Fig. 1
From: Modeling aspects of the language of life through transfer-learning protein sequences
Performance comparisons. The predictive power of the ELMo-based SeqVec embeddings was assessed for per-residue (upper row) and per-protein (lower row) prediction tasks. Methods using evolutionary information are highlighted by hashes above the bars. Approaches using only the proposed SeqVec embeddings are highlighted by stars after the method name. Panel A used three different data sets (CASP12, TS115, CB513) to compare three-state secondary structure prediction (y-axis: Q3; all DeepX developed here to test simple deep networks on top of the encodings tested; DeepProf used evolutionary information). Panel B compared predictions of intrinsically disordered residues on two data sets (CASP12, TS115; y-axis: MCC). Panel C compared per-protein predictions for subcellular localization between top methods (numbers for Q10 taken from DeepLoc [47]) and embeddings based on single sequences (Word2vec-like ProtVec [42] and our ELMo-based SeqVec). Panel D: the same data set was used to assess the predictive power of SeqVec for the classification of a protein into membrane-bound and water-soluble