ArticleScientific reports2022
Raster plots machine learning to predict the seizure liability of drugs and to identify drugs.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Seize the Data: A User-Friendly GUI for High-Resolution Analysis of Seizure Dynamics in HD-MEA Recordings.eNeuro · 2026Article
- Human iPSCs: atrial versus ventricular cardiomyocytes and their functional and pharmacological differences.Biochemical Society transactions · 2026Review
- Applications of artificial intelligence in stem cell therapy.World journal of stem cells · 2025Review
- Applications of artificial intelligence in drug discovery for neurological diseases.Neurotherapeutics : the journal of the American Society for Experimental NeuroTherapeutics · 2025Review
- Adverse Outcome Pathway and Machine Learning to Predict Drug Induced Seizure Liability.ACS chemical neuroscience · 2025Article
- Evaluating chemical effects on human neural cells through calcium imaging and deep learning.iScience · 2024Article
- Characterization of human iPSC-derived sensory neurons and their functional assessment using multi electrode array.Scientific reports · 2024Article
- Harnessing the potential of human induced pluripotent stem cells, functional assays and machine learning for neurodevelopmental disorders.Frontiers in neuroscience · 2024Review
- The use of artificial intelligence in induced pluripotent stem cell-based technology over 10-year period: A systematic scoping review.PloS one · 2024Article
- Large-Area Field Potential Imaging Having Single Neuron Resolution Using 236 880 Electrodes CMOS-MEA Technology.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2023Article
- Recent Progress and Perspectives on Neural Chip Platforms Integrating PDMS-Based Microfluidic Devices and Microelectrode Arrays.Micromachines · 2023Review
- Gene Expression Profile as a Predictor of Seizure Liability.International journal of molecular sciences · 2023Article
- A functional neuron maturation device provides convenient application on microelectrode array for neural network measurement.Biomaterials research · 2022Article
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Authors and funding
6 authors.
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Abstract
In vitro microelectrode array (MEA) assessment using human induced pluripotent stem cell (iPSC)-derived neurons holds promise as a method of seizure and toxicity evaluation. However, there are still issues surrounding the analysis methods used to predict seizure and toxicity liability as well as drug mechanisms of action. In the present study, we developed an artificial intelligence (AI) capable of predicting the seizure liability of drugs and identifying drugs using deep learning based on raster plots of neural network activity. The seizure liability prediction AI had a prediction accuracy of 98.4% for the drugs used to train it, classifying them correctly based on their responses as either seizure-causing compounds or seizure-free compounds. The AI also made concentration-dependent judgments of the seizure liability of drugs that it was not trained on. In addition, the drug identification AI implemented using the leave-one-sample-out scheme could distinguish among 13 seizure-causing compounds as well as seizure-free compound responses, with a mean accuracy of 99.9 ± 0.1% for all drugs. These AI prediction models are able to identify seizure liability concentration-dependence, rank the level of seizure liability based on the seizure liability probability, and identify the mechanism of the action of compounds. This holds promise for the future of in vitro MEA assessment as a powerful, high-accuracy new seizure liability prediction method.
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