Enhancers play a critical role in gene expression regulation. However, their accurate prediction remains a significant challenge due to the limited feature information provided by sequence semantics. To address this issue, we propose a novel multimodal framework, termed iEnhancer-Fusion, for enhancer identification and classification. The proposed model integrates two complementary modalities: DNA sequence features extracted using DNABERT-2, and DNA breathing features captured through a hybrid network comprising convolutional layer and Multi-Head Attention mechanism. These heterogeneous features are further fused via a Cross-Attention mechanism, enabling deep interaction between modalities and effectively overcoming the representational limitations of sequence-only models. Comparative experiments against seven representative enhancer prediction methods across two tasks demonstrate that iEnhancer-Fusion achieves superior performance across all key evaluation metrics. Specifically, in Task 1, the model achieves average ACC, MCC, and AUC scores of$82.70 \%, 65.52 \%$, and 87.35 %, respectively; in Task 2, the average scores for ACC, MCC, and AUC are$93.10 \%, 86.88 \%$, and 97.54 %, respectively.

MOTIVATION: Transcription factors are pivotal in the regulation of gene expression, and accurate identification of transcription factor binding sites (TFBSs) at high resolution is crucial for understanding the mechanisms underlying gene regulation. The task of identifying TFBSs from DNA sequences is a significant challenge in the field of computational biology today. To address this challenge, a variety of computational approaches have been developed. However, these methods face limitations in their ability to achieve high-resolution identification and often lack interpretability. RESULTS: We propose BertSNR, an interpretable deep learning framework for identifying TFBSs at single-nucleotide resolution. BertSNR integrates sequence-level and token-level information by multi-task learning based on pre-trained DNA language models. Benchmarking comparisons show that our BertSNR outperforms the existing state-of-the-art methods in TFBS predictions. Importantly, we enhanced the interpretability of the model through attentional weight visualization and motif analysis, and discovered the subtle relationship between attention weight and motif. Moreover, BertSNR effectively identifies TFBSs in promoter regions, facilitating the study of intricate gene regulation. AVAILABILITY AND IMPLEMENTATION: The BertSNR source code can be found at https://github.com/lhy0322/BertSNR.

Inferring potential drug indications plays a vital role in the drug discovery process. It can be time-consuming and costly to discover novel drug indications through biological experiments. Recently, graph learning-based methods have gained popularity for this task. These methods typically treat the prediction task as a binary classification problem, focusing on modeling associations between drugs and diseases within a graph. However, labeled data for drug indication prediction is often limited and expensive to acquire. Contrastive learning addresses this challenge by aligning similar drug-disease pairs and separating dissimilar pairs in the embedding space. Thus, we developed a model called DrIGCL for drug indication prediction, which utilizes graph convolutional networks and contrastive learning. DrIGCL incorporates drug structure, disease comorbidities, and known drug indications to extract representations of drugs and diseases. By combining contrastive and classification losses, DrIGCL predicts drug indications effectively. In multiple runs of hold-out validation experiments, DrIGCL consistently outperformed existing computational methods for drug indication prediction, particularly in terms of top-k. Furthermore, our ablation study has demonstrated a significant improvement in the predictive capabilities of our model when utilizing contrastive learning. Finally, we validated the practical usefulness of DrIGCL by examining the predicted novel indications of Aspirin.