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.
Great news about scArches, we have made it to the January issue of Nature Biotechnology.
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.
I am happy to announce our paper titled CONDITIONAL OUT-OF-DISTRIBUTION GENERATION FOR UN-PAIRED DATA USING TRVAE is accepted as a highlight talk at EECB 2020 (accept rate 20.19%). The paper will also be published as a journal article in Bioinfomatics.
This week was full of good newses for us! Our paper titled “Out-of-distribution prediction with disentangled representations for single-cell RNA sequencing data” has been accepted as spotlight talk at ICML-CompBio-2020. The paper is available here.