Identifying Competitive Attributes Based on an Ensemble of Explainable Artificial Intelligence

Competitor analysis is a fundamental requirement in both strategic and operational management, and the competitive attributes of reviewer comments are a crucial determinant of competitor analysis approaches. Most studies have focused on identifying competitors or detecting comparative sentences, not competitive attributes. Thus, the authors propose a method based on explainable artificial intelligence (XAI) that can detect competitive attributes from consumers’ perspectives. They construct a model to classify the reviewer comments for each competitive product and calculate the importance of each keyword in the reviewer comments during the classification process. This is based on the assumption that keywords significantly influence product classification. The authors also propose an additional novel methodology that combines various XAI techniques such as local interpretable model-agnostic explanations, Shapley additive explanations, logistic regression, gradient-based class activation map, and layer-wise relevance propagation to build a robust model for calculating the importance of competitive attributes for various data sources

Novel function of C5 protein as a metabolic stabilizer of M1 RNA

AbstractEscherichia coli RNase P is a ribonucleoprotein composed of a large RNA subunit (M1 RNA) and a small protein subunit (C5 protein). We examined if C5 protein plays a role in maintaining metabolic stability of M1 RNA. The sequestration of C5 protein available for M1 RNA binding reduced M1 RNA stability in vivo, and its reduced stability was recovered via overexpression of C5 protein. In addition, M1 RNA was rapidly degraded in a temperature-sensitive C5 protein mutant strain at non-permissive temperatures. Collectively, our results demonstrate that the C5 protein metabolically stabilizes M1 RNA in the cell

BioCAD: an information fusion platform for bio-network inference and analysis

Background : As systems biology has begun to draw growing attention, bio-network inference and analysis have become more and more important. Though there have been many efforts for bio-network inference, they are still far from practical applications due to too many false inferences and lack of comprehensible interpretation in the biological viewpoints. In order for applying to real problems, they should provide effective inference, reliable validation, rational elucidation, and sufficient extensibility to incorporate various relevant information sources. Results : We have been developing an information fusion software platform called BioCAD. It is utilizing both of local and global optimization for bio-network inference, text mining techniques for network validation and annotation, and Web services-based workflow techniques. In addition, it includes an effective technique to elucidate network edges by integrating various information sources. This paper presents the architecture of BioCAD and essential modules for bio-network inference and analysis. Conclusion : BioCAD provides a convenient infrastructure for network inference and network analysis. It automates series of users' processes by providing data preprocessing tools for various formats of data. It also helps inferring more accurate and reliable bio-networks by providing network inference tools which utilize information from distinct sources. And it can be used to analyze and validate the inferred bio-networks using information fusion tools.ope

Understanding Perceived Privacy: A Privacy Boundary Management Model

Consumer data is asset to organizations. Analysis of consumers’ transactional data helps organizations to understand customer behaviors and preferences. Before organizations could capitalize on these data, they ought to have effective plans to address consumers’ privacy concerns because violation of consumer privacy brings long-term reputational damage to organizations. This paper proposes and tests a Privacy Boundary Management Model that explains how consumers formulate and manage their privacy boundary. Survey data was collected from 98 users of online banking websites who have used the system for a minimum of six months. The PLS results showed that the model accounts for high variance in perceived privacy. Three elements of the FIPs (notice, access, and enforcement) have significant impact on perceived effectiveness of privacy policy. Perceived effectiveness in turns significantly influences privacy control and privacy risks. Privacy control affects perceived privacy and trust while privacy risk influences privacy concern and perceived privacy. Privacy concern has a negative relationship with perceived privacy and trust has a positive relationship with perceived privacy. The findings have novel implications for organizations and policy makers

Germ cell-specific gene 1 targets testis-specific poly(A) polymerase to the endoplasmic reticulum through protein–protein interactions

AbstractTestis-specific poly(A) polymerase (TPAP) is a cytoplasmic poly(A) polymerase that is highly expressed in round spermatids. We identified germ cell-specific gene 1 (GSG1) as a TPAP interaction partner protein using yeast two-hybrid and coimmunoprecipitation assays. Subcellular fractionation analysis showed that GSG1 is exclusively localized in the endoplasmic reticulum (ER) of mouse testis where TPAP is also present. In NIH3T3 cells cotransfected with TPAP and GSG1, both proteins colocalize in the ER. Moreover, expression of GSG1 stimulates TPAP targeting to the ER, suggesting that interactions between the two proteins lead to the redistribution of TPAP from the cytosol to the ER.Structured summaryMINT-6168263:Gsg1 (uniprotkb:Q8R1W2), TPAP (uniprotkb:Q9WVP6) and Calmegin (uniprotkb:P52194) colocalize (MI:0403) by cosedimentation (MI:0027)MINT-6168204, MINT-6168178:Gsg1 (uniprotkb:Q8R1W2) and TPAP (uniprotkb:Q9WVP6) colocalize (MI:0403) by fluorescence microscopy (MI:0416)MINT-6167930:Gsg1 (uniprotkb:Q8R1W2) physically interacts (MI:0218) with TPAP (uniprotkb:Q9WVP6) by two-hybrid (MI:0018)MINT-6168112, MINT-6168011, MINT-6168054:Gsg1 (uniprotkb:Q8R1W2) physically interacts (MI:0218) with TPAP (uniprotkb:Q9WVP6) by coimmunoprecipitation (MI:0019)MINT-61668069, MINT-6168101:Gsg1 (uniprotkb:Q8R1W2) physically interacts (MI:0218) with TPAP (uniprotkb:Q9WVP6) by pull-down (MI:0096)MINT-6168218:Gsg1 (uniprotkb:Q8R1W2) and GRP78 (uniprotkb:P20029) colocalize (MI:0403) by fluorescence microscopy (MI:0416)MINT-6168381:TPAP (uniprotkb:Q9WVP6) and GRP78 (uniprotkb:P20029) colocalize (MI:0403) by fluorescence microscopy (MI:0416

Selective deep convolutional neural network for low cost distorted image classification

Neural networks trained using images with a certain type of distortion should be better at classifying test images with the same type of distortion than generally-trained neural networks, given other factors being equal. Based on this observation, an ensemble of convolutional neural networks (CNNs) trained with different types and degrees of distortions is used. However, instead of simply classifying test images of unknown distortion types with the entire ensemble of CNNs, an extra tiny CNN is specifically trained to distinguish between the different types and degrees of distortions. Then, only the dedicated CNN for that specific type and degree of distortion, as determined by the tiny CNN, is activated and used to classify a possibly distorted test image. This proposed architecture, referred to as a \textit{selective deep convolutional neural network (DCNN)}, is implemented and found to result in high accuracy with low hardware costs. Detailed simulations with realistic image distortion scenarios using three popular datasets show that memory, MAC operations, and energy savings of up to 93.68%, 93.61%, and 91.92%, respectively, can be achieved with almost no reduction in image classification accuracy. The proposed selective DCNN scores up to 2.18x higher than the state-of-the-art DCNN model when evaluated using NetScore, a comprehensive metric that considers both CNN performance and hardware cost. In addition, it is shown that even higher hardware cost reduction can be achieved when selective DCNN is combined with previously proposed model compression techniques. Finally, experiments conducted with extended types and degrees of image distortion show that selective DCNN is highly scalable.11Ysciescopu