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  • دکتری (1388)

    مهندسی صنایع

    دانشگاه تربیت مدرس،

  • کارشناسی‌ارشد (1377)

    مهندسی صنایع، مهندسی سیستمهای اقتصادی اجتماعی

    موسسه عالی آموزش و پژوهش مدیریت و برنامه ریزی، ایران

  • کارشناسی (1373)

    مهندسی صنایع

    دانشگاه صنعتی شریف،

  • تحلیل شبکه های اجتماعی و پیچیده، بهینه سازی شبکه
  • علم داده، کلان داده و بهینه سازی
  • متن کاوی
  • بایو انفورماتیک و شبکه های ژنی

    داده ای یافت نشد

    ارتباط

    رزومه

    GrAR: A novel framework for Graph Alignment based on Relativity concept

    MA Soltanshahi, B Teimourpour, T Khatibi, H Zare
    Journal Papers , , {Pages }

    Abstract

    Dynamics of customer segments: A predictor of customer lifetime value

    Abdolreza Mosaddegh, Amir Albadvi, Mohammad Mehdi Sepehri, Babak Teimourpour
    Journal PapersExpert Systems with Applications , Volume 172 , 2021 June 15, {Pages 114606 }

    Abstract

    Most studies in the literature have focused on past behavior of customers to measure customer lifetime value, however, the rapid developments of technology and products make new conditions that cannot be predicted by past records anymore. In the era of new media and social networks, customers’ needs and expectations change fast which lead to instability of customer lifetime value.In the present study, we studied the dynamics of bank customers through value segments using big data analytics. By mining patterns of associations between customer transitions, we found six major categories, including the pattern of Local Leaders whose transitions are repeated by some follower groups within next two periods. Such results suggest that the dynamic

    Cancer driver gene detection in transcriptional regulatory networks using the structure analysis of weighted regulatory interactions

    Mostafa Akhavan Safar, Babak Teimourpour, Abbas Nozari-Dalini
    Journal PapersarXiv preprint arXiv:2101.07619 , 2021 January 19, {Pages }

    Abstract

    Identification of genes that initiate cell anomalies and cause cancer in humans is among the important fields in the oncology researches. The mutation and development of anomalies in these genes are then transferred to other genes in the cell and therefore disrupt the normal functionality of the cell. These genes are known as cancer driver genes (CDGs). Various methods have been proposed for predicting CDGs, most of which based on genomic data and based on computational methods. Therefore, some researchers have developed novel bioinformatics approaches. In this study, we propose an algorithm, which is able to calculate the effectiveness and strength of each gene and rank them by using the gene regulatory networks and the stochastic analysis

    Persian Opinion Mining: A Networked Analysis Approach

    M Heydari, B Teimourpour
    Journal Papers , , {Pages }

    Abstract

    KatzDriver: A network based method to cancer causal genes discovery in gene regulatory network

    M Akhavan-Safar, B Teimourpour
    Journal Papers , , {Pages }

    Abstract

    GenHITS: A network science approach to driver gene detection in human regulatory network using gene’s influence evaluation

    M Akhavan-Safar, B Teimourpour, M Kargari
    Journal Papers , , {Pages }

    Abstract

    A Network Science Approach to Driver Gene Detection In Human Regulatory Network Using Genes Influence Evaluation

    Mostafa Akhavan Safar, Babak Teimourpour, Mehrdad Kargari
    Journal PapersarXiv preprint arXiv:2001.09481 , 2020 January 26, {Pages }

    Abstract

    Cancer disease occurs because of a disorder in the cellular regulatory mechanism, Which causes cellular malformation. The genes that start the malformation are called Cancer driver genes (CDGs). Numerous computational methods have been introduced to identify cancer driver genes that use the concept of mutation. Regarding abnormalities spread in human cell and tumor development, CDGs are likely to be the potential types of gene with high influence in the network. This increases the importance of influence diffusion concept for the identification of CDGs. recently developed a method based on influence maximization for identifying cancer driver genes. One of the challenges in these types of networks is to find the power of regulatory interacti

    A new application of community detection for identifying the real specialty of physicians

    Saeed Shirazi, Amir Albadvi, Elham Akhondzadeh, Farshad Farzadfar, Babak Teimourpour
    Journal PapersInternational Journal of Medical Informatics , 2020 May 4, {Pages 104161 }

    Abstract

    BackgroundThere is an increasing trend in using network science methods and algorithms, including community detection methods, in different areas of healthcare. These areas include protein networks, drug prescriptions, healthcare fraud detection, and drug abuse. Counterfeit drugs, off-label marketing issues, and finding the healthcare community structures in a network of hospitals, are examples of using community detection in healthcare.ObjectiveThis paper attempts to find physicians’ real medical specialties based on their prescription history. As a novel application of community detection in the healthcare field, this knowledge can be used as an alternative for missing values of the healthcare databases. Therefore, it could help scienti

    KatzDriver: A network Based method to predict cancer causal genes in GR Network

    Mostafa Akhavansafar, Babak Teimourpour
    Journal PapersarXiv preprint arXiv:2003.06266 , 2020 March 13, {Pages }

    Abstract

    One of the important issues in oncology is finding the genes that perturbation the cell functionality, and result in cancer propagation. The genes, namely driver genes, when they mutate in expression, result in cancer through activation of the mutated proteins. So, many methods have been introduced to predict this group of genes. These are mostly computational methods based on the number of mutations of each gene. Recently, some network-based methods have been proposed to predict Cancer Driver Genes (CDGs). In this study, we use a network-based approach and relative importance of each gene in the propagation and absorption of genes anomalies in the network to recognize CDGs. The experimental results are compared with 19 previous methods tha

    A network Based method to predict cancer causal genes in GR Network.

    Mostafa Akhavansafar, Babak Teimourpour
    Journal PapersbioRxiv , 2020 January 1, {Pages }

    Abstract

    One of the important issues in oncology is finding the genes that perturbation the cell functionality, and result in cancer propagation. The genes, namely driver genes, when they mutate in expression, result in cancer through activation of the mutated proteins. So, many methods have been introduced to predict this group of genes. These are mostly computational methods based on the number of mutations of each gene. Recently, some network-based methods have been proposed to predict Cancer Driver Genes (CDGs). In this research, we use a network-based approach and relative importance of each gene in the propagation and absorption of genes anomalies in the network to recognize CDGs. The experimental results are compared with 18 previous methods

    Analysis of ResearchGate, A Community Detection Approach

    Mohammad Heydari, Babak Teimourpour
    Journal PapersarXiv preprint arXiv:2003.05591 , 2020 March 12, {Pages }

    Abstract

    We are living in the data age. Communications over scientific networks creates new opportunities for researchers who aim to discover the hidden pattern in these huge repositories. This study utilizes network science to create collaboration network of Iranian Scientific Institutions. A modularity-based approach applied to find network communities. To reach a big picture of science production flow, analysis of the collaboration network is crucial. Our results demonstrated that geographic location closeness and ethnic attributes has important roles in academic collaboration network establishment. Besides, it shows that famous scientific centers in the capital city of Iran, Tehran has strong influence on the production flow of scientific activi

    Social Network Analysis of Passes and Communication Graph in Football by mining Frequent Subgraphs

    Amir Hossein Ahmadi, Ahmadi Noori, Babak Teimourpour
    Conference Papers2020 6th International Conference on Web Research (ICWR) , 2020 April 22, {Pages 07-Jan }

    Abstract

    Sport is regarded as an inseparable part of human life. Currently, a growing trend is observed in people's interest in football teams. In general, a successful procedure in players' communication is one of the main factors required for the victory of that team. The present study aimed to perform analyzes based on the perspective of social and communication networks (such as player passes and in-game transactions) to improve team performance. The analysis was performed on data collected from three matches of the Persepolis club in the first half-season of the Iranian Premier League 2019–20. This research seeks to review this issue from two integral perspectives as follows: 1) evaluating the performance of individuals as a part of a social

    Scientific Map of Data Mining Related Articles in CIVILICA Database Based on Co-Word Analysis

    Fateme Shahrabi, Babak Teimourpour, Mina Ghasemi, Meysam Alavi
    Journal Papers , Volume 6 , 2020 January , {Pages }

    Abstract

    Today, due to the large volume of data and the high speed of its production, data analysis using traditional methods is infeasible. Data mining, as one of the most popular topics in the present century, has contributed to the advancement of science and technology in a significant number of fields. In recent years, researchers have used data mining extensively for data analysis. One of the important issues for researchers in this field is to identify common areas in the field of data mining and to find areas of active research in this field for future research. On the other hand, the analysis of social networks in recent years as an appropriate tool for examining the present and future relationships between the entities of a network structur

    Scientific Map of Papers Related to Data Mining in Civilica Database Based on Co-Word Analysis

    Fateme Shahrabi Farahani, Meysam Alavi, Mina Ghasemi, Babak Teimourpour
    Journal PapersInternational Journal of Web Research , Volume 3 , Issue 1, 2020 June 30, {Pages 18-Nov }

    Abstract

    Today, due to the large volume of data and the high speed of data production, it is practically impossible to analyze data using traditional methods. Meanwhile, data mining, as one of the most popular topics in the present century, has contributed to the advancement of science and technology in a number of areas. In the recent decade, researchers have made extensive use of data mining to analyze data. One of the most important issues for researchers in this field is to identify common mainstreams in the fields of data mining and to find active research fields in this area for future research. On the other hand, the analysis of social networks in recent years as a suitable tool to study the present and future relationships between the entiti

    GenHITS: A Network Science Approach to Driver Gene Detection in Human Regulatory Network Using Gene’s Influence Evaluation

    Mostafa Akhavan Safar, Babak Teimourpour, Mehrdad Kargari
    Journal PapersJournal of Biomedical Informatics , 2020 December 14, {Pages 103661 }

    Abstract

    Cancer is among the diseases causing death, in which, cells uncontrollably grow and reproduce beyond the cell regulatory mechanism. In this disease, some genes are initiators of abnormalities and then transmit them to other genes through protein interactions. Accordingly, these genes are known as cancer driver genes (CDGs). In this regard, several methods have been previously developed for identifying cancer driver genes. Most of these methods are computational-based, which use the concept of mutation to predict CDGs. In this research, a method has been proposed for identifying CDGs in the transcription regulatory network using the concept of influence diffusion and by modifying the Hyperlink-Induced Topic Search algorithm based on the diff

    KatzDriver: A network based method to cancer causal genes discovery in gene regulatory network.

    Mostafa Akhavan Safar, Babak Teimourpour
    Journal PapersBiosystems , 2020 December 10, {Pages 104326 }

    Abstract

    One of the important problems in oncology is finding the genes that perturb the cell functionality and cause cancer. These genes, namely cancer driver genes (CDGs), when mutated, lead to the activation of the abnormal proteins. This abnormality is passed on to other genes by protein-protein interactions, which can cause cells to uncontrollably multiply and become cancerous. So, many methods have been introduced to predict this group of genes. Most of these methods are computational-based, which identify the CDGs based on mutations and genomic data. In this study, we proposed KatzDriver, as a network-based approach, in order to detect CDGs. This method is able to calculate the relative impact of each gene in the spread of abnormality in the

    Optimal allocation of short-term marketing resources with a life cycle customer value approach

    Amir Albadvi, Babak Teimourpour
    Journal PapersIranian Journal of Insurance Research , Volume 35 , Issue 2, 2020 August 22, {Pages }

    Abstract

    Objective:?Developing an optimization model based on CLV to customer targeting for the retention program Method:? This research consists of three?steps. In the first step, using data mining methods, the churn probability of each customer is determined. In the second step, the customer lifetime value for each customer is calculated and finally, in the last step, the model is solved using the LP-metric model in GAMS software. we used the proposed?model for one of the insurance organizations of the country.? Findings:? A bi-objective optimization model based on CLV is proposed for selecting target customers for the retention program and selecting relevant costs for each customer. One of the objective functions is set to maximize CLV from perf

    A network Based method to predict cancer causal genes in GR Network

    M Akhavansafar, B Teimourpour
    Journal Papers , , {Pages }

    Abstract

    Cancer driver gene discovery in transcriptional regulatory networks using influence maximization approach

    Majid Rahimi, Babak Teimourpour, Sayed-Amir Marashi
    Journal PapersComputers in Biology and Medicine , 2019 July 17, {Pages 103362 }

    Abstract

    Cancer driver genes (CDGs) are the genes whose mutations cause tumor growth. Several computational methods have been previously developed for finding CDGs. Most of these methods are sequence-based, that is, they rely on finding key mutations in genomic data to predict CDGs. In the present work, we propose iMaxDriver as a network-based tool for predicting driver genes by application of influence maximization algorithm on human transcriptional regulatory network (TRN). In the first step of this approach, the TRN is pruned and weighted by exploiting tumor-specific gene expression (GE) data. Then, influence maximization approach is used to find the influence of each gene. The top genes with the highest influence rate are selected as the potenti

    Community detection in attributed networks considering both structural and attribute similarities: two mathematical programming approaches

    Esmaeil Alinezhad, Babak Teimourpour, Mohammad Mehdi Sepehri, Mehrdad Kargari
    Journal PapersNeural Computing and Applications , 2019 February , {Pages 18-Jan }

    Abstract

    Community detection is one of the most well-known and emerging research topics in the area of social network analysis. There are a wide variety of approaches to find communities in the literature, each with its own advantages and disadvantages. A majority of these approaches tend to detect communities by only using the network topology. However, the distribution of the node attributes is correlated with the community structure in many real networks. Therefore, the quality of the discovered partitions can be enhanced by considering node attributes. In this study, two novel mathematical programming approaches are proposed to integrate the topological structure and node similarities, in which first the primary attributed network i

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    دروس نیمسال جاری

    • كارشناسي ارشد
      تحليل شبكه هاي اطلاعات ( واحد)
      دانشکده مهندسی صنایع و سیستم‌ها، گروه مهندسي فناوري اطلاعات

    دروس نیمسال قبل

    • كارشناسي ارشد
      شبكه هاي پيچيده ( واحد)
      دانشکده مهندسی صنایع و سیستم‌ها، گروه مهندسي فناوري اطلاعات
    • كارشناسي ارشد
      بازنمايي دانش و استدلال ( واحد)
    • دكتري
      شبكه هاي پيچيده: نظريه شبكه، الگوريتم ها و كاربردها ( واحد)
    • 1400
      رفيعي فرد, جواد
      كشف تكامل اجتماعات احساسات در توييتر به روش تحليل شبكه هاي اجتماعي
    • 1400
      عبدالهي شمامي, مريم
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