Aims: Medical image translation is widely used for data augmentation and cross-domain adaptation in clinical image analysis. However, the nature of medical imaging makes it challenging to collect high-quality samples for the training of translation models. Because of the limited access to and costly expense of medical images, distribution bias is commonly observed between the source and target samples, and this finally leads the models to mismatch the target domain.

Methods: To promote medical image translation, a bias correction method, named content adaptation, has been proposed in this study to align the training samples in the data space. Based on the invariant medical topological structure, paired samples are constructed from weakly paired and unpaired data using content adaptation to correct the distribution bias and promote the image-to-image translation.

Results: Experiments on retinal fundus image translation and COVID-19 CT synthesis demonstrate that the proposed method effectively suppresses structural hallucination and improves both visual quality and quantitative performance. Consistent gains are observed across multiple backbone models under different supervision settings. The results suggest that explicit anatomical alignment provides an effective and model-agnostic way to mitigate distribution bias in medical image translation. By bridging weakly paired data with paired translation paradigms, the proposed approach enhances structural fidelity without requiring strong supervision.

Conclusion: This work presents a topology-guided content adaptation strategy that improves robustness and reduces hallucination in medical image translation. The proposed framework is general and can be readily integrated into existing translation models, offering a practical solution for data-scarce medical imaging scenarios.

Aims: Neural networks capable of capturing temporal dependencies in electroencephalogram (EEG) signals hold considerable potential for seizure prediction by modeling the progressive evolution of preictal EEG changes. However, redundant or less informative temporal features may obscure critical preictal patterns, limiting seizure prediction performance. To address this, we developed a neural architecture that effectively leverages informative temporal features to enhance seizure prediction capability.

Methods: We designed a bidirectional long short-term memory (BiLSTM) network enhanced with a channel attention mechanism, termed Attention-BiLSTM, which adaptively emphasizes informative temporal features while reducing information redundancy. We further analyze the model’s attention weights and feature distributions to provide interpretable insights into its decision-making process.

Results: Evaluation on the CHB-MIT scalp EEG dataset demonstrates that Attention-BiLSTM achieves significant performance improvements over the baseline BiLSTM, with an average accuracy of 94.77%, sensitivity of 94.58%, specificity of 94.97%, and an area under the curve of 98.38%. Furthermore, visualization results indicate that the proposed model progressively enhances feature discriminability and directs attention to the most relevant temporal features for seizure prediction.

Conclusion: The proposed Attention-BiLSTM achieves improved performance and interpretability, offering valuable insights to support future development of scalable and generalizable seizure prediction systems.

Aims: Accurately identifying diagnostic biomarkers for Autism Spectrum Disorder (ASD) is crucial for enabling early diagnosis and timely intervention. Brain causal networks, which outline causal relationships and information transmission pathways among different brain regions, hold significant diagnostic value for ASD. Identifying ASD-relevant causal relationships between brain regions is critical for ASD diagnosis and for elucidating its pathophysiology. This study proposes a new model called the Peter and Clark Momentary Conditional Independence (PCMCI)-Support Vector Machine (SVM) model, which can identify diagnostic biomarkers for ASD.

Methods: The brain blood oxygen level-dependent signals of 116 brain regions from 167 participants, consisting of 72 participants with ASD and 95 Typical Controls (TCs), were used to derive brain causal networks by detecting inter-regional causal relationships using five causal discovery algorithms; the PCMCI algorithm, spectral dynamic causal modeling, Granger causal analysis, Transfer Entropy, and Liang-Kleeman information flow causal analysis. Then, the brain causal relationships were fed into the SVM, Random Forest (RF), and K-Nearest Neighbors (KNN) classifiers, respectively to classify individuals with ASD and TCs, and thereby obtain a model suitable for identifying diagnostic biomarkers for ASD through causal network analysis.

Results: Experimental results demonstrate that the PCMCI-SVM model achieves an accuracy of 91.29% in ASD identification and outperforms the other four causal discovery algorithms, as well as the RF and KNN classifiers. Moreover, our analysis indicates that the right thalamus and right middle temporal gyrus are potential diagnostic biomarkers for ASD. Additionally, the causal relationship between [left inferior parietal→right insula] was found to be associated with the dorsal attention network, while [right cuneus→left cuneus] was associated with the visual network, suggesting that disruptions in these causal relationships may impair the functional integrity of their respective subnetworks.

Conclusion: Our findings show that cognitive processes and brain connectivity are largely influenced by causal interactions between different brain regions. These potential diagnostic biomarkers not only offer insights into the neurofunctional mechanisms of ASD but also hold promise for improving diagnostic accuracy for ASD.

Aims: Driver mutations are crucial factors in the occurrence and development of cancer. Identifying cancer-related driver genes is of great significance for understanding the mechanisms of cancer initiation, prevention, and treatment. With the continuous accumulation of cancer data, how to effectively utilize these data for the identification of cancer driver genes has become a major challenge in the field of cancer biology.

Methods: We propose a novel computational model called iCDG-MOHGAT. This model integrates multi-omics pan-cancer data (such as mutations, DNA methylation, etc.), multi-dimensional gene networks, and disease semantic similarity networks to identify cancer driver genes. We first construct multi-dimensional gene networks using various types of gene correlation information (protein-protein interaction, gene sequence similarity, etc.) and establish disease semantic similarity networks for relevant cancers. Due to the complexity of node and edge types, we utilize a heterogeneous graph attention network to learn and extract features from the multi-dimensional gene networks and disease semantic similarity networks. We also incorporate a fusion learning module to effectively integrate features from different dimensions. Finally, we optimize the random forest classifier using the sparrow algorithm for the task of predicting cancer driver genes.

Results: Experimental results demonstrate that iCDG-MOHGAT outperforms many state-of-the-art models in terms of AUPR and AUROC. In the final prediction results, 91% of the predicted new driver genes have at least one supporting evidence of being cancer genes. In the laboratory, this model can serve as an effective tool for identifying cancer driver genes.

Conclusion: We have introduced a novel computational model named iCDG-MOHGAT, which precisely identifies cancer driver genes by integrating multi-omics pan-cancer data and intricate multidimensional gene networks, coupled with disease semantic similarity networks. Experimental results demonstrate that iCDG-MOHGAT outperforms many state-of-the-art models in terms of AUPR and AUROC. In the final prediction results, 91% of the predicted genes have supporting evidence. In the laboratory, this model can serve as an effective tool for identifying cancer driver genes.

Aims: Drug-target affinity (DTA) prediction is crucial for drug discovery and repositioning. However, existing deep learning-based methods often overlook the synergy between the topological structure of DTA networks and the multimodal features of drugs and targets themselves.

Methods: This study proposes a new method, MIGDTA, a DTA prediction method based on multi-source information and graph convolutional network (GCN), which enhances prediction accuracy by integrating local features with global interaction information. MIGDTA first constructs a drug molecular graph, a target protein graph, and a DTA network, while computing molecular fingerprints and protein descriptors. Subsequently, it employs a graph isomorphism network to learn graph features, a GCN to capture network features, and a multilayer prceptron to encode biological features. Then, it refines heterogeneous network and graph features iteratively through the GCN, and finally concatenates the fused features with biological features for affinity prediction.

Results: Comparative experiments on benchmark datasets demonstrate that MIGDTA significantly outperforms existing methods. On the Davis dataset, compared to the best baseline method, MIGDTA reduces mean squared error (MSE) to 0.185, increases CI by 0.006, and improves $r_m^2$ by 5%. Similar enhancements were observed on the KIBA dataset, where MIGDTA achieves an MSE of 0.130, along with 0.002 and 1% gains in CI and $r_m^2$, respectively.

Conclusion: Feature ablation studies verify the core role of graph features in modeling local structures and network features in capturing global topology, along with the supplementary importance of biological features. Comparative analyses of feature integration approaches confirm the effectiveness of the feature refinement module in fusing multimodal features and enhancing model discriminability.