PUBLICATIONS

[1] Sledzieski, Zhang, Mandoiu, Bansal. “TreeFix-TP: Phylogenetic error correction for accurate reconstruction of viral transmission networks.” Pacific Symposium on Biocomputing (PSB) 2021 (2021).

[2] Sledzieski, Singh, Cowen, Berger. “Sequence-based prediction of protein-protein interactions: a structure-aware interpretable deep learning model.” Conference on Research in Computational Molecular Biology (RECOMB) 2021 (2021).

[3] Sledzieski, Singh, Cowen, Berger. “D-SCRIPT translates genome to phenome with sequence-based, structure-aware, genome-scale predictions of protein-protein interactions.” Cell Systems 12(10): 969-982 (2021).

[4] Sledzieski, Singh, Cowen, Berger. “Adapting protein language models for rapid drug-target interaction prediction.” Machine Learning for Structural Biology Workshop at NeurIPS 2021 (2021).

[5] Kousi, Boix, Park, Mathys, Sledzieski, Peng, Bennett, Tsai, Kellis. “Single-cell mosaicism analysis reveals cell-type-specific somatic mutational burden in Alzheimers Dementia.” bioRxiv (2022).

[6] Singh, Devkota, Sledzieski, Berger, Cowen. “Topsy-Turvy: integrating a global view into sequence-based PPI prediction.” Bioinformatics 38(Supplement 1): i264-i272 (2022).

[7] Sledzieski, Singh, Cowen, Berger. “Contrasting drugs from decoys.” Machine Learning for Structural Biology Workshop at NeurIPS 2022 (2022).

[8] Zaman, Sledzieski, Wu, Bansal. “virDTL: Viral recombination analysis through phylogenetic reconciliation and its application to sarbecoviruses and SARS-CoV-2.” J Comput Biol 30(1): 3–20 (2023).

[9] Kumar, Brenner, Sledzieski, Olaosebikan, Lynn-Goin, Putnam, Yang, Lewinski, Singh, Daniels, Cowen, Klein-Seetharaman. “Transfer of knowledge from model organisms to evolutionarily distant non-model organisms: The coral Pocillopora damicornis membrane signaling receptome.” Plos one 18(2): e0270965 (2023).

[10] Singh, Sledzieski, Bryson, Cowen, Berger. “Contrastive learning in protein language space predicts interactions between drugs and protein targets.” Proceedings of the National Academy of Sciences 120(24): e2220778120 (2023).

[11] Sledzieski, Devkota, Singh, Cowen, Berger. “TT3D: Leveraging pre-computed protein sequence models to predict protein-protein interactions.” Bioinformatics 39(11): btad663 (2023).

[12] Sledzieski, Kshirsagar, Baek, Berger, Dodhia, Lavista Ferres. “Parameter-efficient fine-tuning of protein language models improves prediction of protein-protein interactions.” Machine Learning for Structural Biology Workshop at NeurIPS 2023 (2023).

[13] Vizgaudis, Kumar, Olaosebikan, Roger, Brenner, Sledzieski, Yang, Lewinski, Singh, Daniels, Cowen, Klein-Seetharaman. “Insulin Signaling and Pharmacology in Corals.” Authorea Preprints (2024).

[14] Sledzieski, Kshirsagar, Baek, Berger, Dodhia, Lavista Ferres. “Democratizing protein language models with parameter-efficient fine-tuning.” Proceedings of the National Academy of Sciences 121(26): e2405840121 (2024).

[15] Singh, Im, Qiu, Macnkess, Gupta, Sorenson, Sledzieski, Erlach, Wendt, Nanfack, Bryson, Berger. “Learning the language of antibody hypervariability.” Proceedings of the National Academy of Sciences 122(1): e2418918121 (2024).

[16] Kshirsagar, Meller, Humphreys, Sledzieski, Xu, Dodhia, Horvitz, Berger, Bowman, Lavista Ferres, Baker, Baek. “Rapid and accurate prediction of protein homo-oligomer symmetry.” Nature Communications 16(2017) (2025).

[17] Sledzieski, Versavel, Singh, Ocitti, Devkota, Kumar, Shpilker, Roger, Yang, Lewinski, Putnam, Berger, Klein-Seetharaman, Cowen. “Decoding the functional interactome of non-model organisms with PHILHARMONIC.” Conference on Research in Computational Molecular Biology (RECOMB) 2025 (2025).

[18] Sledzieski, Hanson. “RocketSHP: Ultra-fast proteome-scale prediction of protein dynamics.” bioRxiv (2025).

[19] Schäffer, Sledzieski, Cowen, Berger. “Memory-efficient, accelerated protein interaction inference with Blocked, Multi-GPU D-SCRIPT.” Bioinformatics 41(10): btaf564 (2025).

[20] Ocitti, Versavel, Sledzieski, Cowen. “RDS: A ReCIPE for overlapping community detection in biological networks.” ACM Conference on Bioinformatics, Computational Biology, and Health Informatics (ACM-BCB) 2025 (2025).

[21] Ullanat, Jing, Sledzieski, Berger. “Learning the language of protein-protein interactions.” Nature Communications 17(1199) (2026).

[22] Sledzieski, Hanson. “The landscape of machine learning approaches for modeling protein conformational ensembles.” Current Opinion in Structural Biology (2026).

[23] Catrina, Bepler, Sledzieski+, Singh+. “Reverse Distillation: Consistently Scaling Protein Language Model Representations.” International Conference on Learning Representations 2026 (2026).

[24] Golkar, Kovalic, Espejo Morales, Sledzieski, Li, Sokolova, Krawezik, Bietti, Skok Gibbs, Klypa, Xiong, Lanusse, Parker, Cho, Cranmer, Hehir, McCabe, Meyer, Morel, Mukhopadhyay, Pettee, Qu, Shen, Fouhey, Sotoudeh, Mulligan, Cossio, Hanson, Jones, Troyanskaya, Ho. “MIMIC: A Generative Multimodal Foundation Model for Biomolecules.” arXiv (2026).