Multiple Relevance Vector Machine Research Articles

Prediction of Genetic Factors of Hyperthyroidism Based on Gene Interaction Network.

The number of hyperthyroidism patients is increasing these years. As a disease that can lead to cardiovascular disease, it brings great potential health risks to humans. Since hyperthyroidism can induce the occurrence of many diseases, studying its genetic factors will promote the early diagnosis and treatment of hyperthyroidism and its related diseases. Previous studies have used genome-wide association analysis (GWAS) to identify genes related to hyperthyroidism. However, these studies only identify significant sites related to the disease from a statistical point of view and ignore the complex regulation relationship between genes. In addition, mutation is not the only genetic factor of causing hyperthyroidism. Identifying hyperthyroidism-related genes from gene interactions would help researchers discover the disease mechanism. In this paper, we purposed a novel machine learning method for identifying hyperthyroidism-related genes based on gene interaction network. The method, which is called “RW-RVM,” is a combination of Random Walk (RW) and Relevance Vector Machines (RVM). RW was implemented to encode the gene interaction network. The features of genes were the regulation relationship between genes and non-coding RNAs. Finally, multiple RVMs were applied to identify hyperthyroidism-related genes. The result of 10-cross validation shows that the area under the receiver operating characteristic curve (AUC) of our method reached 0.9, and area under the precision-recall curve (AUPR) was 0.87. Seventy-eight novel genes were found to be related to hyperthyroidism. We investigated two genes of these novel genes with existing literature, which proved the accuracy of our result and method.

Risk Assessment of Groundwater Depletion Induced Land Subsidence: A Case Study in Taiyuan Basin, China

Groundwater depletion induced land subsidence affects the safety of local communities. In this research, a data-driven approach is applied to predict and assess the risk level of land subsidence in Taiyuan Basin, Shanxi Province, China based on Geographic Information Systems data and field investigation data. First, the relevance vector machine is introduced to model and classify the risky/non-risky ground subsidence cases. Next, an ensemble classifier using multiple relevance vector machines and elastic net is proposed in this research. Multiple land subsidence locations in Taiyuan Basin have been investigated in this study. Five benchmarking machine learning classification algorithms including decision tree, random forest, multi-layer perceptron, support vector machine and classical relevance vector machine have been compared in this study. Four evaluation metrics including accuracy, sensitivity, specificity and area under the receiver operating characteristic curve have been introduced to assess the classification performance of all the algorithms tested. Computational results demonstrated the outperformance of the proposed approach in classifying risky land subsidence cases compared with other benchmarking algorithms.

Obstacle classification and 3D measurement in unstructured environments based on ToF cameras.

Inspired by the human 3D visual perception system, we present an obstacle detection and classification method based on the use of Time-of-Flight (ToF) cameras for robotic navigation in unstructured environments. The ToF camera provides 3D sensing by capturing an image along with per-pixel 3D space information. Based on this valuable feature and human knowledge of navigation, the proposed method first removes irrelevant regions which do not affect robot's movement from the scene. In the second step, regions of interest are detected and clustered as possible obstacles using both 3D information and intensity image obtained by the ToF camera. Consequently, a multiple relevance vector machine (RVM) classifier is designed to classify obstacles into four possible classes based on the terrain traversability and geometrical features of the obstacles. Finally, experimental results in various unstructured environments are presented to verify the robustness and performance of the proposed approach. We have found that, compared with the existing obstacle recognition methods, the new approach is more accurate and efficient.