Among biological networks, co-expression networks have been widely studied. One of the most commonly used pipelines for the construction of co-expression networks is weighted gene co-expression network analysis (WGCNA), which can identify highly co-expressed clusters of genes (modules). WGCNA identifies gene modules using hierarchical clustering. The major drawback of hierarchical clustering is that once two objects are clustered together, it cannot be reversed; thus, re-adjustment of the unbefitting decision is impossible. In this paper, we calculate the similarity matrix with the distance correlation for WGCNA to construct a gene co-expression network, and present a new approach called the k-module algorithm to improve the WGCNA clustering results. This method can assign all genes to the module with the highest mean connectivity with these genes. This algorithm re-adjusts the results of hierarchical clustering while retaining the advantages of the dynamic tree cut method. The validity of the algorithm is verified using six datasets from microarray and RNA-seq data. The k-module algorithm has fewer iterations, which leads to lower complexity. We verify that the gene modules obtained by the k-module algorithm have high enrichment scores and strong stability. Our method improves upon hierarchical clustering, and can be applied to general clustering algorithms based on the similarity matrix, not limited to gene co-expression network analysis.

Patients with estrogen receptor-negative breast cancer generally have a worse prognosis than estrogen receptor-positive patients. Nevertheless, a significant proportion of the estrogen receptor-negative cases have favorable outcomes. Identifying patients with a good prognosis, however, remains difficult, as recent studies are quite limited. The identification of molecular biomarkers is needed to better stratify patients. The significantly mutated genes may be potentially used as biomarkers to identify the subtype and to predict outcomes. To identify the biomarkers of receptor-negative breast cancer among the significantly mutated genes, we developed a workflow to screen significantly mutated genes associated with the estrogen receptor in breast cancer by a gene coexpression module. The similarity matrix was calculated with distance correlation to obtain gene modules through a weighted gene coexpression network analysis. The modules highly associated with the estrogen receptor, called important modules, were enriched for breast cancer-related pathways or disease. To screen significantly mutated genes, a new gene list was obtained through the overlap of the important module genes and the significantly mutated genes. The genes on this list can be used as biomarkers to predict survival of estrogen receptor-negative breast cancer patients. Furthermore, we selected six hub significantly mutated genes in the gene list which were also able to separate these patients. Our method provides a new and alternative method for integrating somatic gene mutations and expression data for patient stratification of estrogen receptor-negative breast cancers.

In the image edge detection, the bounds of step-structure and dirac-structure are distinguished by using modular-angle-separated wavelet transform, or combining with scale-independent algorithm, thus obtaining the bounds of the step-structure. But the connection with this algorithm's edge and response times of the same edge hasn't solved well. To solve this problem, a new edge detection method which combines the merit of canny edge detection algorithm with the above algorithm is given in this paper. Good effect is achieved in the image edge detection of the bound of step-structure. This algorithm is simple, useful and it's easy to realize.

Drug–drug interactions (DDIs) can severely affect patient health and safety. Predicting potential DDIs before administering medication to patients is crucial for drug development as it helps to prevent adverse drug reactions. Many effective DDI prediction methods have been proposed using graph neural networks; however, these methods only aggregate information from directly connected nodes restricted to a drug-related manner and fail to capture long-range dependencies in heterogeneous networks. To address this issue, we propose an enhanced multiple-feature hybrid graph method to predict DDIs (MFHG-DDI). Specifically, we constructed a heterogeneous network incorporating multiple drug features and DDI information. We employed an enhanced hybrid graph module that integrates a graph convolutional network, graph attention network, and global average pooling to learn latent features, ultimately applying a prediction function to predict DDIs. MFHG-DDI reframes the heterogeneous network as a graph classification task, capturing DDI information efficiently through an enhanced hybrid graph. Known DDI datasets were used to train and evaluate the proposed model. The experimental results indicate that integrating multiple drug features into the hybrid graph method can enhance the DDI prediction accuracy, increases the success rate of combination therapy, and has the potential to enhance drug safety.