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

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

Information Overload and its Consequences in the Context of Online Consumer Reviews

An online consumer review is the information including experiences, evaluations and opinions on products from the consumer perspective. An online consumer review plays two roles - informant and recommender. Considering two factors of review structure (the number of reviews and review type), this study analyzes the effect of online consumer reviews on consumers’ information processing depending on their levels of involvement. Generally, more positive reviews seem better from the perspective of online consumer reviews as recommenders. However, from the perspective of online consumer reviews as information providers, consumers may be confronted with too much information when a large number of reviews are offered, which results in information overload. We investigate when information overload occurs in the context of online consumer reviews, what strategies against the information overload consumers use depending on their levels of involvement, and how the product attitude and purchasing intention are changed. Our findings have implications for online sellers in terms of how to manage online consumer reviews contents

The Effect of Site Trust on Trust in the Sources of Online Consumer Review and Trust in the Sources of Consumer Endorsement in Advertisement

Consumer endorsements have along been used as an advertising strategy, and now, it is also easy to see consumer endorsements in online shopping sites. A positive Online Consumer Review (OCR) is a consumer endorsement in the web site. Although the sources of both OCR and consumer endorsement in advertisement (CEA) are typical consumers, trust in the source of OCR could be perceived differently from trust in the source of CEA. Trust in the information source ensures that consumers comfortably accept the endorsement. In e-commerce, how is a consumer’s judgment involving trust based on endorsements made by other consumers? This experimental study investigates whether trust in a web site is transferred to trust in the source of OCR and CEA. Moreover, it also tests which source credibility could be more influenced by site trust

Evaluation of large language models for discovery of gene set function

Gene set analysis is a mainstay of functional genomics, but it relies on manually curated databases of gene functions that are incomplete and unaware of biological context. Here we evaluate the ability of OpenAI's GPT-4, a Large Language Model (LLM), to develop hypotheses about common gene functions from its embedded biomedical knowledge. We created a GPT-4 pipeline to label gene sets with names that summarize their consensus functions, substantiated by analysis text and citations. Benchmarking against named gene sets in the Gene Ontology, GPT-4 generated very similar names in 50% of cases, while in most remaining cases it recovered the name of a more general concept. In gene sets discovered in 'omics data, GPT-4 names were more informative than gene set enrichment, with supporting statements and citations that largely verified in human review. The ability to rapidly synthesize common gene functions positions LLMs as valuable functional genomics assistants

Incorporating reliability measurements into the predictions of a recommender system

In this paper we introduce the idea of using a reliability measure associated to the predic- tions made by recommender systems based on collaborative filtering. This reliability mea- sure is based on the usual notion that the more reliable a prediction, the less liable to be wrong. Here we will define a general reliability measure suitable for any arbitrary recom- mender system. We will also show a method for obtaining specific reliability measures specially fitting the needs of different specific recommender systems

The Causal Relationships among EDI Controls: A Structural Equation Model

Abstract Advances in EDI (Electronic Data Interchange) demand appropriate controls in order to realize the potential benefits from it. Formal, informal, and automated controls are basic parts of EDI controls. The state of one of three controls is suggested to affect performance indirectly through their effect on another controls in the research model. The causal relationships are tested using structural equation modeling approach with LISREL. Informal controls turn out to play an important role in the causal relationships, as they significantly affect formal and automated controls to have indirect effect on performance. The results of the study indicate that the interrelationships among controls are closely related to system performance

Identification of drug-target interaction by a random walk with restart method on an interactome network

Abstract Background Identification of drug-target interactions acts as a key role in drug discovery. However, identifying drug-target interactions via in-vitro, in-vivo experiments are very laborious, time-consuming. Thus, predicting drug-target interactions by using computational approaches is a good alternative. In recent studies, many feature-based and similarity-based machine learning approaches have shown promising results in drug-target interaction predictions. A previous study showed that accounting connectivity information of drug-drug and protein-protein interactions increase performances of prediction by the concept of ‘guilt-by-association’. However, the approach that only considers directly connected nodes often misses the information that could be derived from distance nodes. Therefore, in this study, we yield global network topology information by using a random walk with restart algorithm and apply the global topology information to the prediction model. Results As a result, our prediction model demonstrates increased prediction performance compare to the ‘guilt-by-association’ approach (AUC 0.89 and 0.67 in the training and independent test, respectively). In addition, we show how weighted features by a random walk with restart yields better performances than original features. Also, we confirmed that drugs and proteins that have high-degree of connectivity on the interactome network yield better performance in our model. Conclusions The prediction models with weighted features by considering global network topology increased the prediction performances both in the training and testing compared to non-weighted models and previous a ‘guilt-by-association method’. In conclusion, global network topology information on protein-protein interaction and drug-drug interaction effects to the prediction performance of drug-target interactions

Additional file 3: of Identification of drug-target interaction by a random walk with restart method on an interactome network

Table S1. Protein-Protein interactions of Q9H4B4 in Uniprot. (DOCX 14 kb