DeepConv-DTI: Prediction of drug-target interactions via deep learning with convolution on protein sequences

Identification of drug-target interactions (DTIs) plays a key role in drug discovery. The high cost and labor-intensive nature of in vitro and in vivo experiments have highlighted the importance of in silico-based DTI prediction approaches. In several computational models, conventional protein descriptors are shown to be not informative enough to predict accurate DTIs. Thus, in this study, we employ a convolutional neural network (CNN) on raw protein sequences to capture local residue patterns participating in DTIs. With CNN on protein sequences, our model performs better than previous protein descriptor-based models. In addition, our model performs better than the previous deep learning model for massive prediction of DTIs. By examining the pooled convolution results, we found that our model can detect binding sites of proteins for DTIs. In conclusion, our prediction model for detecting local residue patterns of target proteins successfully enriches the protein features of a raw protein sequence, yielding better prediction results than previous approaches.Comment: 26 pages, 7 figure

What Drives the Adoption of Mobile Data Services? An Approach from a Value Perspective

Mobile data services (MDS) are wireless value-added pay-per-use services that have attracted increased attention in recent years. In this paper, a theoretical framework is proposed to investigate key drivers of user behavior in wireless pay-per-use services based on a value perspective. This study examines the role of three evaluation criteria -utilitarian, hedonic, and social values - in adoption decisions. Potential adopters have no direct experience with MDS; thus, they likely conceive value based primarily on indirect experience with it, such as through communication with peers or advertisements. In this study, the influence of members in the social networks and external sources are regarded as the major sources of information in order to capture the role of these factors on the perceptions of value. Additionally, according to the age and gender of potential adopters, changes in the degree to which the antecedents lead to MDS acceptance are examined. The proposed model is empirically tested using survey data collected from 287 potential adopters. The analysis results show that the proposed model, based on the aforementioned view of value, provides a significant explanation of the variance in the level of adoption intention toward MDS. The results of this study indicate that utilitarian and social values dominate adoption decisions, whereas the impact of hedonic value in MDS acceptance is weaker than other values. Information from relevant others and from mass media play a critical role in forming the perceptions of value obtained from the use of MDS. The results also shed light on the moderating effects of age and gender on MDS acceptance. Theoretical and practical implications of this work are discussed

Analysis of Trust in the E-Commerce Adoption

Understanding user acceptance of the Internet, especially the usage intention of virtual communities, is important in explaining the fact that virtual communities have been growing at an exponential rate in recent years. This paper studies the trust of virtual communities to better understand and manage the activities of E-commerce. A theoretical model proposed in this paper is to clarify the factors as they are related to the Technology Acceptance Model. In particular the relationship between trust and Intentions is hypothesized. Using the Technology Acceptance Model, this research showed that the importance of trust in virtual communities. According to the research, different ways of stimulating the members are necessary in order to facilitate participation in activities of virtual communities. The effect of trust in members on intention to use is stronger than that of trust in service providers. The intention to purchase is more sensitive to trust in service providers than trust in members

A framework for collaborative filtering recommender systems

As the use of recommender systems becomes more consolidated on the Net, an increasing need arises to develop some kind of evaluation framework for collaborative filtering measures and methods which is capable of not only testing the prediction and recommendation results, but also of other purposes which until now were considered secondary, such as novelty in the recommendations and the users? trust in these. This paper provides: (a) measures to evaluate the novelty of the users? recommendations and trust in their neighborhoods, (b) equations that formalize and unify the collaborative filtering process and its evaluation, (c) a framework based on the above-mentioned elements that enables the evaluation of the quality results of any collaborative filtering applied to the desired recommender systems, using four graphs: quality of the predictions, the recommendations, the novelty and the trust

Stabilization of Traumatic Thoracolumbar Subluxation using Patient-specific Drill Guide with Double Pin and Polymethylmethacrylate in a 12-year-old Maltese Dog

Spinal instability in a 12-year-old Maltese with T11-12 subluxation presenting with neurological symptoms, including hindlimb paraplegia, was treated using double-pin and polymethyl methacrylate (PMMA) fixation. Postoperatively, neurological deficits and hindlimb paraplegia improved, enabling the dog to regain independent ambulation without assistance. Patient-specific drill guides enhanced the accuracy and safety of screw placement, highlighting their potential as an effective method for managing thoracolumbar subluxations in veterinary medicine.