Prioritizing disease candidate genes by a gene interconnectedness-based approach

<p>Abstract</p> <p>Background</p> <p>Genome-wide disease-gene finding approaches may sometimes provide us with a long list of candidate genes. Since using pure experimental approaches to verify all candidates could be expensive, a number of network-based methods have been developed to prioritize candidates. Such tools usually have a set of parameters pre-trained using available network data. This means that re-training network-based tools may be required when existing biological networks are updated or when networks from different sources are to be tried.</p> <p>Results</p> <p>We developed a parameter-free method, interconnectedness (ICN), to rank candidate genes by assessing the closeness of them to known disease genes in a network. ICN was tested using 1,993 known disease-gene associations and achieved a success rate of ~44% using a protein-protein interaction network under a test scenario of simulated linkage analysis. This performance is comparable with those of other well-known methods and ICN outperforms other methods when a candidate disease gene is not directly linked to known disease genes in a network. Interestingly, we show that a combined scoring strategy could enable ICN to achieve an even better performance (~50%) than other methods used alone.</p> <p>Conclusions</p> <p>ICN, a user-friendly method, can well complement other network-based methods in the context of prioritizing candidate disease genes.</p

Multiplayer Serious Games Supporting Programming Learning

Computational thinking (CT) is crucial in education for providing a multifaceted approach to problem-solving. However, challenges exist such as supporting teachers' knowledge of CT and students' desire to learn it, particularly for non-technical students. To combat these challenges, Computer Supported Collaborative Learning (CSCL) has been introduced in classrooms and implemented using a variety of technologies, including serious games, which have been adopted across several domains aiming to appeal to various demographics and skill levels. This research focuses on a Collaborative Multiplayer Serious Game (MSG) for CT skill training. The architecture is aimed at young students and is designed to aid in the learning of programming and the development of CT skills. The purpose of this research is to conduct an empirical study to assess the multiplayer game gameplay mechanics for collaborative CT learning. The proposed game leverages a card game structure and contains complex multi-team multi-player processes, allowing students to communicate and absorb sequential and conditional logics as well as graph routing in a 2D environment. A preliminary experiment was conducted with four fourth-graders and eight sixth-graders from a French school in Morocco who have varying levels of understanding of CT. Participants were split into three groups each with two teams and were required to complete a 16-question multiple-choice quiz before and after playing the same game to assess their initial structural programming logics and the effectiveness of the MSG. Questionnaires were collected along with an interview to gather feedback on their gaming experiences and the game’s role in teaching and learning. The results demonstrate that the proposed MSG had a favourable effect on the participants’ test scores as the scores of 4 of the teams increased and 1 remained the same. All students performed well on the sequential and conditional logics, which was significantly better than the achievement of the Bebras test of the graph routing. Furthermore, according to the participants, the game provides an appealing environment that allows players to immerse themselves in the game and the competitive aspect of the game adds to its appeal and helps develop teamwork, coordination, and communication skills

Effects of System Characteristics on Adopting Web-Based Advanced Traveller Information System: Evidence from Taiwan

This study proposes a behavioural intention model that integrates information quality, response time, and system accessibility into the original technology acceptance model (TAM) to investigate whether system characteristics affect the adoption of Web-based advanced traveller information systems (ATIS). This study empirically tests the proposed model using data collected from an online survey of Web-based advanced traveller information system users. Con­firmatory factor analysis (CFA) was performed to examine the reliability and validity of the measurement model, and structural equation modelling (SEM) was used to evaluate the structural model. The results indicate that three system characteristics had indirect effects on the intention to use through perceived usefulness, perceived ease of use, and attitude toward using. Information quality was the most im­portant system characteristic factor, followed by response time and system accessibility. This study presents implica­tions for practitioners and researchers, and suggests direc­tions for future research.</p

Project RISE: Recognizing Industrial Smoke Emissions

Industrial smoke emissions pose a significant concern to human health. Prior works have shown that using Computer Vision (CV) techniques to identify smoke as visual evidence can influence the attitude of regulators and empower citizens to pursue environmental justice. However, existing datasets are not of sufficient quality nor quantity to train the robust CV models needed to support air quality advocacy. We introduce RISE, the first large-scale video dataset for Recognizing Industrial Smoke Emissions. We adopted a citizen science approach to collaborate with local community members to annotate whether a video clip has smoke emissions. Our dataset contains 12,567 clips from 19 distinct views from cameras that monitored three industrial facilities. These daytime clips span 30 days over two years, including all four seasons. We ran experiments using deep neural networks to establish a strong performance baseline and reveal smoke recognition challenges. Our survey study discussed community feedback, and our data analysis displayed opportunities for integrating citizen scientists and crowd workers into the application of Artificial Intelligence for social good.Comment: Technical repor