Yan Liu

TL;DR: Two new DL-based models for improving the prediction of N6-methyladenine sites, termed LA6mA and AL6mA, which use bidirectional long short-term memory to respectively capture the long-range information and self-attention mechanism to extract the key position information from DNA sequences are developed.

TL;DR: Li et al. as mentioned in this paper proposed a transfer learning-based method, termed SAResNet, which combines the self-attention mechanism and residual network structure for predicting DNA-protein binding from sequence data.

TL;DR: Wang et al. as mentioned in this paper developed a new computational framework by combining triplet network and ensemble DL, which directly optimizes the protein fold embedding itself, making the proteins with the same fold types be closer to each other than those with different fold types in the new protein embedding space.

TL;DR: Zhang et al. as mentioned in this paper explored the intrinsic mechanism of deep convolutional neural network (DCNN) and explained why it works well for protein fold recognition using a visual explanation technique.

TL;DR: TripletGO as discussed by the authors proposed a triplet-network based profiling method with the feature space mapping technique which can accurately recognize function patterns from transcript expressions, and the combination of multiple complementary models, especially those from transcript expression and protein-level alignments, improves the coverage and accuracy of the final GO annotation results.