@article{KewalramaniEtAl:Proteomics23,
  author	= {Kewalramani, Neal and Emili, Andrew and Crovella, Mark},
  title		= {State-of-the-Art Computational Methods to Predict Protein-Protein Interactions with High Accuracy and Coverage},
  journal	= {Proteomics},
  number	= {e2200292},
  year		= {2023},
  URL		= {http://www.cs.bu.edu/faculty/crovella/paper-archive/Proteomics-2023-Kewalramani-Methods.pdf},
  abstract	= {Prediction of protein-protein interactions commonly involves a significant computational component. Rapid recent advances in the power of computational methods for protein interaction prediction motivate a review of the state-of-the-art. We review the major approaches, organized according to the primary source of data utilized: protein sequence, protein structure, and protein coabundance. The advent of deep learning has brought with it significant advances in interaction prediction, and we show how deep learning is used for each source data type. We review the literature taxonomically, present example case studies in each category, and conclude with observations about the strengths and weaknesses of machine learning methods in the context of the principal sources of data for protein interaction prediction.},
  doi		= {10.1002/pmic.202200292},
}