Discovering context-specific relationships from biological literature by using multi-level context terms

<p>Abstract</p> <p>Background</p> <p>The Swanson's ABC model is powerful to infer hidden relationships buried in biological literature. However, the model is inadequate to infer relations with context information. In addition, the model generates a very large amount of candidates from biological text, and it is a semi-automatic, labor-intensive technique requiring human expert's manual input. To tackle these problems, we incorporate context terms to infer relations between AB interactions and BC interactions.</p> <p>Methods</p> <p>We propose 3 steps to discover meaningful hidden relationships between drugs and diseases: 1) multi-level (gene, drug, disease, symptom) entity recognition, 2) interaction extraction (drug-gene, gene-disease) from literature, 3) context vector based similarity score calculation. Subsequently, we evaluate our hypothesis with the datasets of the "Alzheimer's disease" related 77,711 PubMed abstracts. As golden standards, PharmGKB and CTD databases are used. Evaluation is conducted in 2 ways: first, comparing precision of the proposed method and the previous method and second, analysing top 10 ranked results to examine whether highly ranked interactions are truly meaningful or not.</p> <p>Results</p> <p>The results indicate that context-based relation inference achieved better precision than the previous ABC model approach. The literature analysis also shows that interactions inferred by the context-based approach are more meaningful than interactions by the previous ABC model.</p> <p>Conclusions</p> <p>We propose a novel interaction inference technique that incorporates context term vectors into the ABC model to discover meaningful hidden relationships. By utilizing multi-level context terms, our model shows better performance than the previous ABC model.</p

No Evidence for Genome-Wide Interactions on Plasma Fibrinogen by Smoking, Alcohol Consumption and Body Mass Index : Results from Meta-Analyses of 80,607 Subjects

Peer reviewe

Statistical Methods for Wearable Sensor Data

Recently, wearable sensors have emerged as promising tools for collecting behavioral data in free-living conditions. For example, physical activity monitors have been used in large population-based studies such as the National Health and Nutrition Examination Survey and the UK Biobank to track participants’ levels of physical activity in their free-living environment. Such large, population-based studies puts forward unprecedented opportunities in exploring demographic, biological, behavioral, and genetic factors associated with physical activity in free living conditions. However, analyzing the large scale behavioral data collected using the wearable devices may warrant special attention for a variety of reasons. First, the device wear times can be highly variable within- and between- individuals over the measurement days, and may be associated with the measurement outcome such as minutes in moderate to vigorous physical activity (informative observation times). Second, study participants may stop wearing the device from a certain measurement day and onwards (censored observations). Third, the early termination of the wearable sensor monitoring may be related to the measured outcome (informative censoring). Fourth, exploration of large number of potential correlates to the wearable sensor measured outcome may necessitate computationally efficient methods. Lastly, rapid developments in the high-resolution sensor technology have demanded more accurate methods for extracting activity intensity features from these data. The overall goal of this study was to develop novel statistical methods to account for the aforementioned challenges in analyzing data from modern wearable devices, scalable for exploring large population level datasets utilizing the state-of-the-art wearable sensor technology

Correlation Analysis of Noise, Vibration, and Harshness in a Vehicle Using Driving Data Based on Big Data Analysis Technique

A new development process for the noise, vibration, and harshness (NVH) of a vehicle is presented using data analysis and machine learning with long-term NVH driving data. The process includes exploratory data analysis (EDA), variable importance analysis, correlation analysis, sensitivity analysis, and development target selection. In this paper, to dramatically reduce the development period and cost related to vehicle NVH, we propose a technique that can accurately identify the precise connectivity and relationship between vehicle systems and NVH factors. This new technique uses whole big data and reflects the nonlinearity of dynamic characteristics, which was not considered in existing methods, and no data are discarded. Through the proposed method, it is possible to quickly find areas that need improvement through correlation analysis and variable importance analysis, understand how much room noise increases when the NVH level of the system changes through sensitivity analysis, and reduce vehicle development time by improving efficiency. The method could be used in the development process and the validation of other deep learning and machine learning models. It could be an essential step in applying artificial intelligence, big data, and data analysis in the vehicle and mobility industry as a future vehicle development process

Improved Vulnerability Assessment Table for Retaining Walls and Embankments from a Working-Level Perspective in Korea

Climate change can lead to unpredictable slope collapse, which causes human casualties. Therefore, Korea has devoted significant effort to the management of slope disasters. The Ministry of the Interior and Safety of Korea, which oversees the safety of the nation&rsquo;s people, has allocated a four-year budget of $557 million to investigate, assess, and maintain steep slope sites. However, there have been fatalities caused by steep slope site evaluations based on inadequate knowledge and a single retaining walls and embankments (RW&amp;E) assessment table. Therefore, the assessment table for RW&amp;E-type steep slopes needs to be improved in terms of its accuracy, simplicity, and ease of use. In this study, domestic and global evaluation methods were reviewed, problems associated with the existing RW&amp;E assessment table were identified, and a focus group interview was conducted. The RW&amp;E assessment table was improved through an indicator feasibility survey and analytic hierarchy processing. The improved assessment table was categorized from one to four classifications to reduce the ambiguity of the evaluation: concrete, reinforced soil-retaining walls, stone embankments, and gabions. This study will provide the sustainability of slope safety and serve as a reference for classification and evaluation criteria across all national institutions that conduct RW&amp;E evaluations

BayesESS: A tool for quantifying the impact of parametric priors in Bayesian analysis

Bayesian inference has become an attractive choice for scientists seeking to incorporate prior knowledge into their modeling framework. While the R community has been an important contributor in facilitating Bayesian statistical analyses, software to evaluate the impact of prior knowledge to such modeling framework has been lacking. In this article, we present BayesESS, a comprehensive, free, and open source R package for quantifying the impact of parametric priors in Bayesian analysis. We also introduce an accompanying web-based application for estimating and visualizing Bayesian effective sample size for purposes of conducting or planning Bayesian analyses