Publication Date: 2018
Briefings in Bioinformatics (1467-5463)19(5)pp. 878-892
Experimental drug development is time-consuming, expensive and limited to a relatively small number of targets. However, recent studies show that repositioning of existing drugs can function more efficiently than de novo experimental drug development to minimize costs and risks. Previous studies have proven that network analysis is a versatile platform for this purpose, as the biological networks are used to model interactions between many different biological concepts. The present study is an attempt to review network-based methods in predicting drug targets for drug repositioning. For each method, the preferred type of data set is described, and their advantages and limitations are discussed. For each method, we seek to provide a brief description, as well as an evaluation based on its performance metrics.We conclude that integrating distinct and complementary data should be used because each type of data set reveals a unique aspect of information about an organism. We also suggest that applying a standard set of evaluation metrics and data sets would be essential in this fast-growing research domain.
Publication Date: 2023
Journal Of Optimization In Industrial Engineering (24233935)16(2)pp. 257-273
Agent-based modelling and simulation (ABMS) is one of the topics which has been extensively studied by researchers in the field of marketing and consumer behaviour. However, no such analysis has been conducted on using Agent-based modelling and simulation in marketing and consumer behaviour. An extensive bibliometric analysis, as well as a thorough visualization and science mapping, was carried out in this field from 1995 to 2022, in response to capturing recent ABMS development in this field. A total of 1210 documents from the WOS and Scopus databases were analyse d using bibliometrix R-Tool and VOS viewer. The results showed the 20 documents with the most citations were in the area of energy consumption (55%) and innovation diffusion behaviour (20%). The USA has the most publications in this field, with the production of 188 documents. The “EXPERT SYSTEMS WITH APPLICATIONS” is a productive journal publishing in this field. Generally, the major journals that publish research on the use of ABM in marketing and consumer behaviour are multidisciplinary or interdisciplinary. 6 clusters were identified based on the analysis of the most frequent key-words: Cluster 1 (multi-agent systems and consumer behaviour), Cluster 2 (agent-based simulation and SCM), Cluster 3 (ABM and energy consumption), Cluster 4 (AMB and innovation diffusion), Cluster 5 (complex system and Simulation) and Cluster 6 (ABM and TAM). Prediction is one of the goals that has attracted the most attention of ABMS researchers among many goals such as optimization, description, self-organization, and adaptability, and there are many recent works in this field. These results show that many topics that were of interest in the past, such as the ontology of ABMS, are no longer of much interest to researchers, and the attention of researchers has been directed toward issues such as the diffusion of innovation, energy consumption, and pricing in recent years. This topic can determine the appropriate approach for other researchers to research in this field. © 2023 Qazvin Islamic Azad University. All rights reserved.
Publication Date: 2021
Journal of Biomedical Informatics (15320480)115
One of the effective missions of biology and medical science is to find disease-related genes. Recent research uses gene/protein networks to find such genes. Due to false positive interactions in these networks, the results often are not accurate and reliable. Integrating multiple gene/protein networks could overcome this drawback, causing a network with fewer false positive interactions. The integration method plays a crucial role in the quality of the constructed network. In this paper, we integrate several sources to build a reliable heterogeneous network, i.e., a network that includes nodes of different types. Due to the different gene/protein sources, four gene-gene similarity networks are constructed first and integrated by applying the type-II fuzzy voter scheme. The resulting gene-gene network is linked to a disease-disease similarity network (as the outcome of integrating four sources) through a two-part disease-gene network. We propose a novel algorithm, namely random walk with restart on the heterogeneous network method with fuzzy fusion (RWRHN-FF). Through running RWRHN-FF over the heterogeneous network, disease-related genes are determined. Experimental results using the leave-one-out cross-validation indicate that RWRHN-FF outperforms existing methods. The proposed algorithm can be applied to find new genes for prostate, breast, gastric, and colon cancers. Since the RWRHN-FF algorithm converges slowly on large heterogeneous networks, we propose a parallel implementation of the RWRHN-FF algorithm on the Apache Spark platform for high-throughput and reliable network inference. Experiments run on heterogeneous networks of different sizes indicate faster convergence compared to other non-distributed modes of implementation. © 2021 Elsevier Inc.
Publication Date: 2022
Knowledge and Information Systems (02191377)64(12)pp. 3293-3324
Patient similarity assessment, which identifies patients similar to a given patient, is a fundamental component of many secondary uses of medical data. The assessment can be performed using electronic medical records (EMRs). Patient similarity measurement requires converting heterogeneous EMRs into comparable formats to calculate distance. This study presents a new data representation method for EMRs that considers the information in clinical narratives. To address the limitations of previous approaches in handling complex parts of EMR data, an unsupervised manner is proposed for building a patient representation, which integrates unstructured and structured data extracted from patients' EMRs. We employed a tree structure to model the extracted data that capture the temporal relations of multiple medical events from EMR. We processed clinical notes to extract medical concepts using Python libraries such as MedspaCy and ScispaCy and mapped entities to the Unified Medical Language System (UMLS). To capture temporal aspects of the extracted events, we developed two new relabeling methods for the non-leaf nodes of the tree. To create an embedding vector for each patient, we traversed the tree to generate sequences that the Doc2vec algorithm would use. The comprehensive evaluation of the proposed method for patient similarity and mortality prediction tasks demonstrated that our proposed model leads to lower mean-squared error (MSE), higher precision, and normalized discounted cumulative gain (NDCG) relative to baselines. © 2022, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature.
Publication Date: 2014
Simulation Series (07359276)46(1)pp. 77-83
In this paper we propose an agent-based model approach to determining the effects of consumer choice on aggregate demand (CCAD). Our overall goal is to better understand how the availability of information, heuristic decision making, and social norms affect: 1) total aggregate demand and 2) the aggregated demand for disposable vs. more durable goods. In the preliminary model presented here, consumer agents select among baskets of goods with different combinations of quality and disposability. Consumer choices are based on individual agent preferences and subject to a discretionary income constraint. Agents may be either maximizing, which means that they choose the best basket of goods that they can afford, or satisficing, which means that they choose the first affordable basket of goods that they can find with utility greater than their satisfaction threshold. When run at different price levels, the resulting models can be used to generate aggregated demand curves for each group of consumers. We also demonstrate that, satisficers buy more than maximizers overall. Further analysis shows that this is because maximizers focus their trading on more durable products to gain the highest utility, however satisficers purchase more disposable products because they shop for convenience rather than utility maximization.
Roozmand, O.,
Ghasem-aghaee, N.,
Hofstede, G.J.,
Nematbakhsh, M.A.,
Baraani, A.,
Verwaart, T. Publication Date: 2011
Knowledge-Based Systems (0950-7051)24(7)pp. 1075-1095
Simulating consumer decision making processes involves different disciplines such as: sociology, social psychology, marketing, and computer science. In this paper, we propose an agent-based conceptual and computational model of consumer decision-making based on culture, personality and human needs. It serves as a model for individual behavior in models that investigate system-level resulting behavior. Theoretical concepts operationalized in the model are the Power Distance dimension of Hofstede's model of national culture; Extroversion, Agreeableness and Openness of Costa and McCrae's five-factor model of personality, and social status and social responsibility needs. These factors are used to formulate the utility function, process and update the agent state, need recognition and action estimation modules of the consumer decision process. The model was validated against data on culture, personality, wealth and car purchasing from eleven European countries. It produces believable results for the differences of consumer purchasing across eleven European countries. © 2011 Elsevier B.V. All rights reserved.
Osinga, S.A.,
Kramer, M.R.,
Hofstede, G.J.,
Roozmand, O.,
Beulens, A.J. Publication Date: 2010
Lecture Notes in Economics and Mathematical Systems (00758442)645pp. 177-188
This paper investigates the effect of a selected top-down measure (whatif scenario) on actual agent behaviour and total system behaviour by means of an agent-based simulation model, when agents' behaviour cannot fully be managed because the agents are autonomous. The Chinese pork sector serves as case. A multilevel perspective is adopted: the top-down information management measures for improving pork quality, the variation in individual farmer behaviour, and the interaction structures with supply chain partners, governmental representatives and peer farmers. To improve quality, farmers need information, which they can obtain from peers, suppliers and government. Satisfaction or dissatisfaction with their personal situation initiates change of behaviour. Aspects of personality and culture affect the agents' evaluations, decisions and actions. Results indicate that both incentive (demand) and the possibility to move (quality level within reach) on farmer level are requirements for an increase of total system quality. A more informative governmental representative enhances this effect. © Springer-Verlag Berlin Heidelberg 2010.
Publication Date: 2007
Journal of Theoretical and Applied Electronic Commerce Research (07181876)2(1)pp. 1-17
In this paper, we propose a market model which Is based on reputation and reinforcement learning algorithms for buying and selling agents. Three important factors: quality, price and delivery-time are considered in the model. We take into account the fact that buying agents can have different priorities on quality, price and delivery-time of their goods and selling agents adjust their bids according to buying agents preferences. Also we have assumed that multiple selling agents may offer the same goods with different qualities, prices and delivery-times. In our model, selling agents learn to maximize their expected profits by using reinforcement learning to adjust product quality, price and delivery-time. Also each selling agent models the reputation of buying agents based on their profits for that seller and uses this reputation to consider discount for reputable buying agents. Buying agents learn to model the reputation of selling agents based on different features of goods: reputation on quality, reputation on price and reputation on delivery-time to avoid interaction with disreputable selling agents. The model has been implemented with Aglet and tested in a large-sized marketplace. The results show that selling/buying agents that model the reputation of buying/selling agents obtain more satisfaction rather than selling/buying agents who only use the reinforcement learning. © 2007 Universidad de Talca.
Publication Date: 2011
Scientia Iranica (23453605)18(6)pp. 1460-1468
Self-Organizing Maps (SOMs) are among the most well-known, unsupervised neural network approaches to clustering, which are very efficient in handling large and high dimensional datasets. The original Particle Swarm Optimization (PSO) is another algorithm discovered through simplified social model simulation, which is effective in nonlinear optimization problems and easy to implement. In the present study, we combine these two methods and introduce a new method for anomaly detection. A discussion about our method is presented, its results are compared with some other methods and its advantages over them are demonstrated. In order to apply our method, we also performed a case study on forest fire detection. Our algorithm was shown to be simple and to function better than previous ones. We can apply it to different domains of anomaly detection. In fact, we observed our method to be a generic algorithm for anomaly detection that may need few changes for implementation in different domains. © 2012 Sharif University of Technology. Production and hosting by Elsevier B.V. All rights reserved.
Publication Date: 2023
Pattern Analysis and Applications (1433755X)26(4)pp. 1715-1727
Clinical notes contain valuable patient information. These notes are written by health care providers with various scientific levels and writing styles. It might be helpful for clinicians and researchers to understand what information is essential when dealing with extensive electronic medical records. Entities recognizing them and mapping them to standard terminologies is crucial to reducing ambiguity in processing clinical notes. Although named entity recognition and entity linking are critical steps in clinical natural language processing, they can produce repetitive and low-value concepts. On the other hand, all parts of a clinical text do not share the same importance or content in predicting the patient's condition. As a result, it is necessary to identify the section in which each content item is recorded and critical concepts to extract meaning from clinical texts. In this study, these challenges have been addressed by using clinical natural language processing techniques. In addition, a set of unsupervised essential phrase extraction methods has been verified and evaluated to identify key concepts. Considering that most clinical concepts are in the form of multi-word expressions and their accurate identification requires the user to specify an n-gram range, we have proposed a shortcut method to preserve the structure of the term based on TF-IDF (Term Frequency Inverse Document Frequency). To evaluate, we have designed two types of downstream tasks (multiple and binary classification) using the capabilities of transformer-based models. The results show the proposed method's superiority in combination with the SciBERT model. Also, they offer an insight into the efficacy of general methods for extracting essential phrases from clinical notes. © 2023, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature.
Publication Date: 2019
Expert Systems with Applications (0957-4174)119pp. 476-490
Influence maximization is an important issue in social network analysis domain which concerns finding the most influential nodes. Determining the influential nodes is made with respect to information diffusion models. Most of the existing models only contain trust relationships while distrust exist in social networks as well. There exist some drawbacks in limited studies where distrust relationship is involved. The most outstanding drawback is the lack of assessment on the validity of the schemes presented on how influence propagates through distrust relationships in comparison with real word propagation in social networks. In this paper, two schemes are proposed, where based on each, some new models are proposed in two classes: cascade-based and threshold-based. All models of concern here are evaluated in comparison with the benchmark models through two real data sets, the Epinions and Bitcoin OTC. Results obtained indicate the superiority of one of the proposed schemes: when a distrusted user performs an action or adopts an opinion, the target users may tend not to do it. © 2018 Elsevier Ltd
Deffuant, G.,
Roozmand, O.,
Huet, S.,
Khamzina, K.,
Nugier, A.,
Guimond, S. Publication Date: 2023
IEEE Transactions on Computational Social Systems (2329924X)10(3)pp. 922-933
In two studies about farming practices, the respondents who are particularly favorable to organic farming tend to have a higher intention to convert their farm to organic when they perceive other farmers as not very favorable to this practice. This intention can be considered as anticonformist, as it is in opposition to the general view of others. This article hypothesizes that this phenomenon can be explained by some biases on the perceptions of attitudes. It proposes an agent-based model which computes an intention based on the theory of reasoned action (TRA) and assumes some biases in the perception of others' attitudes according to the social judgment theory. It investigates the conditions on the model parameter values for which the simulations reproduce the features observed in the studies. The results show that perceptual biases are a possible explanation of anticonformist intentions. © 2014 IEEE.
Publication Date: 2025
Journal of Supercomputing (15730484)81(5)
The importance of a node, known as centrality, can be defined and measured in various ways. The main challenge of these measurements is their extension to multilayer networks. In multilayer networks, the influence of inter-layer edges compared to intra-layer edges must be considered when calculating centrality measures. Here, the primary purpose is to provide a multilayer network-specific framework to measure the importance of nodes, with special consideration for inter-layer edges and intra-layer ones. First, we considered the different centrality measures offered for multilayer networks, as well as the associated tools and packages. Next, we implemented some more informative measures specific to multilayer networks. The functionality of implemented metrics is provided for some real networks using Python. We assessed these metrics as ranking criteria and then contrasted the ranking results using three methods: intersection similarity, rank differences, and Kendall’s tau. The findings demonstrated that incorporating information from various layers enhances the effectiveness of the criteria. The final product is a publicly available Python package called MultiNetPy, available at https://github.com/Multinetpy/Multinetpy. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.
Publication Date: 2025
Data in Brief (23523409)61
Circular RNAs (circRNAs) are a type of RNAs that play crucial roles in various biological processes. Their outstanding properties such as tissue-specific expression and high resistance to exonuclease degradation make them attractive for research. However, a comprehensive analysis tool for analyzing circRNA data is still required. Here, we present CircPac, a newly developed web-based toolset that searches databases like circBase, circBank, and circRNADisease and organizes data to provide and visualize circRNAs information. Our toolset was created using the Python programming language and its libraries, such as pandas, seaborn, and the Django framework. CircPac enables users to unify the circRNA IDs and subsequently perform various bioinformatic analyses. These analyses include retrieving basic circRNA information, identifying target miRNAs, and analyzing circRNA expression changes in various diseases. Additionally, this toolset generates ready-to-publish figures of circRNA-miRNA interactions and circRNAs expression changes in diseases. CircPac is freely accessible (at https://www.circpac.ir) and offers a user-friendly platform for biologists to efficiently conduct and visualize circRNA data analyses in an appropriate format. © 2025 The Authors
Publication Date: 2020
Expert Systems with Applications (0957-4174)159
Background and objective: Heterogeneous complex networks are large graphs consisting of different types of nodes and edges. The knowledge extraction from these networks is complicated. Moreover, the scale of these networks is steadily increasing. Thus, scalable methods are required. Methods: In this paper, two distributed label propagation algorithms for heterogeneous networks, namely DHLP-1 and DHLP-2 have been introduced. Biological networks are one type of the heterogeneous complex networks. As a case study, we have measured the efficiency of our proposed DHLP-1 and DHLP-2 algorithms on a biological network consisting of drugs, diseases, and targets. The subject we have studied in this network is drug repositioning but our algorithms can be used as general methods for heterogeneous networks other than the biological network. Results: We compared the proposed algorithms with similar non-distributed versions of them namely MINProp and Heter-LP. The experiments revealed the good performance of the algorithms in terms of running time and accuracy. © 2020 Elsevier Ltd
Publication Date: 2014
Proceedings of the Institution of Civil Engineers: Water Management (17517729)167(4)pp. 187-193
Experimental and numerical studies were performed to examine the relationship between the hydraulic properties and physical dimensions of a subsurface weir system and the discharge over it under steady-state conditions. Experimental modelling was based on a laboratory-scale physical sand-tank model, within which a subsurface dam and weir structure was constructed. To evaluate the effect of the dominant parameters on the discharge coefficient, numerical modelling was also carried out. A number of physical and numerical models of subsurface dams and weirs were constructed and the effect of each parameter on the discharge coefficient was studied. The results indicate that the hydraulic conductivity of the aquifer, the head above the weir and the effective width of the aquifer, in addition to the geometry of the subsurface weir, significantly affect discharge efficiency. It was also shown that the Reynolds and Weber numbers only have a small effect on the discharge coefficient. Based on the experimental results, a threshold value is suggested for the subsurface weir submergence. Finally, according to the results of both experimental and numerical models, an equation is derived to estimate the discharge coefficient of subsurface weirs.
Mohammadi, F.,
Bina, B.,
Amin, M.M.,
Pourzamani, H.R.,
Yavari, Z.,
Shams, M. Publication Date: 2018
Canadian Journal of Chemical Engineering (00084034)96(8)pp. 1762-1769
Alkylphenols (APs) received great attention in the past decade because they were on the priority hazardous substances list. The kinetics of a lab-scale moving bed biofilm reactor (MBBR) that was fed with synthetic wastewater containing 4-NonylPhenol (4-NP) and 4-tert-OctylPhenol (4-t-OP) was investigated in this paper. The MBBR reactor was evaluated under different APs, organic loading rates, and hydraulic retention times (HRT). The substrate removal rate was predicted with the first-order, second-order, Stover-Kincannon, and Monod substrate removal models. 4-NP and 4-t-OP pollutants were removed in the different percentages of 87.1 to 99.9 % and 83.2 to 99.9 %, respectively. Biokinetic parameters, like Y, KS, k, μmax, and kd, that would be favourable to design an MBBR were evaluated. Based on the results, the second-order (Grau), Stover-Kincannon, and Monod models were observed to be the most suitable for this reactor. These models showed high correlation coefficients of about 99.6, 99.1, and 92.9 %, respectively. Consequently, these models could be utilized in anticipating the performance and design of MBBR reactors. © 2017 Canadian Society for Chemical Engineering
Publication Date: 2026
Scientific Reports (20452322)16(1)
This study uses advanced time-series forecasting and causal modelling techniques to examine long-term patterns in Australian road traffic fatalities. Four statistical approaches were assessed: Holt-Winters, Theta, TBATS, and Vector Autoregression, with each offering strengths across different forecasting horizons. TBATS provided the most reliable short-term predictions, while Vector Autoregression performed best for medium- and long-term projections. A causal analysis using a random-effects panel model identified several key contributors to fatal crash risk, including older age groups, remote and outer-regional settings, nighttime periods, and high-speed environments. In contrast, younger adults and single-vehicle crashes were associated with lower fatality likelihood. Overall, the results demonstrate the value of flexible time-series techniques and panel data methods for guiding evidence-based road safety policy, targeted interventions, and infrastructure planning. © The Author(s) 2025.
Publication Date: 2017
Journal of Biomedical Informatics (15320480)68pp. 167-183
Drug repositioning offers an effective solution to drug discovery, saving both time and resources by finding new indications for existing drugs. Typically, a drug takes effect via its protein targets in the cell. As a result, it is necessary for drug development studies to conduct an investigation into the interrelationships of drugs, protein targets, and diseases. Although previous studies have made a strong case for the effectiveness of integrative network-based methods for predicting these interrelationships, little progress has been achieved in this regard within drug repositioning research. Moreover, the interactions of new drugs and targets (lacking any known targets and drugs, respectively) cannot be accurately predicted by most established methods. In this paper, we propose a novel semi-supervised heterogeneous label propagation algorithm named Heter-LP, which applies both local and global network features for data integration. To predict drug-target, disease-target, and drug-disease associations, we use information about drugs, diseases, and targets as collected from multiple sources at different levels. Our algorithm integrates these various types of data into a heterogeneous network and implements a label propagation algorithm to find new interactions. Statistical analyses of 10-fold cross-validation results and experimental analyses support the effectiveness of the proposed algorithm. © 2017
Publication Date: 2019
Methods in Molecular Biology (19406029)1903pp. 291-316
Using existing drugs for diseases which are not developed for their treating (drug repositioning) provides a new approach to developing drugs at a lower cost, faster, and more secured. We proposed a method for drug repositioning which can predict simple and complex relationships between drugs, drug targets, and diseases. Since biological networks typically present a suitable model for relationships between different biological concepts, our primary approach is to analyze graphs and complex networks in the study of drugs and their therapeutic effects. Given the nature of existing data, the use of semi-supervised learning methods is crucial. So, in our research, we have developed a label propagation method to predict drug-target, drug-disease, and disease-target interactions (Heter-LP), which integrates various data sources at different levels. The predicted interactions are the most prominent relationships among the millions of relationships suggested to the related researchers for further investigation. The main advantages of Heter-LP are the effective integration of input data, eliminating the need for negative samples, and the use of local and global features together. The main steps of this research are as follows. The first step is the construction of a heterogeneous network as a data modeling task, in which data are collected and prepared. The second step is predicting potential interactions. We present a new label propagation algorithm for heterogeneous networks, which consists of two parts, one mapping and the other an iterative method for determining the final labels of the entire network vertices. Finally, for evaluation, we calculated the AUC and AUPR with tenfold cross-validation and compared the results with the best available methods for label propagation in heterogeneous networks and drug repositioning. Also, a series of experimental evaluations and some specific case studies have been presented. The result of the AUC and AUPR for Heter-LP was much higher than the average of the best available methods. © 2019, Springer Science+Business Media, LLC, part of Springer Nature.
Publication Date: 2025
Case Studies on Transport Policy (22136258)20
In Australia, road crash injuries continue to be a serious public health issue. Machine learning is used in this study to analyse injury data from road crashes between 2011 and 2021 that was taken from the national hospitalized injury database. We investigate how the number of injuries and duration of stay for road users are affected by variables such as gender, age, seasonal variation, collision type, and location (urban vs. regional). Road safety measures are informed by patterns and relationships found in the data by machine learning models. Hospitalizations have been trending upward between 2011 and 2019, with a pause in 2020 due to COVID-19 lockdowns. In all categories, men sustain more injuries than women, though the number varies according to age and geography. The type of road user also affects collision patterns. The time-series projections demonstrate that the goal of zero fatalities in 2050 will not be achieved under the business-as-usual scenario. The findings highlight the necessity of focused interventions predicated on collision trends and demographics. This includes better infrastructure design, increased surveillance, and customized safety measures. © 2025 The Author(s)
Publication Date: 2023
International Journal of Organizational Analysis (19348835)31(5)pp. 1364-1383
Purpose: This study aims to adopt a follower-centric approach in leadership and ethics research by investigating the impact of implicit followership theories (IFTs) on followers’ constructive resistance to leaders’ unethical requests. Specifically, it analyzes the mediating role of organizational citizenship behavior in the relationship between IFTs and constructive resistance. Indeed, this study aims to examine whether followers with more positive beliefs about the characteristics that a follower should have IFTs are more likely to resist unethical leadership and whether this relationship is mediated by organizational citizenship behavior as volunteering acts that exceed the formal job requirements. Design/methodology/approach: The proposed hypotheses were tested using survey data from 273 employees working in a steel manufacturer company in Iran. The variance-based structural equation modeling technique was used to analyze data. Findings: The results show that followership antiprototype negatively affects both follower’s constructive resistance and organizational citizenship behavior. Furthermore, organizational citizenship behavior mediates the relationship between IFTs and follower’s constructive resistance. Also, both followership prototype and organizational citizenship behavior have a positive effect on follower’s constructive resistance. Originality/value: Contrary to the dominant leader-centric approach in leadership and organizational ethics research, few studies have examined the role of followers and their characteristics. The results of this study provide important insights into the role of followers in resistance against the leader’s unethical request. © 2021, Emerald Publishing Limited.
Personalized QoE has significant implications for businesses in terms of customer satisfaction, loyalty, and revenue generation. By delivering experiences tailored to individual users, businesses can build stronger relationships, improve customer retention, and gain a competitive edge in the marketplace. In this paper, we have attempted to use a clustering-based approach to enhance personalized QoE assessment via personalized federated learning technique. To achieve this, first, we classify users to different clusters, based on some user-related QoE influencing factors. Second, we employ independent personalized federated learning QoE predictors in clusters to assess the QoE level of the service. We conducted some experiments to compare the performance of our method to the traditional personalized federated learning based QoE assessment approach. The results demonstrate that the proposed approach increases the accuracy of QoE evaluations by about 16% in average. © 2023 IEEE.
Publication Date: 2023
Journal Of Cellular And Molecular Medicine (15821838)27(5)pp. 714-726
DNA methylation is an early event in tumorigenesis. Here, by integrative analysis of DNA methylation and gene expression and utilizing machine learning approaches, we introduced potential diagnostic and prognostic methylation signatures for stomach cancer. Differentially-methylated positions (DMPs) and differentially-expressed genes (DEGs) were identified using The Cancer Genome Atlas (TCGA) stomach adenocarcinoma (STAD) data. A total of 256 DMPs consisting of 140 and 116 hyper- and hypomethylated positions were identified between 443 tumour and 27 nontumour STAD samples. Gene expression analysis revealed a total of 2821 DEGs with 1247 upregulated and 1574 downregulated genes. By analysing the impact of cis and trans regulation of methylation on gene expression, a dominant negative correlation between methylation and expression was observed, while for trans regulation, in hypermethylated and hypomethylated genes, there was mainly a negative and positive correlation with gene expression, respectively. To find diagnostic biomarkers, we used 28 hypermethylated probes locating in the promoter of 27 downregulated genes. By implementing a feature selection approach, eight probes were selected and then used to build a support vector machine diagnostic model, which had an area under the curve of 0.99 and 0.97 in the training and validation (GSE30601 with 203 tumour and 94 nontumour samples) cohorts, respectively. Using 412 TCGA-STAD samples with both methylation and clinical data, we also identified four prognostic probes by implementing univariate and multivariate Cox regression analysis. In summary, our study introduced potential diagnostic and prognostic biomarkers for STAD, which demands further validation. © 2023 The Authors. Journal of Cellular and Molecular Medicine published by Foundation for Cellular and Molecular Medicine and John Wiley & Sons Ltd.
Publication Date: 2025
Biochemistry and Biophysics Reports (24055808)41
Introduction: Gastric cancer (GC) is among the deadliest malignancies globally, characterized by hypoxia-driven pathways that promote cancer progression, including stemness mechanisms facilitating invasion and metastasis. This study aimed to develop a prognostic decision tree using genes implicated in hypoxia and stemness pathways to predict outcomes in GC patients. Materials and methods: GC RNA-seq data from The Cancer Genome Atlas (TCGA) were analyzed to compute hypoxia and stemness scores using Gene Set Variation Analysis (GSVA) and the mRNA expression-based stemness index (mRNAsi). Hierarchical clustering identified clusters with distinct survival outcomes, and differentially expressed genes (DEGs) between clusters were identified. Weighted Gene Co-expression Network Analysis (WGCNA) identified modules and hub genes associated with clinical traits. Overlapping DEGs and hub genes underwent functional enrichment, protein-protein interaction (PPI) network analysis, and survival analysis. A prognostic decision tree was constructed using survival-associated shared genes. Results: Hierarchical clustering identified six clusters among 375 TCGA GC patients, with significant survival differences between cluster 1 (low hypoxia, high stemness) and cluster 4 (high hypoxia, high stemness). Validation in the GSE62254 dataset corroborated these findings. WGCNA revealed modules linked to clinical traits and survival, with functional enrichment highlighting pathways like cell adhesion and calcium signaling. The decision tree, based on genes such as AKAP6, GLRB, and RUNX1T1, achieved an AUC of 0.81 (training) and 0.67 (test), demonstrating the utility of combined scores in patient stratification. Conclusion: This study introduces a novel hypoxia-stemness-based prognostic decision tree for GC. The identified genes show promise as prognostic biomarkers, warranting further clinical validation. © 2024 The Authors
Publication Date: 2023
International Journal of Management Science and Engineering Management (17509653)18(2)pp. 77-87
One of the most important tools is an appropriate pricing mechanism to attract more customers and increase profits. The retailers’ main question is how to set the prices and inventory policies to maximize profit in a competitive heterogeneous market in presence of non-zero lead time and lost sales. A reinforcement learning algorithm is proposed to create appropriate decision-making mechanisms for pricing. A coordinated inventory policy in a competitive environment reduces logistic costs and leads to a higher profit. We use a reinforcement learning algorithm to investigate the performance of a retailer in a competitive environment. An agent-based modeling experimental environment combined with a simulation-optimization method in which a virtual market has been reproduced is used. The market is not homogeneous with respect to customer behavior. It is assumed that the retailer uses (R, Q) policy where the lead time is a fixed amount (L), and the shortage is permissible. The quality, distance, service level, and price are factors that influence customers’ choices. The simulation results for some randomly generated examples show that the algorithm in the competitive environment can make more profit than other available methods and the combined utilization of simulation-optimization methods has been able to find better solutions for the hybrid model of pricing and inventory management considering customer behavior. The results of simulation for three different categories of customers (more sensitive to price, equally sensitive to price, quality and service level, and more sensitive to quality (indicate that the average profit for the proposed algorithm is higher than that of other examined algorithms. © 2023 International Society of Management Science and Engineering Management.
