RESUMEN
CONTEXT: Epilepsy is a common neurological disease and is classified into different types based on features such as the kind of seizure, age of onset, part of brain effected, etc. There are nearly 30 approved anti-epileptic drugs (AEDs) for treating different epilepsies and each drug targets proteins exhibiting a specific molecular mechanism of action. There are many genes, proteins, and microRNAs known to be associated with different epileptic disorders. This rich information on epilepsy-associated data is not available at one single location and is scattered across multiple publicly available repositories. There is a need to have a single platform integrated with the data, as well as tools required for epilepsy research. METHODS AND MATERIAL: Text mining approaches are used to extract data from multiple biological sources. The data is curated and populated within an in-house developed epilepsy database. Machine-learning based models are built in-house to know the probability of a protein being druggable based on the significant protein features. A web interface is provided for the access of the epilepsy database as well as the ML-based tool developed in-house. RESULTS: The epilepsy-associated data is made accessible through a web browser. For a protein of interest, the platform provides all the feature values, and the results generated using different machine learning models are displayed as visualization plots. CONCLUSIONS: To meet these objectives, we present TREADS, a platform for epilepsy research community, having both database and an ML-based tool for the study of AED targets. TO ACCESS TREADS: https://treads-aer.cdacb.in.