Author

Christopher Carpenter

Arc Research Institute

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

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Bioinformatics· 1 Immunology· 1 Systems Biology· 1

Signature Assays, Organisms & Methods

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Approach:deep learning· 3 Organism:human· 3 Research Focus:benchmarking· 2 Assay:bulk RNA-seq· 2 Research Focus:drug response / drug discovery· 2 Research Focus:perturbation response prediction· 2 Assay:scRNA-seq· 2 Approach:transformer / foundation model· 2

Frequent Co-Authors

Abhinav AdduriiD· 2 shared Alexander DobiniD· 2 shared Chiara Ricci-TamiD· 2 shared Dave P. Burke· 2 shared Dhruv Gautam· 2 shared Mingze DongiD· 2 shared Yusuf RoohaniiD· 2 shared Adam Krejčí· 1 shared

Activity by Year

2025-2026 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 36 mutual co-author links

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2 shared papersSystems Biology

Abhinav AdduriORCID

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Author profile →

3 papers

Recent·Most cited

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

Stack: In-Context Learning of Single-Cell Biology

Mingze Dong, Abhinav Adduri, Dhruv Gautam, Lujing Wu, Courtney Kernick, Mary Margaret Coons, Yi-Chen Chih, Christopher Carpenter, Rohan M. Shah, Chiara Ricci-Tam, Po‐Yuan Tung, Nianzhen Li, Alexander Dobin, Yuval Kluger, Dave P. Burke, , Yusuf Roohani

SO_SCPLOWTACKC_SCPLOW is a foundation model trained on human single-cell data that enables in-context learning for predicting perturbation effects without fine-tuning.

bioRxiv · Jun 8, 2026 · PreprintBioinformatics

Affinity-matured CD72-targeting Nanobody CAR T-cells Enhance Elimination of Antigen-Low B-cell Malignancies

Adila Izgutdina, Tasfia Rashid, William C. Temple, Sarah Aminov, Bonell Patiño-Escobar, Sujata B. Walunj, Huimin Geng, Hiroyuki Takamatsu, Daniel Gil-Alós, Amrik S. Kang, Emilio Ramos, Szu‐Ying Chen, Haley Johnson, Matthew A. Nix, Akul Naik, , Arun P. Wiita

This study shows that affinity-matured CD72-targeting nanobody CAR T-cells improve in vitro efficacy against CD72-low tumors but show limited in vivo benefit, while CD72 expression remains stable after CD19 resistance in B-cell malignancies.

Journal for ImmunoTherapy of Cancer · Dec 1, 2025Peer-reviewedImmunology