Computational prediction of miRNAs, diseases, and genes associated with circRNAs has important implications for circRNA research, as well as provides a reference for wet experiments to save costs and time. In this study, SGCNCMI, a computational model combining multimodal information and graph convolutional neural networks, combines node similarity to form node information and then predicts associated nodes using GCN with a distributive contribution mechanism. The model can be used not only to predict the molecular level of circRNA–miRNA interactions but also to predict circRNA–cancer and circRNA–gene associations. The AUCs of circRNA—miRNA, circRNA–disease, and circRNA–gene associations in the five-fold cross-validation experiment of SGCNCMI is 89.42%, 84.18%, and 82.44%, respectively. SGCNCMI is one of the few models in this field and achieved the best results. In addition, in our case study, six of the top ten relationship pairs with the highest prediction scores were verified in PubMed.

During the development of drug and clinical applications, due to the co-administration of different drugs that have a high risk of interfering with each other’s mechanisms of action, correctly identifying potential drug–drug interactions (DDIs) is important to avoid a reduction in drug therapeutic activities and serious injuries to the organism. Therefore, to explore potential DDIs, we develop a computational method of integrating multi-level information. Firstly, the information of chemical sequence is fully captured by the Natural Language Processing (NLP) algorithm, and multiple biological function similarity information is fused by Similarity Network Fusion (SNF). Secondly, we extract deep network structure information through Hierarchical Representation Learning for Networks (HARP). Then, a highly representative comprehensive feature descriptor is constructed through the self-attention module that efficiently integrates biochemical and network features. Finally, a deep neural network (DNN) is employed to generate the prediction results. Contrasted with the previous supervision model, BioChemDDI innovatively introduced graph collapse for extracting a network structure and utilized the biochemical information during the pre-training process. The prediction results of the benchmark dataset indicate that BioChemDDI outperforms other existing models. Moreover, the case studies related to three cancer diseases, including breast cancer, hepatocellular carcinoma and malignancies, were analyzed using BioChemDDI. As a result, 24, 18 and 20 out of the top 30 predicted cancer-related drugs were confirmed by the databases. These experimental results demonstrate that BioChemDDI is a useful model to predict DDIs and can provide reliable candidates for biological experiments. The web server of BioChemDDI predictor is freely available to conduct further studies.

Protein-protein interactions (PPIs) in plants are essential for understanding the regulation of biological processes. Although high-throughput technologies have been widely used to identify PPIs, they are usually laborious, expensive, and suffer from high false-positive rates. Therefore, it is imperative to develop novel computational approaches as a supplement tool to detect PPIs in plants. In this work, we presented a method, namely DST-RoF, to identify PPIs in plants by combining an ensemble learning classifier-Rotation Forest (RoF) with discrete sine transformation (DST). Specifically, plant protein sequence is firstly converted into Position-Specific Scoring Matrix (PSSM). Then, the discrete sine transformation was employed to extract effective features for obtaining the evolutionary information of proteins. Finally, these optimal features were fed into the RoF classifier for training and prediction. When performed on the plant datasets Arabidopsis, Rice, and Maize, DST-RoF yielded high prediction accuracy of 82.95%, 88.82%, and 93.70%, respectively. To further evaluate the prediction ability of our approach, we compared it with 4 state-of-the-art classifiers and 3 different feature extraction methods. Comprehensive experimental results anticipated that our method is feasible and robust for predicting potential plant-protein interacted pairs.

Satellite networks are essential to global connectivity yet face severe multidimensional cybersecurity threats. This systematic review conducts a holistic analysis of threats across the physical, network, and user layers. We propose the Sat-ATT&CK knowledge matrix to model satellite-specific attack chains. Corresponding defense technologies are organized within the core functions (Protect, Detect, Respond) of the National Institute of Standards and Technology (NIST) Cybersecurity Framework, establishing a structured threat–defense mapping. Furthermore, an exploratory case study on fine-tuning a large language model (SatSec) using the compiled literature corpus is presented. Finally, we identify key challenges and outline the future research directions toward a more resilient and intelligent security paradigm.

The Automatic Dependent Surveillance-Broadcast (ADS-B) is a key component of the new-generation air traffic surveillance system. However, it is vulnerable to security threats due to its plaintext transmission and lack of authentication mechanisms. Existing ADS-B anomaly detection methods still suffer from significant limitations, including low anomaly detection rates and limited adaptability. To address these issues, this paper proposes a novel ADS-B anomaly detection framework driven by large language models (LLMs). The approach utilizes pre-trained LLMs and a self-iterative prompt optimization loop, which integrates historical trajectories and multimodal features to refine expert-initialized prompts. The optimized prompts guide the LLM in identifying ADS-B anomalies. The advantage of the proposed ADS-B anomaly detection framework lies in overcoming the limitation of traditional model adaptation. Experimental results show that the proposed method achieves excellent performance on key metrics: the anomaly detection rate of 98.55%, the false alarm rate controlled at 3.61%, the miss detection rate reduced to 1.45%, and a recall of 96.39%. Compared to traditional detection methods, this method improves detection accuracy by an average of more than 12%. Furthermore, experiments on multi-type anomaly detection tasks validate that the framework exhibits strong adaptability and good generalization, providing effective technical support for the development of aviation data security protection systems.