Samuel Sledzieski

Computational Biology PhD Student
Massachusetts Institute of Technology, USA

Email: samsl@mit.edu
GitHub: samsledje
Twitter: @samsledzieski
Google Scholar

Publications

*Equal Contribution

Journal

  • Sledzieski*, Devkota*, Singh, Cowen, Berger, "TT3D: Leveraging Pre-Computed Protein Sequence Models to Predict Protein-Protein Interactions," Bioinformatics, 2023: btad663. [PDF] [Software]
  • Sledzieski*, Singh*, 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 (2023): e2220778120. [PDF] [Software]
  • 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 (2023). 10.1371/journal.pone.0270965 [PDF]
  • Zaman*, Sledzieski*, Wu, Bansal, "virDTL: Viral recombination analysis through phylogenetic reconciliation and its application to sarbecoviruses and SARS-CoV-2," J Comput Biol. 2022 Sep 20. doi: 10.1089/cmb.2021.0507. Epub ahead of print. PMID: 36125448. [PDF] [Software]
  • Singh*, Devkota*, Sledzieski, Berger, Cowen, "Topsy-Turvy: integrating a global view into sequence-based PPI prediction," Bioinformatics, 38.Supplement_1 (July 2022): i264–i272. [PDF] [Software]
  • 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 (2021): 969-982. [PDF] [Software]

Conference

  • 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, December 2023.
  • Sledzieski*, Singh*, Cowen, Berger, "Contrasting drugs from decoys", Machine Learning for Structural Biology Workshop at NeurIPS, December 2022. [PDF]
  • Sledzieski*, Singh*, Cowen, Berger, "Adapting protein language models for rapid DTI prediction", Machine Learning for Structural Biology Workshop at NeurIPS, December 2021. [PDF]
  • 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), August 2021. [PDF]
  • Sledzieski, Zhang, Mandoiu, Bansal, “TreeFix-TP: Phylogenetic Error Correction for Accurate Reconstruction of Viral Transmission Networks,” Pacific Symposium on Biocomputing (PSB), January 2021. Proceedings, pages 119-130. [PDF] [Software]

Preprints

  • Sledzieski, Kshirsagar, Baek, Berger, Dodhia, Lavista Ferres, “Democratizing Protein Language Models with Parameter-Efficient Fine-Tuning,” Under review. Conference on Research in Computational Molecular Biology (RECOMB) [PDF]
  • Kousi, Boix, Park, Mathys, Sledzieski, Peng, Bennett, Tsai, Kellis, “Single-cell mosaicism analysis reveals cell-type-specific somatic mutational burden in Alzheimer’s Dementia,” bioRxiv. posted 22 April 2022, 10.1101/2022.04.21.489103. [PDF]
Website design courtesy of Vasilios Mavroudis