Abstract Identification of cancer-related genes is helpful for understanding the pathogenesis of cancer, developing targeted drugs and creating new diagnostic and therapeutic methods. Considering the complexity of the biological laboratory methods, many network-based methods have been proposed to identify cancer-related genes at the global perspective with the increasing availability of high-throughput data. Some studies have focused on the tissue-specific cancer networks. However, cancers from different tissues may share common features, and those methods may ignore the differences and similarities across cancers during the establishment of modeling. In this work, in order to make full use of global information of the network, we first establish the pan-cancer network via differential network algorithm, which not only contains heterogeneous data across multiple cancer types but also contains heterogeneous data between tumor samples and normal samples. Second, the node representation vectors are learned by network embedding. In contrast to ranking analysis-based methods, with the help of integrative network analysis, we transform the cancer-related gene identification problem into a binary classification problem. The final results are obtained via ensemble classification. We further applied these methods to the most commonly used gene expression data involving six tissue-specific cancer types. As a result, an integrative pan-cancer network and several biologically meaningful results were obtained. As examples, nine genes were ultimately identified as potential pan-cancer-related genes. Most of these genes have been reported in published studies, thus showing our method’s potential for application in identifying driver gene candidates for further biological experimental verification.
Many disease-related genes have been found to be associated with cancer diagnosis, which is useful for understanding the pathophysiology of cancer, generating targeted drugs, and developing new diagnostic and treatment techniques. With the development of the pan-cancer project and the ongoing expansion of sequencing technology, many scientists are focusing on mining common genes from The Cancer Genome Atlas (TCGA) across various cancer types. In this study, we attempted to infer pan-cancer associated genes by examining the microbial model organism Saccharomyces Cerevisiae (Yeast) by homology matching, which was motivated by the benefits of reverse genetics. First, a background network of protein-protein interactions and a pathogenic gene set involving several cancer types in humans and yeast were created. The homology between the human gene and yeast gene was then discovered by homology matching, and its interaction sub-network was obtained. This was undertaken following the principle that the homologous genes of the common ancestor may have similarities in expression. Then, using bidirectional long short-term memory (BiLSTM) in combination with adaptive integration of heterogeneous information, we further explored the topological characteristics of the yeast protein interaction network and presented a node representation score to evaluate the node ability in graphs. Finally, homologous mapping for human genes matched the important genes identified by ensemble classifiers for yeast, which may be thought of as genes connected to all types of cancer. One way to assess the performance of the BiLSTM model is through experiments on the database. On the other hand, enrichment analysis, survival analysis, and other outcomes can be used to confirm the biological importance of the prediction results. You may access the whole experimental protocols and programs at https://github.com/zhuyuan-cug/AI-BiLSTM/tree/master.
The coordination of power system and power electronics-enabled devices can cost-effectively enhance the system transient performance. This paper proposes an advanced nonlinear control method for the coordination of power systems with capacitive-coupled static synchronous compensator (STATCOM). In this method, multiple error surfaces of the time-domain system are constructed based on low-pass filter to avoid the inherent differential explosion of backstepping design. Such dynamic surface control is further combined with fixed-time/preassigned-time control to ensure the expected transient response. Lyapunov function is designed to resolve the closed-loop dynamics of power systems with capacitive-coupled STATCOM. It is proved that the proposed advanced nonlinear control method can ensure the semi-globally fixed-timely uniformly ultimately bounded (SFUUB) within estimable settling time. Case studies on a classical power system are implemented to show its effective and superior transient performances.
The inherent fluctuations of high penetrated renewables energy sources in power system results in larger and more sharp frequency deviation, which call for faster frequency response and fast load frequency control (LFC). This paper innovatively envisions LFC in power system with fixed-time dynamic surface method. Firstly, the virtual control law is designed through backstepping design to ensure the asymptotic stability of each subsystem. Additionally, dynamic surface control (DSC) is adopted to eliminate the influence by adding a first-order filter in the backstepping design. By using the fixed-time theory, the frequency of the LFC for power system reaches stability in a predetermined time. Finally, the simulation of the power system proves the effectiveness of the proposed method.
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