Add contextualized virtual screening to bibliography

Author: blengerich

_bibliography/papers.bib

@@ -1,6 +1,23 @@
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+@article{ellington2025virtual,
+    title = {Virtual Screening on Cellular Systems with Contextualized Networks},
+    journal = {Machine Learning for Computational Biology (MLCB)},
+    author = {Ellington, Caleb and Addagudi, Sohan and Wang, Jiaqi and Lengerich, Benjamin and Xing, Eric P.},
+    abstract = {Virtual screening has traditionally focused on molecular targets, often failing to anticipate the complex, system-level failures that arise during clinical trials. 
+        To address this, we propose a framework for virtual screening against entire cellular systems.
+        Our approach uses contextualized modeling, a multi-task learning approach for inferring context-specific network models, to infer perturbation-specific coexpression networks from large-scale screening datasets, enabling accurate prediction of network restructuring under diverse cellular and therapeutic contexts.
+        We demonstrate that context-adaptive models outperform population baselines as well as baselines learned for specific cell type $\times$ perturbation combinations when predicting transcriptional responses and network changes.
+        At test-time, contextualized networks generate accurate models of gene network reorganization on-demand for completely unseen cell types and therapies.
+        Across multiple independent runs, networks provide a standard, cohesive, and constrained latent space to compare therapeutic effects from different perturbation modalities (knockout, overexpression, small molecule).
+        Comparing perturbations in terms of cell-level effects leads to a principled approach to drug repurposing, safety profiling, and interpreting mechanism of action.
+        This work advances a systems-level foundation for \textit{in silico} preclinical screening, promising a new approach for mapping potential therapies to rare and heterogeneous diseases.
+    },
+    year = {2025},
+    bibtex_show = {true},
+}
+
 @article{liu2025mka,
     title = {MKA: Memory-Keyed Attention for Efficient Long-Context Reasoning},
     journal={ICML Long Context Foundation Models (LCFM)},