Predicting the gene mutation status in whole slide images (WSIs) is crucial for the clinical treatment, cancer management, and research of gliomas. With advancements in CNN and Transformer algorithms, several promising models have been proposed. However, existing studies have paid little attention on fusing multi-magnification information, and the model requires processing all patches from a whole slide image. In this paper, we propose a cross-magnification attention model called CroMAM for predicting the genetic status and survival of gliomas. The CroMAM first utilizes a systematic patch extraction module to sample a subset of representative patches for downstream analysis. Next, the CroMAM applies Swin Transformer to extract local and global features from patches at different magnifications, followed by acquiring high-level features and dependencies among single-magnification patches through the application of a Vision Transformer. Subsequently, the CroMAM exchanges the integrated feature representations of different magnifications and encourage the integrated feature representations to learn the discriminative information from other magnification. Additionally, we design a cross-magnification attention analysis method to examine the effect of cross-magnification attention quantitatively and qualitatively which increases the model's explainability. To validate the performance of the model, we compare the proposed model with other multi-magnification feature fusion models on three tasks in two datasets. Extensive experiments demonstrate that the proposed model achieves state-of-the-art performance in predicting the genetic status and survival of gliomas.

Novel drug-target interaction (DTI) prediction is crucial in drug discovery and repositioning. Recently, graph neural network (GNN) has shown promising results in identifying DTI by using thresholds to construct heterogeneous graphs. However, an empirically selected threshold can lead to loss of valuable information, especially in sparse networks, a common scenario in DTI prediction. To make full use of insufficient information, we propose a DTI prediction model based on Dynamic Heterogeneous Graph (DT-DHG). And progressive learning is introduced to adjust the receptive fields of node. The experimental results show that our method significantly improves the performance of the original GNNs and is robust against the choices of backbones. Meanwhile, DT-DHG outperforms the state-of-the-art methods and effectively predicts novel DTIs.

Synchronization (insertions-deletions) errors are still a major challenge for reliable information retrieval in DNA storage. Unlike traditional error correction codes (ECC) that add redundancy in the stored information, multiple sequence alignment (MSA) solves this problem by searching the conserved subsequences. In this paper, we conduct a comprehensive simulation study on the error correction capability of a typical MSA algorithm, MAFFT. Our results reveal that its capability exhibits a phase transition when there are around 20% errors. Below this critical value, increasing sequencing depth can eventually allow it to approach complete recovery. Otherwise, its performance plateaus at some poor levels. Given a reasonable sequencing depth (≤ 70), MSA could achieve complete recovery in the low error regime, and effectively correct 90% of the errors in the medium error regime. In addition, MSA is robust to imperfect clustering. It could also be combined with other means such as ECC, repeated markers, or any other code constraints. Furthermore, by selecting an appropriate sequencing depth, this strategy could achieve an optimal trade-off between cost and reading speed. MSA could be a competitive alternative for future DNA storage.

BACKGROUND: During the pathogenesisof complex diseases, a sudden health deterioration will occur as results of the cumulative effect of various internal or external factors. The prediction of an early warning signal (pre-disease state) before such deterioration is very important in clinical practice, especially for a single sample. The single-sample landscape entropy (SLE) was proposed to tackle this issue. However, the PPI used in SLE was lack of definite biological meanings. Besides, the calculation of multiple correlations based on limited reference samples in SLE is time-consuming and suspect. RESULTS: Abnormal signals generally exert their effect through the static definite biological functions in signaling pathways across the development of diseases. Thus, it is a natural way to study the propagation of the early-warning signals based on the signaling pathways in the KEGG database. In this paper, we propose a signaling perturbation method named SSP, to study the early-warning signal in signaling pathways for single dynamic time-series data. Results in three real datasets including the influenza virus infection, lung adenocarcinoma, and acute lung injury show that the proposed SSP outperformed the SLE. Moreover, the early-warning signal can be detected by one important signaling pathway PI3K-Akt. CONCLUSIONS: These results all indicate that the static model in pathways could simplify the detection of the early-warning signals.

BACKGROUND: MicroRNAs are a class of important small noncoding RNAs, which have been reported to be involved in the processes of tumorigenesis and development by targeting a few genes. Existing studies show that the imbalance between cell proliferation and apoptosis is closely related to the initiation and development of cancers. However, the impact of miRNAs on this imbalance has not been studied systematically. RESULTS: In this study, we first construct a cell fate miRNA-gene regulatory network. Then, we propose a systematical method for calculating the global impact of miRNAs on cell fate genes based on the shortest path. Results on breast cancer and liver cancer datasets show that most of the cell fate genes are perturbed by the differentially expressed miRNAs. Most of the top-identified miRNAs are verified in the Human MicroRNA Disease Database (HMDD) and are related to breast and liver cancers. Function analysis shows that the top 20 miRNAs regulate multiple cell fate related function modules and interact tightly based on their functional similarity. Furthermore, more than half of them can promote sensitivity or induce resistance to some anti-cancer drugs. Besides, survival analysis demonstrates that the top-ranked miRNAs are significantly related to the overall survival time in the breast and liver cancers group. CONCLUSION: In sum, this study can help to systematically study the important role of miRNAs on proliferation and apoptosis and thereby uncover the key miRNAs during the process of tumorigenesis. Furthermore, the results of this study will contribute to the development of clinical therapy based miRNAs for cancers.