Author

Beatrice Bevilacqua

Arc Research InstituteORCID 0000-0002-9462-4599Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Assay:bulk RNA-seq· 2 Approach:deep learning· 2 Research Focus:differential expression· 2 Organism:human· 2 Research Focus:perturbation response prediction· 2 Assay:perturbation screen· 2 Assay:scRNA-seq· 2 Approach:transformer / foundation model· 2

Frequent Co-Authors

Abhinav AdduriiD· 2 shared Basak EraslaniD· 2 shared Hani GoodarziiD· 2 shared Rishi VermaiD· 2 shared Yusuf RoohaniiD· 2 shared Adam Krejčí· 1 shared Aidan F. WintersiD· 1 shared Alexander DobiniD· 1 shared

Activity by Year

2026-2026 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 45 mutual co-author links

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2 shared papersBioinformatics

Abhinav AdduriORCID

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2 papers

Recent·Most cited

PIE: Generalizing perturbation effects across unseen perturbations, contexts and datasets

Rishi Verma, Abhinav Adduri, Beatrice Bevilacqua, Basak Eraslan, Dave Burke, Hani Goodarzi, Yusuf Roohani

Predicting differentially expressed genes and expression shifts per condition from database embeddings plus per-gene evidence, PIE reaches 1.2–3.2 times the best baseline's AUPRC on unseen contexts, perturbations and datasets.

bioRxiv · Oct 5, 2026 · PreprintBioinformatics✦ In-depth digest

Predicting cellular responses to perturbation across diverse contexts with STATE

Abhinav Adduri, Dhruv Gautam, Beatrice Bevilacqua, Mohsen Naghipourfar, Alishba Imran, Rohan Shah, Noam Teyssier, Rishi Verma, Christopher Carpenter, Basak Eraslan, Francis Chalissery, Rajesh Ilango, Vishvak Subramanyam, Chiara Ricci-Tam, Sanjay Nagaraj, , Yusuf Roohani

State, a set-based transformer trained on over 100 million perturbed cells, predicts perturbation responses in new cellular contexts better than mean, linear and deep learning baselines.

Cell · Aug 31, 2026Peer-reviewedSystems Biology✦ In-depth digest