dataesr/scientific-paragraphs-categorization huggingface dataset:
Evaluation Process We invited 5 participants with good prior knowledge of DTI data and associated techniques. Their level of experience ranges from 5 to 37 years. One participant is the head of the neuroscience lab in a major French university (37 years experience); one participant is a neuroscientist (12 years experience); two are postdocs with 5 and 10 years experience; and one is a researcher with 10 years experience. Each evaluation lasted one hour and operated as follows. First, we provided a general introduction of our context and goal. After naming DTI and tractography (which was, as outlined above, well known to the participants), we introduced the problem of data size: The input data for this visualization is a large set (hundreds of thousands) of 3D tracts that follow the main anisotropy directions in a DTI volume.
These tracts are in general locally aligned with the directions of the neural fibers. However, directly visualizing a large and densely sampled tract volume creates problems, mainly due to occlusion. We propose to address such problems by a simplified visualization technique that bundles close and same-direction tracts, so that occlusion decreases but the resulting structures still closely reflect the underlying fiber anatomy. We next introduced the six visualization techniques listed in Table  4  and illustrated them by interactively running our visualization tool. The dataset used during this presentation (and also further on in the evaluation) consists of a brain MRI scan coming from a healthy person, exhibiting no particular anatomical or physiological anomalies. As such, users should expect to see the typical structures they are familiar with from brain anatomy.
The anisotropic bundling techniques described in Sections 3. 2. 1 and 3. 2. 2 limit such problems as they constrain the bundling to follow additional data-related information (anisotropy). Still, the inherent trade-off of bundling between simplification and veracity remains. This is not a problem specific to our method-many other methods that produce simplified visualizations of DTI data share the same issue  [14, 16, 60]. Exploring how to both measure and limit deformations caused by bundling that create false insights is an interesting and important direction for future work. Evaluation: Our evaluation is inherently limited by the format chosen-inviting only professionals deeply familiar with DTI visualization, exposing them to precisely the same material, and setting a time limit of under one hour for the entire process.
The EMBL-EBI ChEMBL database references 516 past or present clinical trials for sildenafil and other PDE5A inhibitors, for example, tadalafil including several Phase IV trials to assess efficacy in reducing obesity. 11 Another notable example is the tumor necrosis factor, TNFSF11, which has been genetically associated with monogenic familial osteopetrosis  [58]  and bone mineral density  [51, 59]  and against which an inhibitor, the monoclonal antibody, denosumab, has been successfully developed and approved in the treatment of osteoporosis. GWASs also reported an association between TNFSF11 and inflammatory bowel disease suggesting denosumab as a potential therapeutic for Crohn's disease and ulcerative colitis  [60, 61]. Most GWASs have been performed on complex traits and diseases, that is, known or suspected to be under the control of many different genes (e.
By mapping these 101 loci to their genes using a combination of genomic, transcriptomic and epigenomic data, they identified 98 RA risk genes and then used protein-protein interaction (PPI) databases to identify their direct PPI partners. Twenty height proteins encoded by genes associated to RA were found to interact directly with ten proteins that were targets of already approved RA drugs and with two proteins, CDK6 and CDK4 that are targetable by drugs with other disease indications, for instance, the CDK4/CDK6 cyclin-dependent kinase inhibitor, paldociclib (breast cancer and mantle cell lymphoma) and the CDK4 inhibitor/ tumor suppressor gene p16 modulator, capridine-beta (advanced solid tumors and psoriatic disorders), two promising drug repurposing candidates for RA.
Building human diseases networks with human genetic data and network biology The construction of human disease networks is an obvious essential step of network pharmacology. Briefly, the biological network of a disease represents interactions between gene-products and cellular metabolites involved in the etiology and symptoms of that disease (see Refs. [98, 100]  for a definition). In general, the construction of the biological network of disease starts with the creation of a global molecular network derived from the results of largescale experiments that have characterized the human interactome. Examples of key resources are the Human Protein Reference Database (HPRD, http://www. hprd. org/), the Biological General Repository for Interaction Datasets (BioGRID, http://www. thebiogrid. org) and STRING (https://string-db.
org/cgi/input. pl) for protein-protein interactions, the Kyoto Encyclopedia of Genes and Genomes (KEGG, https://www. genome. jp/kegg/) and Omni-Path (http://omnipathdb. org/) for metabolic pathways; the Genotype-Tissue Expression (GTEx, https://www. gtexportal. org/home/) database is also a key resource to identify gene-regulatory interactions as well as the privately-owned Qiagen Ingenuity Pathway Analysis tool (https://www. qiagenbioinformatics. com/products/ ingenuity-pathway-analysis/). The molecular disease network is then refined by reducing the global interactome to a subpart that contains: i) the genes associated to the disease and, ii) the key cellular partners of those genes  [98]. The genes associated with the disease can be identified through functional studies and family linkage-studies, an important part of which is compiled by the Online Mendelian Inheritance in Man database (OMIM, https://www.
This dataset was then used together with a carefully curated collection of PPI databases to build a biological network connecting those genes and to search for subparts of that network significantly enriched with nominally significant genes with respect to the MS GWASs. They found that approximately 80 genes are arranged in a small protein-protein subnetwork, of which two-thirds had nominally significant p-values in independent meta-analyses of multiple MS GWASs. By performing a gene set enrichment analysis over those genes, which consists in annotating a list of genes with molecular/cellular/physiological/features (GeneOntology Terms, Functional Group, Tissue expression, etc. ) and finding features which are statistically overrepresented  [102, 103] , Zipp et al. [102]  found that a third of the 80 genes that were highly expressed in immune-related cell types and half being highly expressed in the CNS, some of which, such as S1PR1, the gene that encodes for the Sphingosine-1-Phosphate Receptor 1 are known targets for MS.
Greene et al. [104]  developed a similar approach, termed 'NetWAS', which combines nominally significant genes from GWAS and tissue-specific networks built from protein-protein interaction databases, gene expression and transcription factor data. Using GWASs of hypertension, their method yielded tissue-specific networks (i. e. a list of GWASs nominally associated genes and their PPI partners in a specific tissue) that were highly enriched for the GeneOntology term 'regulation of blood pressure', at a higher frequency than the GWAS-alone top genes, and which contained an overrepresented proportion of targets of antihypertensive drugs from DrugBank (https://www. drugbank. ca/) and three other drug databases. NetWAS and several recent upgrades are now available as an online tool 'GIANT2' (http://giant-v2.
A similar network-based combinational repurposing approach to Alzheimer's disease and Parkinson's disease, two complex neurological diseases, led to the identification of a synergic low-dose combination of repurposed baclofen and the acamprosate as highly promising based on experiments performed in cellular and animal models of those diseases  [124] [125] [126]. Following a similar approach and using GWASs,  Cheng et al. [127]  recently focused on hypertension and cancer to predict efficient pairwise drug combinations based on systems biology analyses and especially on the overlap between protein-protein networks of disease genes and FDA-approved drug targets. Genes were linked to diseases using eight different sources, most of which rely on GWASs and other types of genetic studies, including the GWAS catalog, DisGeNET, a comprehensive database of disease-gene associations which compiles results from GWAS, modelorganism studies and other inputs from the scientific literature (http://www.
disgenet. org/) and the OMIM catalog. A key finding of Cheng et al. was that successful FDA-approved pairwise combinations are to be found when the subnetworks of both drugs overlap different parts of the disease subnetwork (which they term 'complementary exposure'), which increases therapeutic efficacy while limiting toxicity. Collectively, these studies demonstrate the feasibility of a genetics-informed network-based repurposing and combinational repurposing approach in the treatment of a variety of diseases: pathogen-induced or not, rare or common, monogenic, or complex. This provides a global picture of a productive research model for combinational repurposing may look (Figure  3 ). From this, it is clear that a genetics and systems biology approach to combinational repurposing requires a combination of the power of big genetic data with network biology resources and methods, from protein to drug database inputs.
Among them, deep learning-based natural language processing (NLP) method which allows better text mining of the scientific literature and patient medical records. As an example, Rajkomar et al. [133]  analyzed more than 200 000 electronic health records from two US academic medical centers that represented more than 45 million data points. They predicted mortality, discharge diagnosis, length of stay, and readmission using deep learning NLP methods with high accuracy (i. e. an area under the operator curve between 0. 75 and 0. 94). The public-private OpenTargets platform has recently released 'LINK', a knowledge graph tool of the Open Targets literature built using NLP (https://link. opentargets. io/). This AI-powered tool provides visually explorable links between genes, diseases, and drugs based on all available PubMed abstracts.
Recently, Zong et al. [134]  adapted the DeepWalk algorithm, tested initially on social network data such as Flickr and YouTube, to predict novel associations between drugs and targets based on the exploration of a biological network built from drugs, diseases, and PPI databases. When the association between a target and a drug is unknown, for example, for new chemical entities, it is also possible to use deep learning in the form of a convolutional neural network to predict the docking of a protein with its ligand  [135].
The disease network is then overlapped with databases of available drugs. When multiple repurposable drugs are identified, it is possible to combine them and test their synergistic as well as side effects in animal models. The animal studies-validated combo (compared to the drugs alone) can then be tested in human trials. human genetics, network biology, and network polypharmacology as a successful scheme for the next generation of drug repurposing efforts. Essential features for the next generation of drug repurposing approaches are leveraging human genetic data using GWASs to combine with the construction of the biological networks of human diseases to identify synergistic combinations of repurposable drugs. These can restore homeostasis to treat dysfunctional molecular pathways, which underpin most diseases while minimizing the side effects of these repurposed combinational treatments via the use of low doses, which remain efficient because of their synergy (Table  1 ).
Acknowledgements We thank  Jan Bouaziz ,  Qibo Sun ,  Florian Leverve ,  Marina Ossokina ,  Sarra Seninet ,  Lydie Boussicault ,  Anthony Brureau ,  Mohamed Jabri ,  Fabrice Glibert ,  Celia Lokmane ,  Antoine Chouteau ,  Peter Schmitt ,  Julien Laffaire , as well as our colleagues at  GeneNet Pharmaceuticals Co. , Ltd and at Tasly Pharmaceutical Group Co. , Ltd  for fruitful discussions.
Part 1: Clinical profiles HBN cohort and participants The Healthy Brain Network (HBN) cohort initiative within the Child Mind Institute began in 2015 with the goal of collecting brain imaging, cognitive/behavioral, and genetic data for 10,000 children and adolescents (5-21 years old) to investigate the heterogeneity behind neuropsychiatric and neurocognitive development  34. It comprises a population of individuals at-risk for developing psychiatric disorders and typically developing participants. Subjects were recruited through flyer dissemination and subsequently assessed on clinical questionnaires at three sites in New York City, USA: Staten Island, Mobile Van, Midtown. In the status of the HBN cohort, consensus diagnostics are not available for most of the subjects enrolled; however, this does not preclude the possibility to carry out our dimensional study since subjects were not selected based on an ASD diagnosis, but rather on the presence of behavioral constructs relevant in the field of ASD.
Behavioral assessments One of the most prominent dimensions in ASD patients is social impairment. Here, we used data from the widely used 65-item parent social responsiveness scale (SRS) as a quantitative measure of clinical autistic traits, making it the central variable of interest in our study and in fact the score from which we separate out our autistic traits individuals. The SRS has been proven as a valid measure of autistic traits and thus has been used as a measure of autistic traits (for the purpose of understanding ASD) in several behavioral, genetic, and neuroimaging studies  3, 28, 32, 33, [40] [41] [42] [43] [44]. Though not a diagnostic tool, the SRS exhibits high inter-rater and cross-cultural reliability, and correlates greatly with the Autism Diagnostic Observation Schedule (ADOS) and the Autism Diagnostic Interview-Revised (ADI-R) diagnostic assessments for ASD from the DSM-5, making it a robust measure to use in the dimensional study of ASD behaviors  3, 33, 40, 45.
Python version 2. 7 and R 3. 4. 0 ). T1-weighted images were processed using the Free-Surfer software version 6. 0. 0 (https://surfer. nmr. mgh. harvard. edu/). For more information on precise methods of image analysis and the construction of anatomical information for each individual done by this software, refer to  52, 53. Briefly, the FreeSurfer analysis stream includes intensity normalization, skull stripping, and segmentation of gray (pial) and white matter surfaces  52. Subsequent tessellation, as well as various topology corrections and inflation, leads to 3D meshes of cortical surfaces in different resolutions. Our work is based on a tessellation with ~160,000 vertices per hemisphere and used the FreeSurfer fsaverage template. We focused on three morphological measures of which the processing stream created vertex-wise maps for analysis: cortical thickness (CT), surface area (SA), and gyrification (lGI).
Most importantly, within the current investigation gender differences were controlled for in the neuroimaging analysis. This study has several potential limitations. Firstly, though the SRS included a repetitive behavior subscale, it would have been interesting to include an independent repetitive behavior component within the clustering analysis. Concerning the unsupervised clustering, inherent limitations include the somewhat arbitrary determination of the number of clusters, and difficulties to reproduce the same partitioning in another dataset. Also, it is challenging to account for covariates in unsupervised clustering analyses. Alternative approaches could also have been applied on this dataset that would prove interesting in future studies including clustering based on SRS subscales (with subsequent study of behavioral and morphological traits), or clustering on a broader range of scales (not only pertaining to behaviors central in ASD) with subsequent isolation of subgroups high in SRS.
16 ] [19] [20]  Recent efforts further expand this work to link multiomics data with both radiography and histopathology imaging,  21, 22  toward developing methods for imaging genomics. These large archives of multimodal and multiscale data sources provide complementary insights into the mechanistic basis of cancer, toward better diagnosis and treatment, and open unprecedented modeling opportunities to link multiomics data with clinical and imaging phenotypes. As a solution to imaging genomics, we introduce the Imaging-AMARETTO software tools to systematically interrogate networks derived from multiomics data for relevance to imaging biomarkers of clinical outcomes. We demonstrate the utility of these imaging genomics tools by integrating three patient brain tumor databases, including the TCGA GBM and LGG and the IvyGAP GBM cohorts.
METHODS The *AMARETTO Software Architecture The *AMARETTO framework (Fig  1 ) provides tools for network-based fusion of multiomics, clinical, and imaging data within and across multiple patient studies of cancer. Specifically, this framework offers modular and complementary solutions to multimodal and multiscale aspects of network-based modeling within and across studies of cancer through the AMARETTO and Community-AMARETTO algorithms, respectively. In this work, we present an imaging genomics software toolbox that comprises the newly formulated Imaging-AMARETTO and Imaging-Community-AMARETTO algorithms that together facilitate interpretation of patient-derived multiomics networks for their relevance to radiography and histopathology imaging biomarkers of clinical outcomes. Resources of the *AMARETTO  23  software toolbox are available through GitHub, Bioconductor, R Jupyter Notebook, GenePattern, GenomeSpace, and GenePattern Notebook.
Imaging-AMARETTO From a User's Perspective The workflow for multiomics, clinical, and imaging data fusion includes utilities to learn networks from individual patient cohorts using Imaging-AMARETTO and to link networks across multiple related patient cohorts using Imaging-Community-AMARETTO. Together, these workflows allow users to integrate patient tumor-derived multiomics or transcriptional profiles with clinical and molecular characteristics and radiography and histopathology imaging features within and across related patient cohorts. The Imaging-AMARETTO source code in R is available from GitHub. 24 An R Jupyter Notebook that.
CONTEXT Key Objective Imaging-AMARETTO provides software tools for imaging genomics through multiomics, clinical, and imaging data fusion within and across multiple patient studies of cancer, toward better diagnostic and prognostic models of cancer. Knowledge Generated Our network-based imaging genomics tools serve as powerful hypothesis generators that facilitate the testing of known hypotheses and uncovering of novel hypotheses for follow-up with experimental validation studies. Our case study that integrated multiple studies of brain cancer illustrates how Imaging-AMARETTO can be used for imaging diagnostics and prognostics by interrogating multimodal and multiscale networks for imaging biomarkers to identify their clinically relevant underlying molecular mechanisms.
Relevance We anticipate that our Imaging-AMARETTO tools for network-based fusion of multiomics, clinical, and imaging data will directly lead to better diagnostic and prognostic models of cancer. In addition, our tools for network biology and medicine will open new avenues for drug discovery by integrating pharmacogenomics data into these networks, toward better therapeutics of cancer. provides stepwise guidelines for running the source code directly from GitHub for its application to brain cancer is available from Google Colaboratory. 25 aging-AMARETTO supports multiple workflows: a patient cohort with only transcriptional profiles and a patient cohort with multiomics profiles. When only RNA-seq data are available for a cohort, a predefined list of candidate driver genes is required, which can be selected or uploaded by the user.
Predefined lists of known drivers are available for collections of transcription factors (TFutils  26, 27  and Molecular Signatures Database [MSigDB] 28,29 C3) and cancer driver genes (COSMIC  30  Cancer Gene Census, MSigDB Hallmark). When genetic or epigenetic data are also available, they can help to guide the selection of candidate driver genes. Potential cancer drivers are identified as somatic recurrent cancer aberrations from genetic and epigenetic data sources using GISTIC  31  and MethylMix. 32, 33 The GISTIC algorithm is used to identify copy number amplifications and deletions from DNA copy number variation data. The MethylMix algorithm is used to identify hyper-and hypomethylated sites from DNA methylation data. The user also specifies the number of regulatory modules to be learned from the data and the percentage of most varying genes to be included in the analysis.
Networks of regulatory modules are inferred from RNA-seq data using an iterative optimization procedure. The algorithm  [34] [35] [36] [37]  starts with an initialization step that clusters the genes into modules of co-expressed target genes. For each of these modules, we learn the regulatory programs as a linear combination of candidate driver genes that best predict their target genes' expression using Elastic Net-regularized regression. 38 5] [36] [37]  The algorithm iterates over these two steps until convergence. This analysis generates a network of regulatory modules, defined as a group of target genes collectively activated or repressed by their associated drivers.
Downstream Utility for Interpreting Clinical and Experimental Outcomes We developed several downstream utilities that facilitate interpretation of the Imaging-AMARETTO networks, including functional characterization, driver validation, clinical correlation, and imaging association. To functionally characterize modules and communities, we provide signatures from known gene sets databases (MSigDB) that can be augmented with user-defined signatures, such as immune cell,  39, 40  stromal cell,  45  and stemness  [41] [42] [43]  signatures. Regulatory modules and communities are assessed for enrichment in these known functional categories (hypergeometric test). To validate the predicted drivers as regulators of their targets in modules and communities, we assess whether activator or repressor drivers have a direct or indirect impact on their targets using experimental genetic perturbation data.
Target Genes Expression Phenotype Associations Driver Genes Expression AHR STAT3 STAT3 STAT3 FIG 2. Imaging-AMARETTO predicts STAT3 and AHR as known drivers of tumor-associated microglia and macrophage mechanisms in low-grade glioma (LGG). These heat maps present module 125 from The Cancer Genome Atlas (TCGA) LGG cohort that is a member of community 5 shared across the three cohorts (TCGA glioblastoma multiforme [GBM], Ivy Glioblastoma Atlas Project GBM, and TCGA LGG). For all patient-derived samples (rows), the heat maps show driver genes' multiomics profiles (columns), including DNA methylation and RNA gene expression data (left panels); target genes' (columns) RNA gene expression levels (middle panel); and relevant biomarkers (columns), including clinical and molecular and imaging phenotypes (right panels).