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| Name | AETIONOMY |
|---|---|
| Long Name | Organising mechanistic knowledge about neurodegenerative diseases for the improvement of drug development and therapy |
| Description | Organising mechanistic knowledge about neurodegenerative diseases for the improvement of drug development and therapy. Today, diseases are defined largely on the basis of their symptoms, yet while two patients may share the same diagnosis, the underlying causes of their symptoms may be very different. This means that a treatment that works in one patient may prove ineffective in another. There is now broad recognition that a new approach to disease classification is needed, and that is where the AETIONOMY project comes in. It will pave the way towards a new approach to the classification of neurodegenerative diseases, particularly Alzheimer’s and Parkinson’s diseases, thereby improving drug development and increasing patients’ chances of receiving a treatment that works for them. |
| Objectives | 1. The generation of a publicly accessible knowledge base with mechanism-based taxonomies for Alzheimer's disease (AD) and Parkinsonism (PD). The knowledge base combines curated relevant omics-data, disease models for AD and PD, and dedicated curation and analysis services. Partners Univ. of Luxembourg and Fraunhofer SCAI will team up to make this knowledge base sustainable and will maintain it for another 5 years after the end of public funding. 2. A recruitment of more than 400 patients has been executed for analyses. Additionally, virtual patient cohorts for AD and PD have been generated. An initial, clinical validation of the mechanism-based taxonomies for AD and PD has been made and the proof, that the mechanism-based taxonomy can be used for patient subgroup and target/biomarker identification. 3. The taxonomy has been shared with regulatory authorities to ensure consideration of the mechanism-based taxonomy in future development of regulatory requirements. The mechanism-based taxonomy will be promoted in the researcher community to facilitate independent qualification and validation of results achieved in the course of the AETIONOMY project. Public awareness of the project objectives and the problem-solving approach taken in AETIONOMY have been generated, dissemination of project results has reached out to global stakeholders, including patient organisations, the European citizens funding the project, and the political and administrative bodies involved in this IMI project. |
| Website | https://www.aetionomy.eu/ |
| Start date | 01-01-2014 |
| End date | 31-12-2018 |
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| Name | Projects | Type of institution | Country | |
|---|---|---|---|---|
| Alzheimer Europe | EPAD AMYPAD MOPEAD AETIONOMY EMIF RADAR-AD ROADMAP NEURONET Pharma-Cog EPND | Patient/carers organisation | Luxembourg | |
| Sanofi-Aventis Recherche & Developpement | EPAD EQIPD AETIONOMY IM2PACT PHAGO NEURONET Mobilise-D IDEA-FAST EPND | EFPIA | France | |
| Novartis Pharma AG | EPAD IMPRiND EQIPD AETIONOMY IM2PACT PRISM RADAR-AD ROADMAP Mobilise-D Pharma-Cog EPND | EFPIA | Switzerland | |
| UCB Biopharma SPRL | EPAD EQIPD AETIONOMY EMIF PD-MitoQUANT RADAR-CNS IDEA-FAST Pharma-Cog EPND | EFPIA | Belgium | |
| Erasmus Universitair Medisch Centrum Rotterdam | PRISM ADAPTED AETIONOMY EMIF ROADMAP EPAD IDEA-FAST | Academia | Netherlands | |
| Consorci Institut d'Investigacions Biomèdiques August Pi I Sunyer | AETIONOMY Pharma-Cog | Academia | Spain | |
| Fraunhofer Gesellschaft Zur Foerderung Der Angewandten Forschung EV | EPAD AETIONOMY PHAGO RADAR-AD | Academia | Germany | |
| Fundacio Barcelonabeta Brain Research Center | EPAD AMYPAD AETIONOMY | Academia | Spain | |
| Gottfried Wilhelm Leibniz Universitaet Hannover | AETIONOMY | Academia | Germany | |
| Institut Du Cerveau Et De La Moelle Épinière | AETIONOMY PD-MitoQUANT | Academia | France | |
| Karolinska Institutet | EPAD AMYPAD MOPEAD AETIONOMY EMIF RADAR-AD | Academia | Sweden | |
| Universitaetsklinikum Bonn | AETIONOMY ADAPTED PHAGO | Academia | Germany | |
| Université D'Aix-Marseille | AETIONOMY Pharma-Cog | Academia | France | |
| Pharmacoidea Fejleszto Es Szolgaltato Kft | IM2PACT AETIONOMY | SME | Hungary | |
| Université du Luxembourg | AETIONOMY EPND | Academia | Luxembourg | |
| Boehringer Ingelheim International Gmbh | PRISM EPAD EQIPD AETIONOMY EMIF Pharma-Cog PRISM2 | EFPIA | Germany |
| WP number | Description | Project | |
|---|---|---|---|
| WP1 | Governance & Communication: All management activities, all legal and organisational aspects of the project and all communications are being dealt with in WP1. | AETIONOMY | |
| WP2 | Knowledge & Data Management: Work Package 2 deals with all data and knowledge management activities including the design, implementation and operation of the AETIONOMY knowledge base. | AETIONOMY | |
| WP3 | Knowledge Integration & data mining pipeline for hypothesis generation: WP3 executed taxonomy construction, knowledge modelling, data- and graph-mining and hypotheses generation. | AETIONOMY | |
| WP4 | Ethical & Legal Governance: WP4 contains all activities in the ethical and legal context. | AETIONOMY | |
| WP5 | Clinical Validation: In WP5, the clinical studies for the validation of the mechanism-based taxonomy were organised. | AETIONOMY |
| Deliverable number | Title | Project | Submission date | Link | Keywords | |||||
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| Title | First author last name | Year | Project | Link | Keywords | |
|---|---|---|---|---|---|---|
| PyBEL: a Computational Framework for Biological Expression Language | Hoyt | 2017 | AETIONOMY | https://doi.org/10.1093/bioinformatics/btx660 | Informatics research paper, text mining, software, knowledge networks, python | |
| Systematic Analysis of GWAS Data Reveals Genomic Hotspots for Shared Mechanisms between Neurodegenerative Diseases | Naz | 2017 | AETIONOMY | https://doi.org/10.4172/2161-0460.1000368 | Clinical research paper, Alzheimer's disease, Parkinson's disease, GWAS, LD (Linkage Disequilibrium), shared genetic loci, genetic variants, shared pathology, neurodegenerative diseases | |
| Comorbidity analysis between Alzheimer's disease and Type 2 Diabetes Mellitus based on shared pathways and the role of T2DM drugs | Karki | 2017 | AETIONOMY | http://dx.doi.org/10.3233/JAD-170440 | Informatics research paper, clinical research, Alzheimer’s disease, OpenBEL, comorbidity, disease mechanisms, disease modeling, metformin, diabetes | |
| Of mice and men: comparative analysis of neuro-inflammatory mechanisms in human and mouse using cause-and-effect model | Kodamullil | 2017 | AETIONOMY | http://dx.doi.org/10.3233/JAD-170255 | Informatics research paper, Alzheimer’s disease, biology, mechanism, human, mice, models of disease, neuroinflammation | |
| Neuroimaging Feature Terminology (NIFT): a controlled terminology for the annotation of brain imaging features | Iyappan | 2017 | AETIONOMY | http://dx.doi.org/10.3233/JAD-161148 | Informatics research paper, clinical research, Alzheimer’s disease, annotation, brain, neuroimaging; terminology | |
| Multimodal Mechanistic Diseases (NeuroMMSig): a web server for mechanism enrichment | Dominog-Fernandez | 2017 | AETIONOMY | http://dx.doi.org/10.1093/bioinformatics/btx399 | Informatics research paper, clinical research, basic research, neurodegenerative disease, knowledge base, database, mechanisms | |
| Tracing investment in drug development for Alzheimer disease | Kodamullil | 2017 | AETIONOMY | https://doi.org/10.1038/nrd.2017.169 | Review, Alzheimer's disease, drug development, candidates, biomarkers, Industry | |
| Reasoning over genetic variance information in cause-and-effect models of neurodegenerative disease | Naz | 2016 | AETIONOMY | http://dx.doi.org/10.1093/bib/bbv063 | Informatics research paper, clinical research, Alzheimer’s disease, BEL model, GWAS, causal reasoning, cause-and-effect, genetic variants | |
| NeuroRDF: semantic integration of highly curated data to prioritize biomarker candidates in Alzheimer's disease | Iyappan | 2016 | AETIONOMY | http://dx.doi.org/10.1186/s13326-016-0079-8 | Informatics research paper, Alzheimer's disease, Data curation, Data harmonization, Data integration, Disease modeling, Neurodegenerative diseases, RDF, Semantic web | |
| Toward a Pathway Inventory of the Human Brain for Modeling Disease Mechanisms Underlying Neurodegeneration | Iyappan | 2016 | AETIONOMY | http://dx.doi.org/10.3233/JAD-151178 | Informatics research paper, clinical research, Alzheimer’s disease, disease mechanism, text mining, disease modeling, neurodegeneration, pathway terminology | |
| DNA methylation in Parkinson's disease | Wüllner | 2016 | AETIONOMY | https://doi.org/10.1111/jnc.13646 | Review article, Parkinson's disease, aging, alpha‐synuclein, DNA methylation, neurons, epigenetics, expression regulation | |
| Is dementia research ready for big data approaches? | Hofmann-Apitius | 2015 | AETIONOMY | http://dx.doi.org/10.1186/s12916-015-0367-7 | Commentary, Alzheimer's disease, dementia, Big data, Data interoperability, Semantic harmonization, Disease modeling, Data mining, Disease mechanisms | |
| Towards the taxonomy of human disease | Hofmann-Apitius | 2015 | AETIONOMY | http://dx.doi.org/10.1038/nrd4537 | Commentary, Biomarkers, disease mechanisms, Diseases of the nervous system, Drug development, Drug discovery, Neurological disorders | |
| Exploring novel mechanistic insights in Alzheimer’s disease by assessing reliability of protein interactions | Malhotra | 2015 | AETIONOMY | http://dx.doi.org/10.1038/srep13634 | Informatics research paper, Alzheimer's disease, text mining, disease mechanisms, interaction networks, platform | |
| PDON: Parkinson’s disease ontology for representation and modeling of the Parkinson’s disease knowledge domain | Younesi | 2015 | AETIONOMY | http://dx.doi.org/10.1186/s12976-015-0017-y | Informatics research paper, Parkinson’s disease, ontology, disease modeling, knowledge engineering | |
| Computable cause-and-effect models of healthy and Alzheimer's disease states and their mechanistic differential analysis | Kodamullil | 2015 | AETIONOMY | http://dx.doi.org/10.1016/j.jalz.2015.02.006 | Informatics research paper, Alzheimer's disease, OpenBEL, APP, text mining, network model, Alzheimer's disease model, Neurotrophin signaling, Type 2 diabetes mellitus | |
| A call to reform the taxonomy of human disease | Kola | 2015 | AETIONOMY | http://dx.doi.org/10.1038/nrd3534 | Commentary, Disease genetics, Drug discovery, Taxonomy | |
| Biophysical constraints on the evolution of tissue structure and function | Hunter | 2014 | AETIONOMY | http://dx.doi.org/10.1113/jphysiol.2014.273235 | basic science research paper, physiology, molecular transport, endothelial, vessel, interaction networks, biophysics | |
| White matter changes in preclinical Alzheimer's disease: a magnetic resonance imaging-diffusion tensor imaging study | Molinuevo | 2014 | AETIONOMY | http://dx.doi.org/10.1016/j.neurobiolaging.2014.05.027 | Clinical research paper, Alzheimer's disease, Diffusion tensor imaging, Cerebrospinal fluid, Cognition, Diagnosis, Preclinical phases | |
| CSF microRNA Profiling in Alzheimer’s Disease: a Screening and Validation Study | Dangla-Valls | 2017 | AETIONOMY | https://doi.org/10.1007/s12035-016-0106-x | Clinical research paper, Alzheimer’s disease, microRNAs, Biomarkers, Cerebrospinal fluid, miR-125b, miR-222 | |
| Characterization and clinical use of inflammatory cerebrospinal fluid protein markers in Alzheimer’s disease | Brosseron | 2018 | AETIONOMY | https://doi.org/10.1186/s13195-018-0353-3 | Clinical research paper, Alzheimer’s disease, MCI, CSF Biomarker, Inflammation, Discriminative power | |
| Bioinformatics Mining and Modeling Methods for the Identification of Disease Mechanisms in Neurodegenerative Disorders. | Hofmann-Apitius | 2015 | AETIONOMY | https://doi.org/10.3390/ijms161226148 | Review article, bioinformatics, data integration, disease models, genetics, graphical models, knowledge-based modelling, mechanism-identification, multiscale, neurodegeneration | |
| Multicenter Alzheimer's and Parkinson's disease immune biomarker verification study | Brosseron | 2019 | AETIONOMY | https://doi.org/10.1016/j.jalz.2019.07.018 | Alzheimer's disease, Parkinson's disease, Mild cognitive impairment, Cerebrospinal fluid, Biomarker, Inflammation, Amyloid, Tau, Aging, Multicenter, | |
| PathMe: merging and exploring mechanistic pathway knowledge | Domingo-Fernández | 2019 | AETIONOMY | https://doi.org/10.1186/s12859-019-2863-9 | Bioinformatics, Pathways, Database integration, Network analysis, Biological networks, Biological expression language | |
| Quantifying mechanisms in neurodegenerative diseases (NDDs) using candidate mechanism perturbation amplitude (CMPA) algorithm | Karki | 2019 | AETIONOMY | https://doi.org/10.1186/s12859-019-3101-1 | Alzheimer’s disease, Parkinson’s disease, Mitochondrial dysfunction, Aggregation of neurofibrillary tangles, OpenBEL | |
| Re-curation and Rational Enrichment of Knowledge Graphs in Biological Expression Language | Tapley Hoyt | 2019 | AETIONOMY | https://doi.org/10.1093/database/baz068 | ||
| Contribution of Syndecans to Cellular Internalization and Fibrillation of amyloid-β(1-42) | Letoha | 2019 | AETIONOMY | https://doi.org/10.1038/s41598-018-37476-9 | ||
| CSF glial biomarkers YKL40 and sTREM2 are associated with longitudinal volume and diffusivity changes in cognitively preserved individuals | Falcon | 2019 | AETIONOMY | https://doi.org/10.1016/j.nicl.2019.101801 | TREM2, YKL40, Preclinical Alzheimer's disease, Longitudinal analysis, Mean diffusivity | |
| Epigenetic Analysis in Human Neurons: Considerations for Disease Modeling in PD | de Boni | 2019 | AETIONOMY | https://doi.org/10.3389/fnins.2019.00276 | Parkinson’s disease, epigenetics, human, iPSC, neurons, stem cells | |
| Realistic simulation of virtual multi-scale, multi-modal patient trajectories using Bayesian networks and sparse auto-encoders | Sood | 2020 | AETIONOMY | https://doi.org/10.1038/s41598-020-67398-4 | Drug development, translational research | |
| The Impact of Pathway Database Choice on Statistical Enrichment Analysis and Predictive Modeling | Mubeen | 2019 | AETIONOMY | https://doi.org/10.3389/fgene.2019.01203 | Pathway enrichment, benchmarking, databases, machine learning, statistical hypothesis testing | |
| Integration of Structured Biological Data Sources using Biological Expression Language | Hoyt | 2019 | AETIONOMY | https://doi.org/10.1101/631812 | Data Integration, Semantic Web, Biological Expression Language, Knowledge graphs | |
| Cerebrospinal Fluid Levels of Kininogen‐1 Indicate Early Cognitive Impairment in Parkinson's Disease | Markaki | 2020 | AETIONOMY | https://doi.org/10.1002/mds.28192 | cognition, kininogen‐1 ,neurodegeneration, Parkinson's disease | |
| Towards a European health research and innovation cloud (HRIC) | Aarestrup | 2020 | AETIONOMY | http://dx.doi.org/10.1186/s13073-020-0713-z | data, health, metadata, sharing, access, patient, cloud, privacy, innovation, standards, | |
| ANMerge: A Comprehensive and Accessible Alzheimer’s Disease Patient-Level Dataset | Colin | 2020 | AETIONOMY | http://dx.doi.org/10.3233/JAD-200948 | AddNeuroMed, Alzheimer’s disease, biomarkers, cohort analysis, cohort studies, data-driven science, dataset, dementia, genome wide association studies, magnetic resonance imaging, multimodal | |
| Differences in cohort study data affect external validation of artificial intelligence models for predictive diagnostics of dementia - lessons for translation into clinical practice | Colin | 2020 | AETIONOMY | https://doi.org/10.1007/s13167-020-00216-z | Predictive preventive personalized medicine (3 PM/PPPM), Disease risk prediction, Cohort data, Model validation, Machine learning, Disease modeling, Artificial intelligence, Individualized patient profiling, Interdisciplinary, Multiprofessional, Risk modeling, Survival analysis, Bioinformatics, Alzheimer’s disease, Neurodegeneration, Precision medicine, Cohort comparison, Health data, Medical data, Data science, Translational medicine, Digital clinic, Propensity score matching, Sampling bias, Model performance, Dementia | |
| Clustering of Alzheimer’s and Parkinson’s disease based on genetic burden of shared molecular mechanisms | Fröhlich | 2020 | AETIONOMY | https://dx.doi.org/10.1038/s41598-020-76200-4 | patients, Parkinson's disease, Alzheimer's disease, mechanisms, patient, cluster, clusters, clustering, genes, Drug Repurposing, computational science, drug development, genetics research, translational research, Multidisciplinary, [SDV]Life Sciences [q-bio], R, Science, Q, Neurology, medicine.medical_specialty, medicine, Computational biology, Apolipoprotein E, Parkinson's disease, medicine.disease, Mechanism based, Disease, Cluster analysis, Subtyping, Precision medicine, business.industry, business |
| Title | Description | Type | Project | |
|---|---|---|---|---|
| AETIONOMY PD cohort | The AETIONOMY PD study was a multi-centre, cross-sectional clinical study that recruited participants from 6 sites across 3 European countries, aiming to validate the mechanism-based taxonomies generated by the AETIONOMY project. Total n=405 (clinical data), including n=25 with genetic PD, n=251 idiopathic PD, n=39 at risk of PD, n=90 healthy controls. MRI imaging is available for 30 participants, see 'Bio Samples' for list of biological samples and specimens available from the cohort. Data from a multi-omics approach were also collected. For more information, please see: |
cohort-clinical-aetionomy-7 | AETIONOMY | |
| Clinical, neuroimaging and -omics datasets from AETIONOMY PD study | The AETIONOMY PD study generated clinical, neuroimaging and -omics datasets from n=405 participants (clinical data), including n=25 with genetic PD, n=251 idiopathic PD, n=39 at risk of PD, n=90 healthy controls. MRI imaging is available for 30 participants. For more information, please see: |
dataset-clinical-aetionomy-8 | AETIONOMY | |
| DNA, CSF, plasma, serum and fibroblasts samples from the AETIONOMY PD study | The AETIONOMY PD study was a multi-centre, cross-sectional clinical study that recruited participants from 6 sites across 3 European countries, aiming to validate the mechanism-based taxonomies generated by the AETIONOMY project. The AETIONOMY PD study generated DNA (n=396), CSF (n=99), plasma (n=391), serum (n=391) and fibroblast samples (n=160) from the 405 participants recruited to the AETIONOMY PD cohort study. Sample sharing requests should be addressed to Jean-Christophe Corvol at the ICM in Paris. For more information, please see: |
biological-samples-clinical-aetionomy-2 | AETIONOMY | |
| AData(Viewer) | Data collected in cohort studies lay the groundwork for a plethora of Alzheimer’s disease (AD) research endeavors. ADataViewer lets you explore this AD data landscape and identify cohort datasets that suit your research needs. We accessed and curated major AD cohort datasets in a purely data-driven manner with the aim of 1) characterizing their underlying data, 2) assessing the quantity and availability of data, and 3) evaluating the interoperability across these distinct cohort datasets. All displayed results are based on the data that were shared and made accessible to the curators. https://adata.scai.fraunhofer.de/
|
platform-non-clinical-aetionomy-7 | AETIONOMY | |
| NeuroMMSig server | The AETIONOMY NeuroMMSig server is a knowledge base representing essential pathophysiology mechanisms of neurodegenerative diseases. Together with dedicated algorithms, this knowledge base forms the basis for a “mechanism-enrichment server” that supports the mechanistic interpretation of multiscale, multimodal clinical data. For more information please visit: http://neurommsig.scai.fraunhofer.de/ https://academic.oup.com/bioinformatics/article/33/22/3679/3884654 |
ontologies-non-clinical-aetionomy-1 | AETIONOMY | |
| In silico model of neurodegenerative disease mechanisms | AETIONOMY has used information retrieval and data mining to generate hypotheses (candidate mechanisms for both diseases AD/PD) based on the AETIONOMY knowledge database. Analyses performed by scientific research partners confirmed the importance of several biological pathways in the pathogenesis of AD/PD also providing further details on the pathways and biomarkers involved. Together, the in silico modelling and in vitro confirmation studies generated 7 new disease mechanisms, which have been selected for validation using AETIONOMY clinical study samples. For more information, please visit: |
disease-model-non-clinical-aetionomy-2 | AETIONOMY | |
| Stratification Algorithms | AETIONOMY developed algorithmic approaches that allow for modeling patient-level data, enabling the identification of pathophysiological mechanisms linked to specific patient subgroups. The algorithms can be either based on unsupervised clustering approaches or bayesian network representations than include variational autoencoder neural networks to represent complex multiscale data over time. These algorithms can be used to test the prevalence of disease mechanisms in patient subgroups; future work will make pathophysiology graphs directly testable in the context of neural network representations of longitudinal patient-level (cohort) data. For more information please visit: |
tools-clinical-aetionomy-1 | AETIONOMY |
| Website: https://www.aetionomy.eu/ |
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