| People | Locations | Statistics |
|---|---|---|
| Naji, M. |
| |
| Motta, Antonella |
| |
| Aletan, Dirar |
| |
| Mohamed, Tarek |
| |
| Ertürk, Emre |
| |
| Taccardi, Nicola |
| |
| Kononenko, Denys |
| |
| Petrov, R. H. | Madrid |
|
| Alshaaer, Mazen | Brussels |
|
| Bih, L. |
| |
| Casati, R. |
| |
| Muller, Hermance |
| |
| Kočí, Jan | Prague |
|
| Šuljagić, Marija |
| |
| Kalteremidou, Kalliopi-Artemi | Brussels |
|
| Azam, Siraj |
| |
| Ospanova, Alyiya |
| |
| Blanpain, Bart |
| |
| Ali, M. A. |
| |
| Popa, V. |
| |
| Rančić, M. |
| |
| Ollier, Nadège |
| |
| Azevedo, Nuno Monteiro |
| |
| Landes, Michael |
| |
| Rignanese, Gian-Marco |
|
Szul, Piotr
in Cooperation with on an Cooperation-Score of 37%
Topics
Publications (3/3 displayed)
Places of action
| Organizations | Location | People |
|---|
document
Scaling Machine Learning to Uncover the Genome’s Role in Complex Diseases
Abstract
The wealth of genomic information around the world hold the promise to understand and predict the genetic risk for complex diseases. Cloud solutions using artificial intelligence and machine learning are key to generate insights from these unprecedented volumes of data. This talk showcases how we find novel disease genes for complex diseases. Using VariantSpark, a novel machine learning framework capable of processing trillion of datapoints from large-cohort Whole Genome Sequencing, we investigate poly-genic risk and identify associated epistatic interactions. Our tools open a new era of cloud-native health research: VariantSpark fosters reproducible and collaborative research by bringing analysis environments and workflows to the data through digital Marketplaces.And Ontoserver, a FHIR-native Terminology Server, enables interoperability for the National Digital Health Programs in Australia, United Kingdom, Netherlands, and is licenced by over 75 HealthCare organisations, universities and vendors globally. This provide the opportunity to standardise phenotype for use in machine learning applications.