Increasing number of experiments show that lncRNAs are involved in many biological processes, and their mutations and disorders are associated with many diseases. However, verifying the relationships between lncRNAs and diseases is time consuming and laborio. Searching for effective computational methods will contribute to our understanding of the underlying mechanisms of disease and identifying biomarkers of diseases. Therefore, we proposed a method called lncRNA-disease association prediction based on linear neighborhood similarity and unbalanced bi-random walk (LDA-LNSUBRW). Given that the known lncRNA-disease associations are rare, a pretreatment step should be performed to obtain the interaction possibility of unknown cases, so as to help us predict the potential associations. In the framework of leave-one-out cross-validation (LOOCV) and fivefold cross-validation (5-fold CV), LDA-LNSUBRW achieved effective performance with AUC of 0.8874 and 0.8632 ± 0.0051, respectively. The experimental results in this paper show that the proposed method is superior to five other state-of-the-art methods. In addition, case studies of three diseases (lung cancer, breast cancer, and osteosarcoma) were carried out to illustrate that LDA-LNSUBRW could predict the relevant lncRNAs.

In recent years, an increasing number of biological experiments and clinical reports have shown that lncRNA is closely related to the development of various complex human diseases. Therefore, studying the relationship between lncRNA and disease is necessary. Doing so not only helps to understand the disease mechanism, but also facilitates the diagnosis, treatment, and prognosis of disease. However, understanding the relationship between lncRNA and disease through biological experiments and clinical studies requires considerable time and money. Over the years, many researchers have developed computational methods to predict potential lncRNA–disease associations. In this study, on the basis of the assumption that functionally similar lncRNAs tend to associate with phenotypically similar diseases, and vice versa, we propose a novel computational method called network consistency projection for human lncRNA–disease associations (NCPHLDA) to predict potential lncRNA–disease associations. This method integrates a lncRNA cosine similarity network, a disease cosine similarity network, and the known lncRNA–disease association network. NCPHLDA is not only a parameterless method but also does not require a negative sample. More importantly, NCPHLDA can predict lncRNA without any known associated diseases. AUC values of 0.9273 and 0.9179 ± 0.0043 are obtained by implementing leave-one-out cross-validation and 5-fold cross-validation for NCPHLDA, respectively. Case studies of three diseases (breast cancer, cervical cancer, and hepatocellular carcinoma) indicate that NCPHLDA has reliable predictive performance. The source code of NCPHLDA is freely available at https://github.com/bryanze/NCPHLDA.

The global transition toward renewable energy and the electrification of transportation is imposing unprecedented power quality (PQ) challenges on modern distribution networks, rendering traditional governance models inadequate. To bridge the existing research gap of the lack of a holistic analytical framework, this review first establishes a systematic diagnostic methodology by introducing the “Triadic Governance Objectives–Scenario Matrix (TGO-SM),” which maps core objectives—harmonic suppression, voltage regulation, and three-phase balancing—against the distinct demands of high-penetration photovoltaic (PV), electric vehicle (EV) charging, and energy storage scenarios. Building upon this problem identification framework, the paper then provides a comprehensive review of advanced mitigation technologies, analyzing the performance and application of key ‘unit operations’ such as static synchronous compensators (STATCOMs), solid-state transformers (SSTs), grid-forming (GFM) inverters, and unified power quality conditioners (UPQCs). Subsequently, the review deconstructs the multi-timescale control conflicts inherent in these systems and proposes the forward-looking paradigm of “Distributed Dynamic Collaborative Governance (DDCG).” This future architecture envisions a fully autonomous grid, integrating edge intelligence, digital twins, and blockchain to shift from reactive compensation to predictive governance. Through this structured approach, the research provides a coherent strategy and a crucial theoretical roadmap for navigating the complexities of modern distribution grids and advancing toward a resilient and autonomous future.