λ-Scaled-Attention: A Novel Fast Attention Mechanism for Efficient Modeling of Protein Sequences
This work addresses efficiency and performance issues in protein sequence modeling, which is important for bioinformatics researchers, though it appears incremental as it builds on existing attention methods.
The paper tackled the challenges of applying attention mechanisms to protein sequences, such as vanishing attention scores and high distribution variations, by introducing a λ-scaled attention technique. It achieved improvements in F1 scores for protein function prediction, with gains of +2.01% for BP and +4.67% for MF over standard attention, and faster convergence during training.
Attention-based deep networks have been successfully applied on textual data in the field of NLP. However, their application on protein sequences poses additional challenges due to the weak semantics of the protein words, unlike the plain text words. These unexplored challenges faced by the standard attention technique include (i) vanishing attention score problem and (ii) high variations in the attention distribution. In this regard, we introduce a novel λ-scaled attention technique for fast and efficient modeling of the protein sequences that addresses both the above problems. This is used to develop the λ-scaled attention network and is evaluated for the task of protein function prediction implemented at the protein sub-sequence level. Experiments on the datasets for biological process (BP) and molecular function (MF) showed significant improvements in the F1 score values for the proposed λ-scaled attention technique over its counterpart approach based on the standard attention technique (+2.01% for BP and +4.67% for MF) and state-of-the-art ProtVecGen-Plus approach (+2.61% for BP and +4.20% for MF). Further, fast convergence (converging in half the number of epochs) and efficient learning (in terms of very low difference between the training and validation losses) were also observed during the training process.