Utilizing machine learning algorithms to predict subject genetic mutation class from in silico models of neuronal networks.

ABSTRACT:

Background

Epilepsy is the fourth-most common neurological disorder, affecting an estimated 50 million patients globally. Nearly 40% of patients have uncontrolled seizures yet incur 80% of the cost. Anti-epileptic drugs commonly result in resistance and reversion to uncontrolled drug-resistant epilepsy and are often associated with significant adverse effects. This has led to a trial-and-error system in which physicians spend months to years attempting to identify the optimal therapeutic approach.

Objective

To investigate the potential clinical utility from the context of optimal therapeutic prediction of characterizing cellular electrophysiology. It is well-established that genomic data alone can sometimes be predictive of effective therapeutic approach. Thus, to assess the

SUBMITTER: Kress GT 

PROVIDER: S-EPMC9647930 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature