ArticleStem cell reports2024
DeePhys: A machine learning-assisted platform for electrophysiological phenotyping of human neuronal networks.
Article in Stem cell reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Loss of schizophrenia risk gene XPO7 disrupts neuronal excitability and network regularity via altered NaMolecular psychiatry · 2026Article
- HIPPIE: a generative model for electrophysiological analysis across species, technologies, and modalities.Nature communications · 2026Article
- Review
- Hypersynchronous iPSC-derived SHANK2 neuronal networks are rescued by mGluR5 agonism.Stem cell reports · 2025Article
- Article
- Advances in large-scale electrophysiology with high-density microelectrode arrays.Lab on a chip · 2025Review
- Machine learning and complex network analysis of drug effects on neuronal microelectrode biosensor data.Scientific reports · 2025Article
- Evaluating chemical effects on human neural cells through calcium imaging and deep learning.iScience · 2024Article
- Multiscale Cloud-Based Pipeline for Neuronal Electrophysiology Analysis and Visualization.bioRxiv : the preprint server for biology · 2024Article
Corrections and comments
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Authors and funding
13 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Reproducible functional assays to study in vitro neuronal networks represent an important cornerstone in the quest to develop physiologically relevant cellular models of human diseases. Here, we introduce DeePhys, a MATLAB-based analysis tool for data-driven functional phenotyping of in vitro neuronal cultures recorded by high-density microelectrode arrays. DeePhys is a modular workflow that offers a range of techniques to extract features from spike-sorted data, allowing for the examination of functional phenotypes both at the individual cell and network levels, as well as across development. In addition, DeePhys incorporates the capability to integrate novel features and to use machine-learning-assisted approaches, which facilitates a comprehensive evaluation of pharmacological interventions. To illustrate its practical application, we apply DeePhys to human induced pluripotent stem cell-derived dopaminergic neurons obtained from both patients and healthy individuals and showcase how DeePhys enables phenotypic screenings.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.