Results & Downloads
Publication in the Journal “Nature Communications”
Article entitled: “Rare disease research workflow using multilayer networks elucidates the molecular determinants of severity in Congenital Myasthenic Syndromes”
Publication in the Journal “Nature Communications”
Blog post
The project members Erik G. Larsson and Liesbet Van der Perre are revealing insights into our project in the blog post called “Golden Frequencies” on the website Wireless Future. They unravel the revolutionary advancements, challenges, and real-world applications reshaping the industry. Click on the link on the right to delve into this insightful read!
Blog post
D7.3 “Updated plan and initial report on dissemination and communication activities”
This deliverable includes information on the dissemination, communication and exploitation activities, as well as on internal and external training activities of the EXFILES project. It is the first report on the communication and dissemination activities of the first 18 project months. It thus contains relevant information about all executed activities up to M18, as well as an updated plan of future activities. The deliverable will be updated and finalized within “D7.6 Final report on dissemination & communication activities” at the project end.
D7.3 “Updated plan and initial report on dissemination and communication activities”
D7.2 “Hands-on trainings for contributors”
This document describes deliverable D7.2 “Hands-on training for contributors”. As part of this deliverable, RISCURE has made available (and will continue to enable access for the entire duration of the project) two standard, hands-on online training courses to all EXFILES contributors.
D7.2 “Hands-on trainings for contributors”
Poster on FEM Simulations to optimize a micro mirror array package for a wide operating temperature range
Partner Fraunhofer presented a poster at SPIE Photonics West 2023.
Poster on FEM Simulations to optimize a micro mirror array package for a wide operating temperature range
Book contribution
Towards Sustainable and Trustworthy 6G – Challenges, Enables and Architectural Design with REINDEER Contributing Authors: Ch. 2: Pål Frenger (EAB); Ch. 3: Vida Ranjbar (KU Leuven); Ch. 4: Liesbet Van der Perre (KU Leuven), Thomas Wilding (TU Graz); Ch. 6: Benjamin Deutschmann (TU Graz), Liesbet Van der Perre (KU Leuven), Klaus Witrisal, Thomas Wilding (TU Graz)
Book contribution
Presentation on Propagation Modeling for Physically Large Arrays: Measurement and Multipath Component Visibility
TU Graz gave a presentation at the 2023 EuCNC & 6G Summit in Gothenburg.
Presentation on Propagation Modeling for Physically Large Arrays: Measurement and Multipath Component Visibility
FEM Simulations to optimize a micro mirror array package for a wide operating temperature range
This paper was published at the SPIE Photonics West 2023 on 15th March 2023.
FEM Simulations to optimize a micro mirror array package for a wide operating temperature range
Paper published in the 26th International Conference on Artificial Intelligence and Statistics (AISTATS)
Paper entitled “Isotropic Gaussian Processes on Finite Spaces of Graphs”
Paper published in the 26th International Conference on Artificial Intelligence and Statistics (AISTATS)
Factsheet 4 – Main Achievements & ongoing work
The fourth iPC factsheet about the main achievements and ongoing work is now available. Much has already been done in the first two project periods to achieve the goal to improve the care of children with cancer and our partners are continuously working in the third project period to solve the mathematical and computational bottlenecks of data- and model-based medicine. Therefore, the fourth iPC factsheet describes the most important achievements so far and the ongoing work in the current project period.
Factsheet 4 – Main Achievements & ongoing work
D7.3 Identification of cell subpopulations in each tumour type, their association with response to therapy, and prediction of effective alternative therapies
Tumour decomposition into cells and subtypes and inference about the effects of treatments and perturbations on each tumour component (cell or tumor subclone).
D7.3 Identification of cell subpopulations in each tumour type, their association with response to therapy, and prediction of effective alternative therapies
Factsheet 3 – iPC Open Source Software
The third iPC factsheet about the iPC Open source software is now available.
It describes 3 of the 25 open source softwares that were developed during the project framework.
The focus will be on INtERAcT, CONSIFER and DECODE, which were developed by our partner IBM.
Factsheet 3 – iPC Open Source Software
Presentation on Advantages of Phase Modulating MEMS for Full 3D Hologram Scene Reconstruction
Partner SeeReal gave a presentation at Digital Holography and Three-Dimensional Imaging 2022.
Presentation on Advantages of Phase Modulating MEMS for Full 3D Hologram Scene Reconstruction
Factsheet 2 – iPC Platforms
The second iPC factsheet about the iPC Platforms is now available. It describes the 5 cloud-based platforms that were developed during the project framework from our partners BSC, XLAB, CHOP, AMC, PMC, DKFZ, UGent and BCM.
Factsheet 2 – iPC Platforms
D1.4 Model development data including genetic perturbation screens and gene-drug synergies
This deliverable reports on the generation of CROPseq and drug screening data for two Ewing Sarcoma cell lines, one Hepatoblastoma cell line and one B-cell Acute Lymphoblastic Leukemia cell line.
D1.4 Model development data including genetic perturbation screens and gene-drug synergies
Conference Paper “ECML PKDD International Workshop on eXplainable Knowledge Discovery in Data Mining”
Paper entitled: “Is Attention Interpretation? A Quantitative Assessment On Sets”
Conference Paper “ECML PKDD International Workshop on eXplainable Knowledge Discovery in Data Mining”
Factsheet 1 – Tumour Type Working Groups
The first iPC factsheet on Tumour Type Working Groups is now available. It describes the 5 different types of childhood cancer and the work our partners are doing.
The working groups are led by our partners IGTP, DKFZ, PMC, CURIE, UZH and MPG.
Factsheet 1 – Tumour Type Working Groups
D7.2 Software to define tumour subclones and association with therapy response
Flow cytometry is an important diagnostic tool in childhood acute lymphoblastic leukaemia (ALL), flow cytometry data analysis is limited by multiple sources of bias and variation. We present a unified machine learning framework for automated analysis of a standardized diagnostic paediatric leukaemia staining that can overcome these challenges. We applied our framework in a large cohort of ALL flow cytometry samples and demonstrated how it can robustly extract the frequencies of cell lineage populations with minimal expert intervention. This work provides a proof of concept that our method meets the needs of an automated analysis tool for diagnostic flow cytometry data.
D7.2 Software to define tumour subclones and association with therapy response
D8.3 Metabolic models
Oncogene-driven metabolic rewiring in cancer is key to allow proliferation of tumour cells in low nutrient and oxygen conditions. To study such phenomena, reconstructing context-specific metabolic models through omics data integration is crucial. Here we report the original pipeline to construct context-specific metabolic models from scRNA-seq data and we applied it to scRNA-seq data from Ewing Sarcoma.
D8.3 Metabolic models
D4.3 Topological analysis of multi-omics and multi-cancer molecular networks resulting in the definition of molecular mechanisms
Three types of network-based analysis of gene-gene interaction networks have been suggested and tested on the multi-omics paediatric cancer datasets. User-friendly computational environment for joint application of matrix factorization and network analysis has been implemented.
D4.3 Topological analysis of multi-omics and multi-cancer molecular networks resulting in the definition of molecular mechanisms
D8.2 Network models for molecular target identification
We focused on the development of patient specific signalling networks using prior knowledge about the molecular events and CRISPR perturbation datasets and associated the activity of the nodes of signalling network with drug response data to find molecular targets.
D8.2 Network models for molecular target identification
D4.4 Consensus multi-omics subtypes of paediatric cancers
We report on the implementation of a method for multilayer community trajectory analysis and its applications, including a published study on medulloblastoma, a study on congenital myasthenic syndromes, and a study on the functional characterization of commonalities among a selection of paediatric tumours.
D4.4 Consensus multi-omics subtypes of paediatric cancers
Consensus multi-omics subtypes of paediatric cancers
We report on the implementation of a method for multilayer community trajectory analysis and its applications, including a published study on medulloblastoma, a study on congenital myasthenic syndromes, and a study on the functional characterization of commonalities among a selection of paediatric tumours.
Consensus multi-omics subtypes of paediatric cancers
D2.4 DAC Portal prototype, validated analytical workflows, analysis prototype, updated metadata standards and portal prototype
We report on the selection of the appropriate data models to handle the available data and metadata to the iPC Central Computational and Data platform. We also report on the current status of the development for the iPC Data portal.
D2.4 DAC Portal prototype, validated analytical workflows, analysis prototype, updated metadata standards and portal prototype
D3.3 Integration of INtERAcT, MelanomaMine and LimTox and application to biomedical publications on paediatric cancers
This deliverable reports on the integration of INtERAcT and the implemented text mining workflow. The workflow was developed to adapt LimTox and MelanomaMine to pediatric tumor abstracts from PubMed and relies on INtERAcT in its downstream component of inferring molecular associations between entities extracted from unstructured text.
D3.3 Integration of INtERAcT, MelanomaMine and LimTox and application to biomedical publications on paediatric cancers
D1.3 Synthetic data for testing and training patient, cancer, and drug models
Synthetic data generation is emerging as an important solution for precision medicine. Therefore, an explainable Variational AutoEncoder (VAE) model is developed for synthetic transcriptomics data generation in medulloblastoma. The model can be used to complement and interpolate available data with synthetic instances. It is also transparent as it is able to match the learned latent variables with unique gene expression patterns. The model can also be adapted to other pediatric cancers and the resulting synthetic datasets used to test and train patient, cancer, and drug models in other work packages of the iPC project.
D1.3 Synthetic data for testing and training patient, cancer, and drug models
Paper published in the Special Issue Hepatoblastoma and Other Pediatric Liver Tumors
Paper entitled “Bridging molecular basis, prognosis, and treatment of pediatric liver tumors”.
Paper published in the Special Issue Hepatoblastoma and Other Pediatric Liver Tumors
Paper published in the Journal for ImmunoTherapy of Cancer
Paper entitled “Identification and validation of viral antigens sharing sequence and structural homology with tumor-associated antigens (TAAs).”
Paper published in the Journal for ImmunoTherapy of Cancer
Paper presented in the “Computer Vision for Microscopy Image Analysis (CVMI)” workshop
Paper entitled “Unsupervised Detection of Cancerous Regions in Histology Imagery using Image-to-Image Translation”
Paper presented in the “Computer Vision for Microscopy Image Analysis (CVMI)” workshop
Paper published in Physics of Life Reviews: pp. 132-134
Paper entiteld: „Adaptation through the lense of single-cell multi-omics data Comment on “Dynamic and thermodynamic models of adaptation” by A.N. Gorban et al.”
Paper published in Physics of Life Reviews: pp. 132-134
Paper published in the Proceedings of International Conference on Machine Learning (ICML) 2021
A new network inference algorithm from TUDA with iPC acknowledge was published in the ICML 2021. Paper entitled “Active Learning of Continuous-Time Bayesian Networks Through Interventions”.
Paper published in the Proceedings of International Conference on Machine Learning (ICML) 2021
D4.2 An interactive online atlas of interconnected network maps based on the NaviCell platform
With the development of the NaviCell 3.0 web server, there is a complete and automated web-based infrastructure for hosting molecular maps, patient similarity network maps, and multi-omics datasets for the project. The NaviCell platform supports molecular map navigation and exploration using the Google maps engine. The logic of navigation is taken from Google maps. This NaviCell 3.0 web-server is freely available and several step-by-step tutorials are accessible.
D4.2 An interactive online atlas of interconnected network maps based on the NaviCell platform
D7.1 Application of software enabling computational deconvolution of bulk RNA-sequencing data to immune cell profiles of patient samples
Computational deconvolution of bulk RNA-sequencing data to infer cell type composition of a sample is challenging. Benchmarking of various computational deconvolution tools revealed various data processing parameters that impact deconvolution accuracy and revealed the importance of a complete reference matrix. As a complete reference matrix is often not available, an algorithm was designed that can handle missing cell types. This algorithm can be applied to establish the immune cell repertoire of primary tumor biopsies without prior knowledge of the full spectrum of cell types in the biopsy.
D7.1 Application of software enabling computational deconvolution of bulk RNA-sequencing data to immune cell profiles of patient samples
D3.1 Identification of important regulatory elements using multi-level matrix factorization approaches
D3.1 describes the techniques for dimensionality reduction used in iPC and their application to a selection of cohorts (at different omics levels) as well as a meta-analysis of the four solid tumor types of interest. The goal of the deliverable is to provide a list of pathways and biological functions having a key role in multiple paediatric cancers.
D3.1 Identification of important regulatory elements using multi-level matrix factorization approaches
D3.2 Adaptation of MelanomaMine and LiMTox to the analysis of paediatric cancers and application to biomedical publications on paediatric cancers
The paper reports on the implementation of the iPC text mining workflow and three use cases for extracting biomedical information from large volumes. The workflow builds on the general framework of two text mining tools, LimTox and MelanomaMine. These tools will be used in the framework of the iPC project but also beyond, having a clear impact in the research community.
D3.2 Adaptation of MelanomaMine and LiMTox to the analysis of paediatric cancers and application to biomedical publications on paediatric cancers
D8.1 Data-driven model for molecular targets and drug repositioning
This deliverable provides a detailed overview of the proposed computational tool for predicting patient-specific drugs with potential therapeutic benefit for paediatric cancer treatment and provides, for example, evidence for the goodness of the model in predicting such patient-specific drugs.
D8.1 Data-driven model for molecular targets and drug repositioning
D2.3 Recommended metadata standards and portal prototype
The iPC project aims to ensure interoperability of data between different resources, so the platform must enforce principles and well-defined standards for data accessibility, usability, and registration. This deliverable provides an overview of the different approaches to representing metadata within the iPC Platform, and the efforts to integrate and leverage them within the iPC Catalog and the overall iPC Central Computational and Data Platform to enable meaningful management of research data.
D2.3 Recommended metadata standards and portal prototype
D1.2 Collection of high-quality clinical and molecular paediatric cancer datasets as well as other tumour types
In this deliverable, demographic, clinical, and molecular profiles were collected for several pediatric and adult tumors. In addition, the focus here is on collections of single cell profiles of high risk cancers. The datasets will be used to evaluate the effects of treatments and perturbations on cancer cells, build models, and provide information on deciphering regulatory interactions. These data will allow characterization of cancer cell types that predict treatment outcome, as well as cell types that are resistant to therapies.
D1.2 Collection of high-quality clinical and molecular paediatric cancer datasets as well as other tumour types
“Artificial Intelligence in Cancer Research: learning at different levels of data granularity”
Article published in the Molecular Oncology Journal, Volume 15, Issue 4 Pages 817-829.
“Artificial Intelligence in Cancer Research: learning at different levels of data granularity”
Article published in the Cancer Cell Journal, Volume 39, Issue 6, P 810-826
Article entitled “STAG2 mutations alter CTCF-anchored loop extrusion, reduce cis-regulatory interactions and EWSR1-FLI1 activity in Ewing sarcoma”.
Article published in the Cancer Cell Journal, Volume 39, Issue 6, P 810-826
Publication in the Journal of Medical Internet Research (JMIR).
Article entitled “Artificial Intelligence–Aided Precision Medicine for COVID-19: Strategic Areas of Research and Development”.
Published in the JMIR, Volume 23 Issue 3 by partner Barcelona Supercomputing Center (BSC).
Publication in the Journal of Medical Internet Research (JMIR).
Paper published in the International Joint Conference on Neural Networks-2021 (IJCNN2021)
Paper entitled: “Clinical trajectories estimated from bulk tumoral molecular proles using elastic principal trees”
Paper published in the International Joint Conference on Neural Networks-2021 (IJCNN2021)
D4.1 Building of cancer type-specific multi-layered molecular and patient similarity networks
iPC uses network inference techniques and applies a selection of pediatric patient cohorts at different omic levels. Networks will be generated, for example, for the generation of molecular patient networks to be used in downstream project activities involving the use of networks.
D4.1 Building of cancer type-specific multi-layered molecular and patient similarity networks
D2.2 “Initial infrastructure framework”
An initial demonstrator of the iPC infrastructure is reviewed. The platform’s architecture is based on modules, which allow parallel developments and integration of different open source-based software components. This allows us to leverage other efforts and contribute towards its sustainability and maintainability. The release of a minimum viable platform is allowing us to capture early feedback from researchers at iPC.
D2.2 “Initial infrastructure framework”