MolE: a foundation model for molecular graphs using disentangled attention von Méndez-Lucio, Oscar, Nicolaou, Christos A., Earnshaw, Berton

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Building, benchmarking, and exploring perturbative maps of transcriptional and morphological data von Celik, Safiye, Hütter, Jan-Christian, Carlos, Sandra Melo, Lazar, Nathan H, Mohan, Rahul, Tillinghast, Conor, Biancalani, Tommaso, Fay, Marta M, Earnshaw, Berton A, Haque, Imran S

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Leveraging electronic health records for predictive modeling of post-surgical complications von Weller, Grant B, Lovely, Jenna, Larson, David W, Earnshaw, Berton A, Huebner, Marianne

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Biophysical Model of AMPA Receptor Trafficking and Its Regulation during Long-Term Potentiation/Long-Term Depression von Earnshaw, Berton A, Bressloff, Paul C

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Diffusion of Protein Receptors on a Cylindrical Dendritic Membrane with Partially Absorbing Traps von Bressloff, Paul C., Earnshaw, Berton A., Ward, Michael J.

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A Dynamic Corral Model of Receptor Trafficking at a Synapse von Bressloff, Paul C., Earnshaw, Berton A.

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A diffusion-activation model of CaMKII translocation waves in dendrites von Earnshaw, Berton A., Bressloff, Paul C.

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Global Asymptotic Stability of Solutions of Nonautonomous Master Equations von Earnshaw, Berton A., Keener, James P.

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Invariant Manifolds of Binomial-Like Nonautonomous Master Equations von Earnshaw, Berton A., Keener, James P.

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MolE: a molecular foundation model for drug discovery von Méndez-Lucio, Oscar, Nicolaou, Christos, Earnshaw, Berton

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IMAGE EMBEDDINGS VIA DEEP LEARNING AND ADAPTIVE BATCH NORMALIZATION von SYPETKOWSKI, Maciej, EARNSHAW, Berton Allen

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IMAGE EMBEDDINGS VIA DEEP LEARNING AND ADAPTIVE BATCH NORMALIZATION von SYPETKOWSKI, Maciej, EARNSHAW, Berton Allen

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Automated Discovery of Pairwise Interactions from Unstructured Data von Zuheng, Xu, Jain, Moksh, Denton, Ali, Whitfield, Shawn, Didolkar, Aniket, Earnshaw, Berton, Hartford, Jason

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MolE: a molecular foundation model for drug discovery von Méndez-Lucio, Oscar, Nicolaou, Christos, Earnshaw, Berton

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DETERMINING THE GOODNESS OF A BIOLOGICAL VECTOR SPACE von EARNSHAW, Berton, JENSEN, James, KHALIULLIN, Renat, LAZAR, Nathan

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Masked Autoencoders for Microscopy are Scalable Learners of Cellular Biology von Kraus, Oren, Kenyon-Dean, Kian, Saberian, Saber, Fallah, Maryam, McLean, Peter, Leung, Jess, Sharma, Vasudev, Khan, Ayla, Balakrishnan, Jia, Celik, Safiye, Beaini, Dominique, Sypetkowski, Maciej, Cheng, Chi Vicky, Morse, Kristen, Makes, Maureen, Mabey, Ben, Earnshaw, Berton

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RxRx1: A Dataset for Evaluating Experimental Batch Correction Methods von Sypetkowski, Maciej, Rezanejad, Morteza, Saberian, Saber, Kraus, Oren, Urbanik, John, Taylor, James, Mabey, Ben, Victors, Mason, Yosinski, Jason, Sereshkeh, Alborz Rezazadeh, Haque, Imran, Earnshaw, Berton

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Masked Autoencoders are Scalable Learners of Cellular Morphology von Kraus, Oren, Kenyon-Dean, Kian, Saberian, Saber, Fallah, Maryam, McLean, Peter, Leung, Jess, Sharma, Vasudev, Khan, Ayla, Balakrishnan, Jia, Celik, Safiye, Sypetkowski, Maciej, Cheng, Chi Vicky, Morse, Kristen, Makes, Maureen, Mabey, Ben, Earnshaw, Berton

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Automated Discovery of Pairwise Interactions from Unstructured Data von Zuheng, Xu, Jain, Moksh, Denton, Ali, Whitfield, Shawn, Didolkar, Aniket, Earnshaw, Berton, Hartford, Jason

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DETERMINING THE GOODNESS OF A BIOLOGICAL VECTOR SPACE von EARNSHAW, Berton, JENSEN, James, KHALIULLIN, Renat, LAZAR, Nathan

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