dataesr/scientific-paragraphs-categorization huggingface dataset:
Study of the modes conversion in the elbow The same dataset can also be post-processed to better identify the various modes to which the sensor is sensitive generated at the two junctions of the elbow. This post-processing consists in computing a two-dimensional fast Fourier transform (FFT2D), allowing us to separate propagative modes by identifying their respective wavenumbers over the frequency spectrum of the excitation signal. Here, three excitation signals were used, made of two cycles of a sine of centre frequencies 32 kHz, 64 kHz and 128 kHz filtered by a Hanning-window. Combining the three broad bandwidths allows us to follow wavenumber variations over a frequency range from 5 kHz to 175 kHz (Figure  11 ): On Fig. 11 , the curve with a positive slope corresponds to the incident mode generated by the MsS system; this confirms that the first torsional mode T(0,1) is the only mode excited using this system.
As a consequence, we should envision mechanisms as causal relationship graphs that involve all levels of biology, from genetic variation and epigenomics patterns via proteolytic processing and cellular physiology to tissue and organ (patho-) physiology. Below, we review two different classes of promising bioinformatics approaches targeting causal disease driving mechanisms in the context of neurodegenerative diseases. One strategy is to benefit from advances in genetics and in particular how to utilize current SNP databases to widen the search for putative disease-causing mechanisms. The rationale is that genetics constitutes one of the most robust and high-quality data-sets in our possession. At the other end of the spectrum, there exists a mechanistic model-driven approach, fuelled by prior knowledge from experts and text mining strategies, which collects and organizes putative mechanisms in a formal framework.
To this end, we collected published GWAS data from the US National Human Genome Research Institute (NHGRI)  [45]. At the time of our analysis (18 January 2014) NHGRI contained 38 publications, which reported 171 SNPs above a significance threshold of p > 1 ˆ10 ´6. In addition, 1280 associations were considered for our analysis obtained from literature. Resulting in a total of 2282 distinct SNPs linked to 601 genes. These 601 genes were obtained by considering 20 kb region upstream and downstream of given variant, and constituted the starting list for further analysis. LD blocks were retrieved from HaploReg  [46]  with LD threshold, r 2 = 0. 8. Next, data for chromatin state dynamics have been determined using ChIP-Seq in a number of different cell lines as reported in ENCODE (ENCODE Project Consortium) and the Roadmap Epigenome Project  [47].
This information can be used to link SNPs to epigenetic regulation in specific cell types. Cell specific chromatin states in six cell lines  [48]  were retrieved for AD or PD associated SNPs from ENCODE and Roadmap using HaploReg. Finally, pathway analysis of the SNPs data was performed using GSEA  [49]. This dataset includes several gene products, which are involved in specific metabolic and signaling pathways. Using GSEA tools signaling or metabolic pathway were retrieved and then ranked based on BH corrected p-value. Not surprisingly, we observed that most of the AD associated SNPs (>73%) were found in intronic and intergenic regions of the genome, i. e. , non-coding regions. For example, included in our analysis of the LD blocks we localized chr19: 45386467-45396144 representing the PVRL2 and TOMM40 gene region, which also had enrichment in the enhancer region suggesting regulation of brain tissue specific chromatin regulatory states in this zone.
Multiscale Cause-and-Effect Modeling and Mechanism Candidate Identification A key element of the model-driven approach towards identification of disease mechanisms in AD and PD is the use of OpenBEL, the open source version of the biological expression language  [61]. OpenBEL is a language designed to capture, represent and analyze knowledge in a computable form, which means, that the formalism of knowledge representation supports causal reasoning algorithms  [62]. OpenBEL focuses on causal and correlative relationships; use of the language is supported by a dedicated OpenBEL framework that provides essential analysis tools and algorithms  [63]. One advantage of BEL over existing modeling languages is explicit support of semantics using domain-specific ontologies, which makes the encoding of biomedical features specific to neurodegeneration feasible by utilizing resources such as Alzheimer's disease ontology  [64]  and Parkinson's disease ontology  [65].
SNPs that were associated with both, AD and type-2-diabetes mellitus (T2DM) were systematically identified and mapped to a comprehensive model of Alzheimer´s disease  [67]. Through modeling of major T2DM pathways, BEL subgraphs could be identified that overlap between AD and T2DM and that share SNPs (or LD blocks) that can be associated with genes and proteins contained in the BEL models. Graph-Mining for the Identification of Important Markers in Context: NeuroRDF. Rapid advancements in high-throughput technologies for quantifying biomolecules have fuelled a dramatic accumulation of large volumes of heterogeneous data types. To leverage such data to help unravel complex disease mechanisms necessitates appropriate standards for data integration and exchange. Semantic web technologies such as Resource Description Framework (RDF)  [74]  have been employed to publish and link heterogeneous data  [75].
The successful outcome of these initiatives and hackathons advocate the utility of RDF as a scalable infrastructure, capable of integrating a wide spectrum of heterogeneous (semi-) structured and unstructured data. Extension of domain ontologies is sometimes needed to fill the gaps between existing resources, especially as new data modalities arise, to improve linking for subsequent analysis. Harnessing the potential of RDF, Iyappan et al. [81]  have recently developed an integrated semantic model encompassing multiple, complementary molecular data types for different neurodegenerative diseases (NeuroRDF  [81] ). These data include literature-derived Protein-Protein-Interaction (PPIs) networks, MicroRNA-Target-Interactions (MTIs) networks, transcriptomic data from GEO  [82]  and ArrayExpress  [83] , and knowledge from established biological databases such as MINT  [84] , IntAct  [85] , etc.
Systematic mapping of protein interactions in different functional contexts can serve as a priori knowledge framework for the interpretation of quantitative molecular datasets such as gene expression data. A considerable resource of protein interactions involved in different biological processes can be found as unstructured text in published articles. Although, text-mining methodologies allow sensitive retrieval of different biological entities from published articles, capturing the context in which those entities are described remains inaccurate due to technological limitations. For NeuroRDF automated text mining was used to identify PPIs  [88]  and MTIs  [89]  in AD articles before manual curation to confirm the disease relevance and capture associated meta-information. The meta-information such as species, experimental technique used, tissue specificity etc.
support in investigating interactions with more granularity. Disease PPIs reflect perturbations of the molecular networks underlying normal physiology. They may highlight key regulators but are an incomplete picture of the affected mechanism which normal state interactions matched to anatomical regions can help fill-in. Therefore, again the authors integrated a PPI network representing the healthy state (derived from 21 databases) in NeuroRDF. Such integration should always be done in a very cautious way as database content often suffers from a lack of specificity and accuracy. For instance, Younesi et al. [90]  invested considerable efforts to retain highly qualified PPIs specific to the healthy human brain, which were used for correlating image-based diagnoses to their corresponding PPIs specific to brain regions.
A RDF model that integrates data for complementary molecular entities, together with carefully curated and enriched meta-data can aid research into disease patho-mechanisms. The RDF semantic query language, SPARQL, can be used to formulate common considerations that relate to a molecules biological role in disease, as queries and evaluate them systematically. The extracted features can used to objectively prioritise existing mechanistic hypotheses as well as uncover more novel etiologies. Such an approach has been employed by the authors as part of the Neuroallianz  [91]  consortium to extract a comprehensive set of gene features by querying NeuroRDF; incorporating annotations from NeuroTransDB, in order to prioritise gene products as potential biomarkers in AD. Extracted features included basic meta-analysis metrics such as median fold change; knowledge-based features such as targets of miRNAs dysregulated in AD; and metrics from the disease PPI network such as node degree and betweenness centrality.
Denoting SHCI's involvement in shared-mechanisms around the commonly reported players: APP, BACE1, PRNP and LRP1 (derived from AlzGene  [94]  database), makes it a novel candidate for further investigations. Workflow for Resource Description Framework (RDF)-mediated data and knowledge integration and the identification of "signals" in a network context. The workflow essentially identifies subgraphs in the feature network that are highly discriminating between healthy and diseased; the term "long list" describes a list of "interesting network features" that provide contextual, mechanistic insight that can be analyzed by experts in the field.
Different tools enable the identification of perturbed pathways and processes from large-scale datasets, however, their output is often limited to ranked lists of associated pathways (e. g. , based on GO annotations) without providing disease-related information or showing potential cross reactivity between pathways. In addition, the impact of differential gene expression and protein activity may vary substantially dependent on a specific tissue, cell type or a subcellular compartment. This fact is often neglected. To avoid this potential pitfall, data analysis and visualization should allow for a more specific and targeted approach, with regard to disease-related mechanisms and networks. To fulfill this Figure  3. Workflow for Resource Description Framework (RDF)-mediated data and knowledge integration and the identification of "signals" in a network context.
Different tools enable the identification of perturbed pathways and processes from large-scale datasets, however, their output is often limited to ranked lists of associated pathways (e. g. , based on GO annotations) without providing disease-related information or showing potential cross reactivity between pathways. In addition, the impact of differential gene expression and protein activity may vary substantially dependent on a specific tissue, cell type or a subcellular compartment. This fact is often neglected. To avoid this potential pitfall, data analysis and visualization should allow for a more specific and targeted approach, with regard to disease-related mechanisms and networks. To fulfill this need, the PD map, a manually curated, fully annotated knowledge repository, was established  [95].
The map describes detailed molecular mechanisms involved in the pathogenesis of PD. The PD map is based on the CellDesigner software  [96] , which is compliant to SBML (Systems Biology Markup Language) and SBGN (Systems Biology Graphical Notation)  [97]. Currently it contains more than thousand PD-related molecular interactions curated from over a thousand PD-related publications. The PD map displays molecular interactions within a cellular and subcellular context with a focus on cellular processes implicated in PD pathogenesis such as synaptic and mitochondrial dysfunction, impaired protein degradation, α-synuclein pathobiology and neuro-inflammation. It is the first freely accessible and high quality knowledge repository of molecular mechanisms contributing to the complex pathogenesis of PD  [98].
Large-scale datasets derived from different experimental approaches such as proteomics, transcriptomics or genomics (e. g. , GWAS analysis) can easily be uploaded to the PD map by any user. The differential expression is displayed on the PD map in the context of the cellular pathways and processes that are modeled in the map. The map provides disease and cellular context-related information in such a way that it is useful for data interpretation and hypothesis generation. An example for the use of the map is given by a meta-analysis of eight genes expression data-sets from post-mortem brain tissue and the mapping of the resulting differentially expressed genes onto the PD map. Different molecular processes such as autophagy, ubiquitin proteasome system and dopamine metabolism could be identified in the PD map to be affected in the diseased tissue.
Interestingly, Ataxin-3 is down regulated in brain tissue from PD patients. Both datasets derived from post-mortem brain tissue and aging are integrated in the PD map (layouts view) and can be explored by the PD map user. Due to the embedded functionalities, the possible applications of the PD map are diverse. Identification of specific pathway perturbation based on data derived from patients could support the stratification of patients. Additional knowledge, like drug targeting information is useful in particular within pharmacological investigations. The PD map can also be used to visualize and contextualize results that are deduced from metabolomics experiments or from related databases such as RECON2  [99]. To ensure the functionality of the PD map the database needs to be updated on a regular basis.
Text mining approaches in combination with expert curation are essential to digest the extensive literature and integrate valuable information into the PD map. Finally, the PD map framework is already used as a blueprint for other diseases maps. on this analysis the CREB signaling could be selected as a potential target for further investigations on the PD aetiology. One hypothesis that could be deduced from the PD map points to a potential involvement of the CREB inhibitor Ataxin-3 which is described to be involved in the neurodegenerative Machado-Joseph disease. Interestingly, Ataxin-3 is down regulated in brain tissue from PD patients. Both datasets derived from post-mortem brain tissue and aging are integrated in the PD map (layouts view) and can be explored by the PD map user.
The procedure to generate scored mechanistic hypotheses covers the following components: (a) A lateralized topological model, generated from the combination of human, monkey and rat connectivity data, is built to provide a weighted reference graph that represents brain regions (about 400 regions) and their pairwise connections (about 4000 connections); (b) The weighted graph in (a) provides the reference map for the application of a Steiner Minimal Tree (SMT) calculation  [104, 105]  that finds parsimonious solutions of paths that link an arbitrary set of brain regions, such that preference is given to connectivity edges in this order: human, monkey, then rat. Given that the SMT problem is known to be NP-complete, we allow for the application of heuristics in the context of NDD calculations  [106] [107] [108] , where deemed appropriate-for instance, these heuristics also take into account expert knowledge about the likely direction of spread of NDD pathology from central, lower nuclei towards higher neocortical grey matter; (c) For each clinical correlation data point, the most parsimonious route (or set of top-scoring routes) linking the brain regions associated with the brain-derived measurements that correlate with the clinical scores is calculated as described in (b) above, to create the correlation reference route set (CRRS); (d) The calculation of a mechanistic hypothesis for NDD, then, takes as an input combinations of clinical scores and brain-derived measurements that have been clustered from clinical study datasets.
(iii) Visualization There are a number of challenges in providing the graphical user interfaces (GUIs) in support of the navigation of multiscale, multi-modal data. To provide an anatomically-meaningful map that conveys functional information with visual immediacy, the ApiNATOMY toolkit makes use of a treemap-based interface that navigates nested tiles representing the mereotopology of lateralized brain regions. This approach is specifically tailored for the depiction of multiscale anatomy, overcoming the expressive limitations of generic software such as Cytoscape  [109]  or Spotfire  [110]. In the ApiNATOMY interactive GUI, these tiles hold (i) graphical symbols for different kinds of multi-modal measurements (e. g. , those measurements that correlate to neuropsychometric scores) taken from the brain regions that the tiles represent; as well as (ii) graphical edges representing either molecular networks derived from BEL representation of regionally-relevant molecular processes, or neural tracts that link one region with another (graphical depiction of a multi-site neural tract depicted in Figure  5 ).
The AETIONOMY Knowledge Base-An Environment for Candidate Mechanism Identification In the context of IMI project AETIONOMY  [60]  we have assembled a complex workflow for candidate mechanism identification that comprises some of the above described approaches. The knowledge base consists of a tranSMART component  [111]  (contributed and enabled through the eTRIKS  [112]  project) and additional functionalities that support model-driven (contextual) analysis of data and that makes data and knowledge completely interoperable. The AETIONOMY Knowledge Base contains highly curated data  [73]. Curated data represent molecular ("omics") entities and their interaction and clinical data (including trial data). A comprehensive, semantic framework representing and formalising knowledge about anatomy (FMA  [113] , BRCO  [114] , NIF  [115] ), about major neurodegenerative diseases (ADO  [64] , PDON  [65] , MSO  [116] ) and about assays and readouts (variables) used in clinical trials (NDD-CTO  [117] ; NIFT  [118] ; clinicaltrials.
gov [119] ) enables "shared semantics" over all scales (from the molecular via the cellular and organ level to the cohort and population level) and all entity types (ranging from genes and proteins to cognitive testing and neuro-imaging). A "model store" harbors various disease models available to the project (AD and PD models in OpenBEL; a PD map in SBML). The model store for BEL models is based on a relational database schema, which makes this type of disease models amenable for SQL-based querying and enables an unprecedented integration of models and databases. A growing number of relevant public databases (e. g. , CTD  [120] , UniProt  [121] , WikiPathways  [122] , DrugBank  [123] , HGNC  [124] , PubMed [125], Reactome  [126] , proteinAtlas  [127] , NCBI taxonomy  [128] , ChEBI  [129] , InterPro  [130] , various PPI databases  [131, 132] ) have been imported in this system; extensive mappings between these databases allows for seamless integration of models, referential databases, and data analysis results coming from the tranSMART component.
Disease models encoded in OpenBEL are being queried using the candidate gene lists (3). Subgraphs representing functional context and containing at least one of the genes identified in differential gene expression and SNP association studies are being identified. Significant SNP loci or interesting differentially expressed genes are immediately "embedded" in a functional context using this approach. Subgraphs are either linked to the toxicogenomics databases CTD (4) or mapped to UniProt protein atlas information  (7). The mapping to brain-region-specific PPIs allows us to put functional (molecular) context into an anatomical site; with high likeliness of brain-region specificity of the interaction predicted by the BEL model. The CTD mapping allows to identify targets and drugs that can be associated with the functional context in that subgraph; for drug target combinations linkouts to Clinical Trials and DrugBank provide useful information on their indication and efficacy.
The differential gene expression lists and the SNP analysis lists contain identifiers, that can be readily mapped to identifiers underlying objects in disease models. Disease models encoded in OpenBEL are being queried using the candidate gene lists (3). Subgraphs representing functional context and containing at least one of the genes identified in differential gene expression and SNP association studies are being identified. Significant SNP loci or interesting differentially expressed genes are immediately "embedded" in a functional context using this approach. Subgraphs are either linked to the toxicogenomics databases CTD (4) or mapped to UniProt protein atlas information  (7). The mapping to brain-region-specific PPIs allows us to put functional (molecular) context into an anatomical site; with high likeliness of brain-region specificity of the interaction predicted by the BEL model.
The CTD mapping allows to identify targets and drugs that can be associated with the functional context in that subgraph; for drug target combinations linkouts to Clinical Trials and DrugBank provide useful information on their represents the protein component. The internal mapping of entities represented in the subgraph to other databases allows for an identification of targets using the link to CTD. Whether these targets have already been tested in the context of neurodegenerative disease trials is being analyzed through a mapping and querying of ClinicalTrials. gov [119]. The AETIONOMY Knowledge Base integrates multiscale data and knowledge to an extend that complex mining tasks are enabled; future developments will also integrate imaging-derived features in both, BEL-based models and on the data side (by incorporation of major clinical study data such as ADNI and PPMI into the knowledge base; access to these resources will, however, be restricted in order to comply with the patient data privacy regulations that have been associated with the scientific use of these data sets).
We can use the algorithms that we have described above as a chain: each result of one step being the input for the next one: in that case each steps acts as a filter and we are restricting at each steps the number of possible results to focus to the most relevant one. We can also use them as independent steps from which results have to be collided. It is to be noticed that the second option can be realized by integrating many dimensions in one step when using the "heat-diffusion" algorithm by taking a composite heat vector: this corresponds to a holistic analysis of the modalities and scales. In addition, we are currently creating a database of mechanisms underlying neurological diseases, while having a particular emphasis on AD and PD, our approach can be extended to other neurological diseases.
This database (which is yet unpublished) is composed of around two hundred BEL subgraphs which represent relevant mechanisms in given contexts on top of which we want to add all the relevant documentation. In the near future, we obviously want to test the subgraphs-hypotheses for diseases-using real life data and we will use each mechanism of our database as a probe which can be challenged using the available datasets (e. g. , ADNI, PPMI) or clinical studies made available to the AETIONOMY project. Finally, the "classical medicine" has already brought a huge wealth of drugs to the living community. The approach advocated here can be considered as a way of thinking and is shared by others, e. g. , our PRECISEADS colleagues, fitting with the general ambition of fully entering the precision or personalized neurology.