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, Shhpilker, 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: Disentangling and Scaling Protein Language Model Representations.” International Conference on Learning Representations 2026 (2026).