Excited to share our new work Population-level integration of single-cell datasets enables multi-scale analysis across samples . We present scPoli to learn representation for cell types and samples that can be updated with new cells and samples.
Proud that our paper on life-long and transfer learning for single-cell biology has been awarded among top 3 papers selected by anonymous reviewers from MDSI at Technical University of Munich. This was among the work done during my doctoral studies at Helmholtz Munich and Life science school at TUM.
Our paper titled Predicting single-cell perturbation responses for unseen drugs is now accepted as at NeurIPS 2022. In this we add chemical graph encoder to my previous work CPA to predict gene expression response to unseen drugs not observed during training.
I am excited to announce the first spatial reference mapping in scArches powered by SageNet to map dissociated single cells scRNAseq into a common coordinate framework using one or more spatially resolved reference datasets.
I am excited to share our new work, we tackled: 1) how to learn a harmonized cell-type hierarchy/taxonomy across many studies with different annotations, 2) how to automatically identify novel cell states (e.
Our paper titled Predicting single-cell perturbation responses for unseen drugs is accepted as spotlight talk at MLDD 2022. This paper augments my previous work CPA with a molecular representation to predict unseen drugs.
Excited to share our new approach, expiMap, to learn gene programs (GP) activity from single-cells “biologically informed deep learning”.We add prior knowledge while learning new cellular circuits, going beyond data integration and towards interpretability.
I am excited to announce my preprint titled Compositional perturbation autoencoder for single-cell response modeling is not out! This is the work from my time at FACEBOOK AI.
The preprint is available on biorxiv.