Single-cell RNA sequencing reveals the gene structure and gene expression status of a single cell, which can reflect the heterogeneity between cells. However, batch effects caused by non-biological factors may hinder data integration and downstream analysis. Although the batch effect can be evaluated by visualizing the data, which actually is subjective and inaccurate. In this work, we propose a quantitative method cKBET, which considers the batch and cell type information simultaneously. The cKBET method accesses batch effects by comparing the global and local fraction of cells of different batches in different cell types. We verify the performance of our cKBET method on simulated and real biological data sets. The experimental results show that our cKBET method is superior to existing methods in most cases. In general, our cKBET method can detect batch effect with either balanced or unbalanced cell types, and thus evaluate batch correction methods.

Prediction of drug-protein binding is critical for virtual drug screening. Many deep learning methods have been proposed to predict the drug-protein binding based on protein sequences and drug representation sequences. However, most existing methods extract features from protein and drug sequences separately. As a result, they can not learn the features characterizing the drug-protein interactions. In addition, the existing methods encode the protein (drug) sequence usually based on the assumption that each amino acid (atom) has the same contribution to the binding, ignoring different impacts of different amino acids (atoms) on the binding. However, the event of drug-protein binding usually occurs between conserved residue fragments in the protein sequence and atom fragments of the drug molecule. Therefore, a more comprehensive encoding strategy is required to extract information from the conserved fragments.

In this paper, we propose a novel model, named FragDPI, to predict the drug-protein binding affinity. Unlike other methods, we encode the sequences based on the conserved fragments and encode the protein and drug into a unified vector. Moreover, we adopt a novel two-step training strategy to train FragDPI. The pre-training step is to learn the interactions between different fragments using unsupervised learning. The fine-tuning step is for predicting the binding affinities using supervised learning. The experiment results have illustrated the superiority of FragDPI.

Circular RNAs (circRNAs) are RNAs with closed circular structure involved in many biological processes by key interactions with RNA binding proteins (RBPs). Existing methods for predicting these interactions have limitations in feature learning. In view of this, we propose a method named circ2CBA, which uses only sequence information of circRNAs to predict circRNA-RBP binding sites. We have constructed a data set which includes eight sub-datasets. First, circ2CBA encodes circRNA sequences using the one-hot method. Next, a two-layer convolutional neural network (CNN) is used to initially extract the features. After CNN, circ2CBA uses a layer of bidirectional long and short-term memory network (BiLSTM) and the self-attention mechanism to learn the features. The AUC value of circ2CBA reaches 0.8987. Comparison of circ2CBA with other three methods on our data set and an ablation experiment confirm that circ2CBA is an effective method to predict the binding sites between circRNAs and RBPs.