Axillary lymph node (ALN) metastasis is a common form of breast cancer metastasis. Understanding molecular mechanisms of ALN metastasis may provide new therapeutic strategy for breast cancer. To explore the functional evolution process of ALN metastasis in breast cancer, we proposed a network model to identify functional modules of ALN metastasis by clustering the pathway evolution network. First, a gene co-expression network was constructed for each ALN stage separately, and integrated gene expression, DNA methylation, and copy number variation as node attributes information. Second, integrative non-negative matrix factorization (IntNMF) algorithm was used to integrate multi-omics data to detect gene co-expression network modules. Next, the pathway evolution network for ALN metastasis was built by integrating enriched pathways within gene co-expression network modules at each stage. Finally, the topological and functional similarity-based module detection algorithm was proposed to identify functional modules from the pathway evolution network, and seven modules were obtained. Compared with other classical algorithms, the proposed algorithm achieved better similarity cohesion while satisfying the structural cohesion. This study identified functional modules of ALN metastasis by clustering the pathway evolution network, which may provide insights into exploring the functional evolution process of ALN metastasis and finding drug targets for breast cancer.

Traditional graph clustering methods consist of two sequential steps, i.e., constructing an affinity matrix from the original data and then performing spectral clustering on the resulting affinity matrix. This two-step strategy achieves optimal solution for each step separately, but cannot guarantee that it will obtain the globally optimal clustering results. Moreover, the affinity matrix directly learned from the original data will seriously affect the clustering performance, since high-dimensional data are usually noisy and may contain redundancy. To address the above issues, this paper proposes a Low-rank Sparse Subspace (LSS) clustering method via dynamically learning the affinity matrix from low-dimensional space of the original data. Specifically, we learn a transformation matrix to project the original data to their low-dimensional space, by conducting feature selection and subspace learning in the sample self-representation framework. Then, we utilize the rank constraint and the affinity matrix directly obtained from the original data to construct a dynamic and intrinsic affinity matrix. Moreover, each of these three matrices is updated iteratively while fixing the other two. In this way, the affinity matrix learned from the low-dimensional space is the final clustering results. Extensive experiments are conducted on both synthetic and real datasets to show that our proposed LSS method outperforms the state-of-the-art clustering methods.

Object recognition is one of the most popular tasks in computer vision. Various works have been reported in recent years. Bag of visual words (BOVW) is shown to be an effective model for this task. BOVW extracts features or visual words from the whole image. However, we know there are two kinds of information in an image: objects and background. Obviously, the features in the objects and background are not equivalently important for recognizing the object. In this paper, we propose a novel solution to considering the different regions. Intuitively speaking, the object in an image is more salient than the background. Based on this observation, we use a salience detection technique to divide an image into three regions: objects, boundary and background. Then we extract features from these regions, respectively. The features are finally integrated with an multi-kernel SVM classifier. We conduct experiments on some datasets, and the results show that our approach outperforms the traditional BOVW and weighted BOVW.