Case Study

Epistop – preventive epilepsy treatment

The EPISTOP project is a large international medical project coordinated by the Children’s Memorial Health Institute (IPCZD). Apart from IPCZD, 10 clinical centers from Europe and Australia and 5 research laboratories from Europe and the USA participated in the study.

The EPISTOP project had two main goals. The first was to evaluate the effectiveness of preventive epilepsy treatment in patients with tuberous sclerosis. Treatment was initiated when epileptic changes were noticed in the EEG video.

The second goal was to discover biomarkers of epileptic seizures.

The study included patients with tuberous sclerosis (TSC) who had an 80-90% chance of developing epileptic seizures in their first year of life. Children (101 patients) were followed from birth to 2 years of age and the progression of the disease was followed.

In addition to clinical data, the project also collected omics (genomic, transcriptomic, proteomic, metabolic), MRI and EEG data for a total of 33 TB of data.

The task facing Transition Technologies S.A. was data collection and cleansing, statistical analysis, and joint analysis of genomic and clinical data. The results of clinical, genomics, transcriptomics, proteomics and metabolic studies were intelligently combined into a single data set, and machine learning methods, including dimensional reduction methods, were applied to build a model that predicts the risk of developing epilepsy and identifies candidates for epilepsy biomarkers.

Read more: here.

The EPISTOP project had two main goals. The first was to evaluate the effectiveness of preventive epilepsy treatment in patients with tuberous sclerosis. Treatment was initiated when epileptic changes were noticed in the EEG video.

The second goal was to discover biomarkers of epileptic seizures.

The study included patients with tuberous sclerosis (TSC) who had an 80-90% chance of developing epileptic seizures in their first year of life. Children (101 patients) were followed from birth to 2 years of age and the progression of the disease was followed. In addition to clinical data, the project also collected omics (genomic, transcriptomic, proteomic, metabolic), MRI and EEG data for a total of 33 TB of data.

The task facing Transition Technologies S.A. was data collection and cleansing, statistical analysis, and joint analysis of genomic and clinical data. The results of clinical, genomics, transcriptomics, proteomics and metabolic studies were intelligently combined into a single data set, and machine learning methods, including dimensional reduction methods, were applied to build a model that predicts the risk of developing epilepsy and identifies candidates for epilepsy biomarkers.

Read more: here.

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