM, and 1 biological process annotation sharing feature from GO. Further details and analysis results for these features can be found in SelectedFeatures_0415_2020. xlsx in Supplemental Results.
Training the Random Forest Classifier to Predict Protein Functional Interactions The random forest model was trained using the RandomForestClassifier model from the scikitlearn Python package (version 0. 23) with Python 3. 8. To determine the optimal parameters for this model, we employed the gridsearch function in scikit-learn. The parameters explored in the grid search included class_weight ('balanced', 'balanced_subsample', None), max_depth (2, 4, 6, 10, None), max_features ('auto', 'sqrt', 'log2'), min_samples_leaf (1, 2, 4), min_samples_split (2, 4, 10), and n_estimators (100, 200, 500, 1000). To conduct the grid search, we randomly selected 100 positive gene pairs representing functional interactions (FIs) and 10,000 negative gene pairs. These pairs were divided into 75% for training and 25% for validation, and this process was repeated 10 times.
The final set of parameters used in the Random Forest model were as follows: bootstrap=True, ccp_alpha=0. 0, class_weight='balanced', criterion='gini', max_depth=10, max_features='auto', max_leaf_nodes=None, max_samples=None, min_impurity_decrease=0. 0, min_impurity_split=None, min_samples_leaf=1, min_samples_split=2, min_weight_fraction_leaf=0. 0, n_estimators=200, n_jobs=None, oob_score=False, random_state=42, verbose=0, warm_start=False. For further details on the implementation, please refer to the Python script available at https://github. com/reactome-idg/fi-networkml/blob/master/scripts/ml/fi_predictor. py.
Downloading and Analyzing the Blood scRNA-seq Dataset We obtained scRNA-seq data for blood from the Tabula Sapiens project  [15]  via https://figshare. com/articles/dataset/Tabula_Sapiens_release_1_0/14267219?file=34701964. We downloaded the h5ad file, loaded it as an AnnData object using the scanpy Python package  [45] , and filtered genes to about 20,000 human protein coding genes recorded in Reactome. After that, we converted the h5ad file into a Seurat rds object  [46] , and then passed this object to the network function in BigSCale2 R package  [16]  with speed. preset='fast'. To select the most reliable correlation, the correlation output from BigSCale2 was then loaded into a Java class to select the top 0. 1% of positive correlations by following the approach described by Iacono et al  [43].
Downloading and Analyzing PubMed Abstracts We downloaded the PubMed 2022 baseline via https://ftp. ncbi. nlm. nih. gov/pubmed/baseline/, to obtain all PubMed indexed abstracts published by the end of 2021. We employed SentenceTransformer (https://www. sbert. net, version 2. 1. 0), a Python package, to embed the downloaded abstracts into 384-dimensional numeric vectors using a pre-trained BERT model called "all-MiniLM-L6-v2". To retrieve abstracts related to a specific gene, we employed a simple text matching approach, collecting any abstracts that mentioned the gene's name or its protein product's name or synonyms. The gene's and protein's names and synonyms were obtained from UniProt's website (https://www. uniprot. org/uniprot/?query=*&fil=reviewed%3Ayes+AND+organism%3A%22Homo +sapiens+%28Human%29+%5B9606%5D%22), selecting columns including Entry, Entry name, Protein names, Gene names (primary), and Gene names (synonym).
To assess the similarities between Reactome pathways and abstracts, we applied the same BERT model to embed the text in the Summation instances annotated for pathways and reactions into numeric vectors. To determine the similarity between a pathway and an abstract, we first calculated cosine similarities using the cos_sim() function in the SentenceTransfomer package between the abstract and the pathway and all events (pathways and reactions) annotated inside this pathway using their BERT embeddings and then took the mean of these similarities. For example, Pathway_1 has Sub_Pathway_1 annotated and Sub_Pathway_1 has a Reaction_1 annotated. To calculate the cosine similarity between Pathway_1 and Abstract_1, we calculated three cosine similarities for Pathway_1 and Abstract_1, Sub_Pathway_1 and Abstract_1, and Reaction_1 and Abstract_1 using their respective BERT embeddings, and then calculated the mean of these three similarities as the cosine similarity between Pathway_1 and Abstract_1.
This process allowed us to capture the relationship between the higher-level pathway, its subpathways, and the annotated reactions within the context of the abstract. To calculate the annotation score for a gene and a pathway, we selected at most 1,000 abstracts having highest average cosine similarities to all Reactome pathways under analyzed, calculated the cosine similarities for individual abstracts to pathways and then took the mean of these similarities as the annotation score. All code utilized in this analysis was implemented in Python and is available in our GitHub repo, https://github. com/reactome-idg/fi-networkml/tree/master/scripts/nlp.
Acknowledgements This project was supported by grants from the  U. S. National Institutes of Health  ( U01CA239069  and  U24 HG0012198 ). The content described in this publication is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. The results published here are in whole or part based upon data generated by the TCGA Research Network: https://www. cancer. gov/tcga. The Genotype- Tissue Expression  (GTEx) Project was supported by the  Common Fund of the Office of the Director of the National Institutes of Health , and by  NCI ,  NHGRI ,  NHLBI ,  NIDA ,  NIMH , and  NINDS. The data used for the analyses described in this manuscript were obtained from https://www. gtexportal. org/home/datasets at the GTEx Portal on November 11, 2019.
No significant differences were observed between the number of proteins having negative correlations and the number of proteins having positive correlations (second panel in A). Additionally, there was no significant difference in the numbers of interacting pathways for dark and non-dark Reactome annotated proteins (rightmost panel in B). Those are presumably because only gene pairs with co-expression are in the top 0. 1% were selected from the scRNA-seq dataset. P-value: ****: <= 1. 0E-04, ***: 1. 00e-04 < p <= 1. 00e-03, **: 1. 00e-03 < p <= 1. 00e-02, *: 1. 00e-02 < p <= 5. 00e-02, ns: p <= 1. 00e+00.
annotation of interacting pathways for 20 dark proteins. xlsx, in Supplemental Results. The Reactome IDG Web Portal Development The detailed annotation was provided in the file, manualWe extended the Reactome web application by adding new RESTful APIs and enhancing the original web-based pathway visualization widgets. The development of the new RESTful API followed the Spring MVC framework (https://spring. io, Release 4. 3. 10) with a MongoDB database (https://www. mongodb. com, version 4. 2. 3) as the backend to store predicted functional interactions, pairwise features, interaction pathways and all related information. We also used a MySQL database for Reactome specific information (e. g. genes in pathways and pathway diagrams). To enhance the pathway widgets, we forked the original Reactome web application projects and added new features using GWT (https://www.
gwtproject. org, version 2. 8. 2). To develop the homepage of the Reactome IDG web portal, we used Vuejs, a JavaScript framework (https://vuejs. org, version 2. 6. 12) and plugins for Vuejs, including vuetify (https://vuetifyjs. com/en/, version 2. 4. 3) for general user interfaces, vue-cytoscape (https://rcarcasses. github. io/vue-cytoscape/, 1. 0. 8) for network views, and vue-plotly (https://david-desmaisons. github. io/vue-plotly/, 1. 1. 0) for plotting. For Java related software projects, we used maven (https://maven. apache. org, version 3. 5. 0) to manage dependencies while for JavaScript project, we used npm (https://www. npmjs. com, version 6. 14. 10). All code is open sourced and available in GitHub repositories hosted at https://github. com/reactome-idg, including idg-pairwise-ws (https://github.
Non-human primate (NHP) studies are unique in translational research, especially in neurosciences and neuroimaging approaches are a preferred method for scaling cross-species comparative neurosciences. In this regard, neuroimaging database development and sharing are encouraged to increase the number of subjects available to the community while limiting the number of animals used in research. We present here a simultaneous PET/MR dataset of 20 Macaca Fascicularis structured according to the Brain Imaging Data Structure (BIDS) standards. This database contains multiple MRI sequences (anatomical, diffusion and perfusion imaging notably), as well as PET perfusion and inflammation using respectively [ 15 O]H 2 O and [ 11 C]PK11195 radiotracers. We describe the pipeline method to assemble baseline data from various cohorts and qualitatively assessed all the data using signal-to-noise and contrastto-noise ratios as well as the median of intensity.
Introduction In the framework of open science, sharing imaging databases offers specific benefits in terms of analytical tools development and validation  [1]. According to the FAIR Data Principles, the development of accessible imaging databases will help increase the reproducibility between studies  [2]. In this respect, neuroimaging scientists examine their respective policies and practices  [3]. This is also valid in pre-clinical research where in-vivo imaging of non-human primates (NHP) holds great potential in comparative biology and biomedical research  [4, 5]. NHP neuroimaging databases enables the scaling of cross-species comparative neurosciences and a better understanding of brain regions' functions in health and disease. Besides, the establishment and sharing of pre-clinical neuroimaging databases complies with the 3R principles, especially reduction through the use of imaging data-sets, and refinement considering that in-vivo imaging is non invasive.
In recent years, the NHP research community has moved forward to tackle challenges ahead, most notably the limited availability of data. The PRIMatE Data Exchange (PRIMatE-DE) initiative addresses this challenge by aggregating independently acquired NHP in vivo imaging datasets  [10]. Initially intended for magnetic resonance imaging (MRI) data, the community has worked to standardize data collection, with minimal acquisition specifications, and data architecture allowing data sharing within the framework of open science  [10]. The collaborative work allowed us to gradually improve our neuroimaging studies to human standards. In this context, the Brain Imaging Data Structure (BIDS) became the gold standard for organizing and sharing neuroimaging datasets  [1]. Nuclear imaging specialists jumped on board and published guidelines to improve the accuracy and sharing of PET data  [11, 12, 13].
Therefore, while PET neuroimaging databases exist, their counterparts in NHP are either missing or not easily available. Furthermore, considering the development of hybrid PET/MR scanners and their translational potential in NHP neuroimaging studies, there is a growing need for NHP PET/MR hybrid imaging databases. We developed a multi-modal database of Macaca fascicularis, acquired on a clinical PET/MR scanner, and constructed with MRI, [ 15 O]H 2 O and [ 11 C]PK11195 PET images to BIDS standard. The data was formatted using a self-designed python script compensating for missing metadata frequently encountered with pre-clinical and retrospective data. The designed script is available in open-access on GitHub. The entire dataset is available upon request from PRIMatE-DE repository. 1 Materials and Methods.
Animal cohort description This dataset, which includes 20 mature male cynomolgus macaques (Macaca fascicularis), was generated using baseline images from primate stroke model described by  Debatisse et al. in 2021 [14]. The experimental protocol was approved by the Animal Welfare Body of Cynbiose and the Ethics Committees of VetAgro-Sup and CELYNE CEEA n°42, and was carried-out in accordance with the European Directive 2010/63/UE and ARRIVE guidelines (Animal Research: Reporting in Vivo Experiments). [15, 16]  The subjects underwent combined PET-MRI acquisitions following anaesthesia induced by IM injection of ketamine (4 mg/kg; KetamineVR 1000, Virbac, France) and midazolam (1. 3 mg/kg; MidazolamVR 5 mg/mL, Mylan, France). Sevoflurane (1%, variable depending on the animal's anesthetic depth; SevoFloVR , Abbott Laboratories, France) maintained anaesthesia during acquisition.
PET-MRI acquisitions PET-MRI sequences were acquired on fully integrated hybrid Biograph mMR PET-MRI 3T Siemens scanner (Siemens Healthcare, Erlangen, Germany). Imaging acquisitions were conducted between 2016 and 2019 with software versions "syngo MR B20P" and "syngo MR E11". MRI sequences are described in table  1    O]H 2 O did not permit such precise radioactivity measurements. The data were reconstructed on a 256 x 256 x 127 matrix (voxel size: 0. 7 x 0. 7 x 2. 0 mm 3 ), 26 cm axial FoV using a point-spread function and OP-OSEM iterative reconstruction method including normalization as well as correction for attenuation, scatter, random counts and dead time. Prior to the PET-MRI session, a CT scan (Siemens Biograph mCT64, Siemens Healthcare, Erlangen, Germany) was obtained for each animal and used for PET attenuation correction.
BIDS formatting The data processing pipeline consisted in formatting the datasets to Brain Imaging Data Structure (BIDS)  [1]. Existing formatting tools such as dcm2bids  [17]  have limited support for missing DICOM tags frequently encountered in preclinical and retrospective datasets. Therefore, an automated python script compatible with MRI and PET acquisitions was developed. Raw images are loaded in DICOM format and converted to NIfTI format with associated metadata in json format in accordance with BIDS guidelines. An overview of the python script tasks is provided in figure  1. The script's ambition is to provide flexibility in data organization and selection when formatting raw data for sharing purposes. Because raw-data folder organization varies between structures, additional parameters are provided enabling the definition of the raw folder structure.