Accumulating experimental studies have indicated that lncRNAs play important roles in various critical biological process and their alterations and dysregulations have been associated with many important complex diseases. Developing effective computational models to predict potential disease-lncRNA association could benefit not only the understanding of disease mechanism at lncRNA level, but also the detection of disease biomarkers for disease diagnosis, treatment, prognosis and prevention. However, known experimentally confirmed disease-lncRNA associations are still very limited. In this study, a novel model of HyperGeometric distribution for LncRNA-Disease Association inference (HGLDA) was developed to predict lncRNA-disease associations by integrating miRNA-disease associations and lncRNA-miRNA interactions. Although HGLDA didn’t rely on any known disease-lncRNA associations, it still obtained an AUC of 0.7621 in the leave-one-out cross validation. Furthermore, 19 predicted associations for breast cancer, lung cancer, and colorectal cancer were verified by biological experimental studies. Furthermore, the model of LncRNA Functional Similarity Calculation based on the information of MiRNA (LFSCM) was developed to calculate lncRNA functional similarity on a large scale by integrating disease semantic similarity, miRNA-disease associations, and miRNA-lncRNA interactions. It is anticipated that HGLDA and LFSCM could be effective biological tools for biomedical research.

Increasing observations have indicated that lncRNAs play a significant role in various critical biological processes and the development and progression of various human diseases. Constructing lncRNA functional similarity networks could benefit the development of computational models for inferring lncRNA functions and identifying lncRNA-disease associations. However, little effort has been devoted to quantifying lncRNA functional similarity. In this study, we developed an Improved LNCRNA functional SIMilarity calculation model (ILNCSIM) based on the assumption that lncRNAs with similar biological functions tend to be involved in similar diseases. The main improvement comes from the combination of the concept of information content and the hierarchical structure of disease directed acyclic graphs for disease similarity calculation. ILNCSIM was combined with the previously proposed model of Laplacian Regularized Least Squares for lncRNA-Disease Association to further evaluate its performance. As a result, new model obtained reliable performance in the leave-one-out cross validation (AUCs of 0.9316 and 0.9074 based on MNDR and Lnc2cancer databases, respectively), and 5-fold cross validation (AUCs of 0.9221 and 0.9033 for MNDR and Lnc2cancer databases), which significantly improved the prediction performance of previous models. It is anticipated that ILNCSIM could serve as an effective lncRNA function prediction model for future biomedical researches.

In this paper, we propose an electronic nose for non-invasive detection and diagnosis of lung cancer based on a kind of virtual array of surface acoustic wave (SAW) gas sensors and an imaging recognition method. It includes a gas path constructed from a two-bag system, solid phase micro extraction (SPME) and a capillary column to pre-concentrate and separate volatile organic compounds (VOCs) in patients' exhaled air. A pair of SAW sensors, one coated with a thin polyisobutylene (PIB) film, is used to detect chemical compounds. Eleven VOCs that are validated as the markers of lung cancer according to a pathology study can be detected qualitatively and quantitatively by this electronic nose. Then, an improved artificial neural network (ANN) algorithm combined with an imaging method is proposed for the recognition of patients. In addition, the concept of a virtual sensors array based on SAW sensors using a capillary column separation technique and imaging is also proposed to simulate a large scale of sensor array response. Finally this e-nose is calibrated by these 11 VOCs separated in three concentrations and is used to diagnose lung cancer patients in Run Run Shaw hospital. The experimental results show that this kind of electronic nose is effective in the recognition of lung cancer patients.