Update citation.cff

CITATION.cff

@@ -1,15 +1,19 @@
+# This CITATION.cff file was generated with cffinit.
+# Visit https://bit.ly/cffinit to generate yours today!
+
 cff-version: 1.2.0
 title: >-
-  Predicting Cellular Responses to Novel Drug Perturbations at a Single-Cell Resolution
-message: 'If you build upon our work, please cite our paper.'
-type: conference-paper
-booktitle: NeurIPS2022
-year: 2022
+  Predicting Cellular Responses to Novel Drug
+  Perturbations at a Single-Cell Resolution
+message: >-
+  If you use this software, please cite it using the
+  metadata from this file.
+type: software
 authors:
   - given-names: Leon
     family-names: Hetzel
   - given-names: Simon
-    family-names: Böhm
+    family-names: Boehm
   - given-names: Niki
     family-names: Kilbertus
   - given-names: Stephan
@@ -19,3 +23,39 @@ authors:
   - given-names: Fabian
     name-particle: J
     family-names: Theis
+identifiers:
+  - type: url
+    value: 'https://neurips.cc/virtual/2022/poster/53227'
+repository-code: 'https://github.com/theislab/chemCPA'
+abstract: >+
+  Single-cell transcriptomics enabled the study of
+  cellular heterogeneity in response to perturbations
+  at the resolution of individual cells. However,
+  scaling high-throughput screens (HTSs) to measure
+  cellular responses for many drugs remains a
+  challenge due to technical limitations and, more
+  importantly, the cost of such multiplexed
+  experiments. Thus, transferring information from
+  routinely performed bulk RNA HTS is required to
+  enrich single-cell data meaningfully.We introduce
+  chemCPA, a new encoder-decoder architecture to
+  study the perturbational effects of unseen drugs.
+  We combine the model with an architecture surgery
+  for transfer learning and demonstrate how training
+  on existing bulk RNA HTS datasets can improve
+  generalisation performance. Better generalisation
+  reduces the need for extensive and costly screens
+  at single-cell resolution. We envision that our
+  proposed method will facilitate more efficient
+  experiment designs through its ability to generate
+  in-silico hypotheses, ultimately accelerating drug
+  discovery.
+
+keywords:
+  - transfer learning
+  - disentanglement
+  - perturbation
+  - single cell
+  - genomics
+  - Drug Discovery
+  - unsupervised