Aims: To develop a robust and clinically feasible framework for cancer survival prediction using only histopathology images while leveraging transcriptomic knowledge during training.

Aims: To develop a robust and clinically feasible framework for cancer survival prediction using only histopathology images while leveraging transcriptomic knowledge during training.

Methods: The study proposed Adaptive Multi-modality Knowledge Distillation (AMKD), a framework designed to transfer complementary molecular-level information from transcriptomic data to pathology-based models. The AMKD framework consists of two essential elements. First, a gene-guided pathology enhancement module is designed to inject genomics-aware information from a multimodal teacher into pathology features. Second, an adaptive redundancy reduction loss is introduced to regulate knowledge distillation by accounting for prediction discrepancies between teacher and student models. This design allows the student model to retain biologically meaningful knowledge during training and remain effective with only histopathology data at inference.

Results: Comprehensive experiments on four The Cancer Genome Atlas (TCGA) cancer cohorts demonstrate that AMKD achieves state-of-the-art survival prediction performance, with an average concordance index (C-index) of 0.669, surpassing both unimodal pathology-based and fully multi-modal approaches. External validation on the independent Clinical Proteomic Tumor Analysis Consortium head and neck squamous cell carcinoma (CPTAC-HNSC) cohort further confirms the robustness and cross-dataset generalization ability of the proposed framework. Ablation studies further confirm the effectiveness of each proposed component in enhancing cross-modal knowledge transfer and improving generalization under incomplete modality scenarios.

Conclusion: The proposed AMKD framework provides a clinically practical solution for robust cancer survival analysis when transcriptomic data are unavailable. By adaptively distilling multi-modal knowledge into a pathology-based model, AMKD bridges the gap between research and clinical applicability, enabling scalable and cost-effective prognostic prediction in real-world settings.

Aims: Since distal enhancers are involved in regulating target genes through physical contacting with proximal promoters, identifying enhancer-promoter interactions (EPIs) is critical to deepening our understanding of gene expression. However, high-throughput experimental methods for identifying EPIs are time-consuming and expensive. Therefore, computational methods for predicting EPIs would be valuable and important, but also face a lot of challenges.

Methods: In this paper, we propose a novel deep learning-based method, namely EPIPAM, to predict EPIs only using genomic sequences. EPIPAM firstly uses a deep convolutional neural network to extract high-level sequence features, and then uses a position attention mechanism to compute the positional correlation coefficients of two subregions separately coming from enhancers and promoters, aiming to focus on important regions of them.

Results: Benchmarking comparisons on six different cell lines show that EPIPAM performs better than the state-of-the-art methods in the task of EPIs prediction. More importantly, we notice that, almost without exception, the predictive performance of all methods is really poor once applying a strategy of splitting training and test data by chromosome. Therefore, we explain the possible reason that leads to this situation by systematically exploring the structure of EPI datasets, and indirectly analyze the difficulty of predicting EPIs only using genomic sequences through ChIA-PET contact datasets.

Conclusion: This study presents a novel deep learning-based method to predict EPIs only using genomic sequences. Although the proposed method achieves higher predictive accuracy, it suffers from several limitations, such as highly selective matching bias, negative sample selection issues, and constraints of pre-trained vectors.

Aims: EffecTri aims to develop a comprehensive, multi-class prediction framework to accurately identify bacterial effector proteins secreted by Type III, IV, and VI secretion systems. Current methodologies often employ binary classifications, overlooking the complexity and interactions among multiple effector classes.

Methods: EffecTri integrates deep contextual embeddings from Evolutionary Scale Modeling and handcrafted descriptors, including Amino Acid Composition and Dipeptide Composition. The performance of the model was rigorously evaluated through comparative descriptor analyses and optimized feature combinations, complemented by Uniform Manifold Approximation and Projection visualization for interpretability.

Results: EffecTri outperformed traditional machine learning methods, achieving a weighted F1-score of 0.850 on an independent test dataset. The fusion of Evolutionary Scale Modeling embeddings with handcrafted descriptors demonstrated superior predictive performance, clearly distinguishing effector classes in UMAP visualizations.

Conclusion: EffecTri represents a robust, interpretable, and accurate computational tool, enhancing the multi-class identification of bacterial secretory effectors and contributing valuable insights into bacterial pathogenic mechanisms.