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

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

How do you feel? Measuring User-Perceived Value for Rejecting Machine Decisions in Hate Speech Detection

Hate speech moderation remains a challenging task for social media platforms. Human-AI collaborative systems offer the potential to combine the strengths of humans' reliability and the scalability of machine learning to tackle this issue effectively. While methods for task handover in human-AI collaboration exist that consider the costs of incorrect predictions, insufficient attention has been paid to accurately estimating these costs. In this work, we propose a value-sensitive rejection mechanism that automatically rejects machine decisions for human moderation based on users' value perceptions regarding machine decisions. We conduct a crowdsourced survey study with 160 participants to evaluate their perception of correct and incorrect machine decisions in the domain of hate speech detection, as well as occurrences where the system rejects making a prediction. Here, we introduce Magnitude Estimation, an unbounded scale, as the preferred method for measuring user (dis)agreement with machine decisions. Our results show that Magnitude Estimation can provide a reliable measurement of participants' perception of machine decisions. By integrating user-perceived value into human-AI collaboration, we further show that it can guide us in 1) determining when to accept or reject machine decisions to obtain the optimal total value a model can deliver and 2) selecting better classification models as compared to the more widely used target of model accuracy.Comment: To appear at AIES '23. Philippe Lammerts, Philip Lippmann, Yen-Chia Hsu, Fabio Casati, and Jie Yang. 2023. How do you feel? Measuring User-Perceived Value for Rejecting Machine Decisions in Hate Speech Detection. In AAAI/ACM Conference on AI, Ethics, and Society (AIES '23), August 8.10, 2023, Montreal, QC, Canada. ACM, New York, NY, USA. 11 page

Increased ATP generation in the host cell is required for efficient vaccinia virus production

To search for cellular genes up-regulated by vaccinia virus (VV) infection, differential display-reverse transcription-polymerase chain reaction (ddRT-PCR) assays were used to examine the expression of mRNAs from mock-infected and VV-infected HeLa cells. Two mitochondrial genes for proteins that are part of the electron transport chain that generates ATP, ND4 and CO II, were up-regulated after VV infection. Up-regulation of ND4 level by VV infection was confirmed by Western blotting analysis. Up-regulation of ND4 was reduced by the MAPK inhibitor, apigenin, which has been demonstrated elsewhere to inhibit VV replication. The induction of ND4 expression occurred after viral DNA replication since ara C, an inhibitor of poxviral DNA replication, could block this induction. ATP production was increased in the host cells after VV infection. Moreover, 4.5 μM oligomycin, an inhibitor of ATP production, reduced the ATP level 13 hr after virus infection to that of mock-infected cells and inhibited viral protein expression and virus production, suggesting that increased ATP production is required for efficient VV production. Our results further suggest that induction of ND4 expression is through a Bcl-2 independent pathway