SGTCDA: Prediction of circRNA-drug sensitivity associations with interpretable graph transformers and effective assessment.

CircRNAs are a type of circular non-coding RNA whose associations with drug sensitivities have been demonstrated in recent studies. Due to the high cost of biomedical experiments for detecting the associations between circRNAs and drug sensitivities, several computational methods have been developed. However, these methods were evaluated mainly based on 5- or tenfold cross-validation, which are often over-optimistic.

GraphNABP: Identifying nucleic acid-binding proteins with protein graphs and protein language models.

The computational identification of nucleic acid-binding proteins (NABP) is of great significance for understanding the mechanisms of these biological activities and drug discovery. Although a bunch of sequence-based methods have been proposed to predict NABP and achieved promising performance, the structure information is often overlooked. On the other hand, the power of popular protein language models (pLM) has seldom been harnessed for predicting NABPs.

NeuroPpred-SHE: An interpretable neuropeptides prediction model based on selected features from hand-crafted features and embeddings of T5 model.

Neuropeptides are the most ubiquitous neurotransmitters in the immune system, regulating various biological processes. Neuropeptides play a significant role for the discovery of new drugs and targets for nervous system disorders. Traditional experimental methods for identifying neuropeptides are time-consuming and costly.

Protein-DNA interface hotspots prediction based on fusion features of embeddings of protein language model and handcrafted features.

The identification of hotspot residues at the protein-DNA binding interfaces plays a crucial role in various aspects such as drug discovery and disease treatment. Although experimental methods such as alanine scanning mutagenesis have been developed to determine the hotspot residues on protein-DNA interfaces, they are both inefficient and costly. Therefore, it is highly necessary to develop efficient and accurate computational methods for predicting hotspot residues.