Evolutionary profiles improve protein-protein interaction prediction from sequence

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Modeling aspects of the language of life through transfer-learning protein sequences (2019, methodology): “This concept resembles that of using k-mers for database searches [33], clustering [81], motifs [82, 83], and prediction methods [72, 76, 84 – 88].”
Modeling the language of life – Deep Learning Protein Sequences (2019, methodology): “This concept resembles 24 that of using k-mers for database searches [33], clustering [74], motifs [75, 76], and prediction 25 methods [66, 70, 77-81].”
Predicting protein‐protein interactions through sequence‐based deep learning (2018, methodology): “…as PIPE2 (Pitre et al., 2012), SigProd (Martin et al., 2005), AutoCorrelation (Guo et al., 2008), Zhou’s work (Zhou et al., 2011), You’s work (You et al., 2014), Profppikernel (Hamp and Rost, 2015), Wong’s work (Wong et al., 2015), Yang’s work (Yang et al., 2010) and Sun’s work (Sun et al., 2017).”
End-to-end multitask learning, from protein language to protein features without alignments (2019, unknown): “…1994), protein flexibility (Capriotti, et al., 2005; Radivojac, et al., 2004), inter-residue contacts (Hayat, et al., 2015), protein-protein interactions (Hamp and Rost, 2015; Zhang, et al., 2012), and subcellular localization (Casadio, et al., 2008; Goldberg, et al., 2014; Nair and Rost, 2003).”