Research area
BIOINFORMATICS
We want to find customized solutions for patients affected by rare diseases,
by applying machine learning techniques and high-performance computing to the study of the biological processes.
We have consolidated partnership with other companies in the field of genomics with whom we pursue this common goal.
by applying machine learning techniques and high-performance computing to the study of the biological processes.
We have consolidated partnership with other companies in the field of genomics with whom we pursue this common goal.
Genomics and epilepsy
Genomics is the study of the human genome, to which we apply machine learning techniques and high-performance computing.
Our aim is to find personalized solutions for patients affected
by rare and polygenic diseases, such as Parkinson’s, Alzheimer’s and epilepsy,
that will result into an improvement in the treatments and assistance provided to all patients with rare diseases and their families.
Our aim is to find personalized solutions for patients affected
by rare and polygenic diseases, such as Parkinson’s, Alzheimer’s and epilepsy,
that will result into an improvement in the treatments and assistance provided to all patients with rare diseases and their families.
Consider, for example, a condition like epilepsy, that is a multifactorial disease believed to have genetic origins.
Some forms seem to be monogenic, with a direct effect on the disease, other forms instead involve more than one gene,
therefore defined as polygenic, and cause epilepsy as a secondary syndrome.
Some forms seem to be monogenic, with a direct effect on the disease, other forms instead involve more than one gene,
therefore defined as polygenic, and cause epilepsy as a secondary syndrome.
The GENOMICS and EPILEPSY project aims to apply advanced genomics techniques
with a machine learning approach to the Polygenic Risk score,
increasing the effectiveness of the database search,
in order to match the data to the indicators
available in a specific population.
with a machine learning approach to the Polygenic Risk score,
increasing the effectiveness of the database search,
in order to match the data to the indicators
available in a specific population.
From the bioinformatic point of view, each form of epilepsy will be characterized by a polygenic score,
which associates an identified score with each gene, and will have as final output
a risk assessment for that given form of epilepsy.
The project will also standardize and integrate existing “big data” from multidisciplinary
and high-quality data sources, obtained from patients with different epilepsy forms
into an original and innovative platform, based on a unique set of advanced analytical methods.
which associates an identified score with each gene, and will have as final output
a risk assessment for that given form of epilepsy.
The project will also standardize and integrate existing “big data” from multidisciplinary
and high-quality data sources, obtained from patients with different epilepsy forms
into an original and innovative platform, based on a unique set of advanced analytical methods.
The first important result of the triangulation between available databases, information on clinical studies already
present in the literature and the use of cutting-edge tools in genomic sequencing will be support the pathology
treatment centres in the construction of ad hoc therapies for each patient, thus decreasing the risk of administering
ineffective drugs and adverse reactions.
present in the literature and the use of cutting-edge tools in genomic sequencing will be support the pathology
treatment centres in the construction of ad hoc therapies for each patient, thus decreasing the risk of administering
ineffective drugs and adverse reactions.
Partners: Ventiseidieci – as lead institution, Dante Labs, Poznań Supercomputing and Networking Center- PSNC,
University Hospital “Azienda Ospedaliero-Universitaria Meyer” and Foundation 29.
University Hospital “Azienda Ospedaliero-Universitaria Meyer” and Foundation 29.