SIMS (Scalable, Interpretable Modeling for Single-Cell RNA-Seq Data Classification) simplifies the task of building accurate classifiers for scRNA-seq data. With its sequential transformer model, designed for large-scale tabular datasets, SIMS ensures both accuracy and interpretability. Just upload your h5ad file, choose your model, and download your results. Ideal for biologists and computational biologists alike, SIMS offers a user-friendly interface for extracting insights from scRNA-seq data effortlessly.

See owner's GitHub repository for more information: https://github.com/braingeneers/SIMS.

This app is based on the paper:

Gonzalez-Ferrer, J., Lehrer, J., O'Farrell, A., Paten, B., Teodorescu, M., Haussler, D., Jonsson, V. D., & Mostajo-Radji, M. A. (2023). Unraveling Neuronal Identities Using SIMS: A Deep Learning Label Transfer Tool for Single-Cell RNA Sequencing Analysis. bioRxiv : the preprint server for biology, 2023.02.28.529615. https://doi.org/10.1101/2023.02.28.529615

This application was not uploaded by the author, but through their publicly available Github repository, https://github.com/braingeneers/SIMS.

MIT License

Copyright (c) 2021, Julian Lehrer