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IEEE International Conference on Circuit ,Power and Computing Technologies (ICCPCT)

作者： Susymary, J. Lawrance, R. AyyaNadar Janaki Ammal Coll Dept Comp Sci Sivakasi Tamil Nadu India AyyaNadar Janaki Ammal Coll Dept Comp Applicat Sivakasi Tamil Nadu India

ISBN: (纸本)9781509049677

Graph mining is an advancing region notably to dig up unique and intuitiveness facts from data that is pictured as a graph. Graph data like protein-protein interaction network is pervasive in actuality so that graph theory means of analysis to network can advantage supplementary findings of proteins associated with positive topological characteristic have precise biological function. Distinct graph mining techniques such as frequent subgraph mining, clustering, classification is feasible to figure out the protein-protein interaction networks. Clustering is one of the well-known technique to boast a class of proteins with related biological function. Some of the graph based clustering methods include local neighborhood density search method, flow simulation method and population based stochastic search method. mcl algorithm based on flow simulation method over protein-protein interaction network of proteins related zika virus has been analytically gauged and indicated how interesting clusters are raised.

关键词： graph mining clustering mcl algorithm graph theory protein-protein interaction zika virus

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A Study of Wireless Mobile Node Localization algorithm Based on mcl and HS

A Study of Wireless Mobile Node Localization Algorithm Based...

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Asia Simulation Conference/International Conference on System Simulation and Scientific Computing (AsiaSim and ICSC 2012)

作者： Chen, Yan Fu, Jingqi Shanghai Univ Sch Mechatron Engn & Automat Shanghai Peoples R China

ISBN: (纸本)9783642343896

This paper proposes the methods to improve the Monte Carlo (mcl) algorithm for the wireless mobile node localization. It combines the anchor boxes constructed by different power signals with the node location information in the previous time to reduce the sampling region. Through sampling and filtering in this region, we adopt the Harmony Search (HS) algorithm to optimize the obtained samples and then calculate the estimated value of the node location. Moreover, the RSSI ranging is used to assist localization. And it takes full advantage of the nodes information with high availabilities. The simulation results show that the improved algorithm reduces the requirements of anchors density and improves the sampling filter efficiencies and the localization accuracy.

关键词： node localization mcl algorithm HS algorithm RSSI

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An efficient algorithm for identifying (l, d) motif from huge DNA datasets

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JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING 2021年第1期12卷 485-495页

作者： Masood, M. Mohamed Divan Arunarani, A. R. Manjula, D. Sugumaran, Vijayan BS Abdur Rahman Crescent Inst Sci & Technol Dept Comp Sci & Engn Chennai Tamil Nadu India Anna Univ Dept Comp Sci & Engn Chennai Tamil Nadu India Oakland Univ Dept Decis & Informat Sci Rochester MI 48063 USA Oakland Univ Ctr Data Sci & Big Data Analyt Rochester MI 48063 USA

Discovering Transcription Factor Binding Sites (TFBS) has immense significance in terms of developing techniques and evaluating regulatory processes in biological systems. The DNA gene sequence encompasses large volume of datasets so a new methodology is needed to analyze them in the quickest possible time. Over the past decades, the planted (l, d) motif discovery methodology has been used for locating TFBS in the genetic region. This paper focuses on developing a new approach for motif identification using planted (l, d) motif discovery algorithm. The proposed algorithm is named ESMD (Emerging Substring based Motif Detection), which is based on two processes: Mining and Combining Emerging Substrings. In the mining step, an array is initially created, based on the suffix array (SA) and the longest common prefix array (LCP). A MapReduce programming model handles the mining of emerging substring process since DNA gene sequences constitute huge data. The next step combines the emerging substrings of different lengths. The resulting models have been evaluated using two different metrics, the Pearson Correlation Coefficient (PCC) and the Area Under Curve (AUC). Both have produced much better results than existing methods.

关键词： Transcription factor binding sites Suffix array Longest common prefixes MapReduce Mining emerging substring Combining emerging substring mcl algorithm

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Interactome based biomarker discovery for irritable bowel syndrome-A systems biology approach

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COMPUTATIONAL BIOLOGY AND CHEMISTRY 2018年 76卷 218-224页

作者： Kalva, Sukesh Bindusree, Ganigara Alexander, V. Madasamy, Parani SRM Univ Dept Genet Engn Kattankulathur 603203 India Exonn Technol Madras Tamil Nadu India

Irritable bowel syndrome (IBS) is a common functional gastrointestinal (GI) disorder around world with no standard therapy. Till date, the pathophysiology of IBS is not clearly understood due to complexity of the disease. Current study reveals the underlying mechanism of IBS using systems biological approach. The complexity of IBS was explained by constructing protein-protein interaction (PPI) network from the text mined genes/proteins. PPI network displayed 68822 interactions from 3595 proteins. IBS interactome was mapped with colon tissue interactome which resulted in a sub-network containing 153 genes. Further, mcl algorithm was applied to sub-network to identify six major clusters. These cluster genes are involved in several pathways such as MAPK, PI3K/Akt, and NF-kappa B. The obtained clustered genes were prioritized using differentially expressed transcriptome data of 45 IBS and 45 normal volunteers. Among the differentially expressed genes, FUS, UNC5CL and BCLAF1 were found in the clusters, suggesting that the identified clusters could play a potential role in the regulation of IBS. Further pathway analysis of cluster genes revealed their molecular association with IBS. Gene prioritization studies identified top 10 genes that can be used as candidate biomarkers for early diagnosis of IBS. Out of top ten genes, PRPF31 was expressed in all biofluids (serum, saliva and urine).

关键词： Irritable bowel syndrome PPI network mcl algorithm Biomarker Bioavailability

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Clustering Approach to Collaborative Filtering Using Social Networks

Clustering Approach to Collaborative Filtering Using Social ...

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4th IEEE International Conference on Electronics Information and Emergency Communication (ICEIEC)

作者： Cogo, Emir Donko, Dzenana Univ Sarajevo Fac Elect Engn Sarajevo 71000 Bosnia & Herceg

ISBN: (纸本)9781467349338

This paper presents results of using clustering to improve results of collaborative filtering. Clusters of users are created using friendship links within a social network using Markov Chain algorithm (mcl). Clusters are then used to make prediction of user choices using item based collaborative filtering with cosine similarity. Using the results from analyzing different cluster sizes, new algorithm was proposed that saves time and memory resources.

关键词： Clustering Graph clustering Collaborative filtering Social networks mcl algorithm

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Nash Differential Evolution (NashDE) algorithm for WAMS Planning 8

Nash Differential Evolution (NashDE) Algorithm for WAMS Plan...

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8th IEEE India International Conference on Power Electronics (IICPE)

作者： Thasnimol, C. M. Rajathy, R. Pondicherry Engn Coll Dept EEE Pondicherry India

ISBN: (纸本)9781538649961

Wide Area Monitoring System (WAMS) is an inevitable component in todays power system since a small catastrophe like fluctuations caused by the renewable energy sources, fast changing loads, electric vehicles, etc. will lead to the collapse of the whole system. This paper addresses the optimal planning of WAMS by Nash Differential Evolution (NashDE) algorithm, in which the variables of the problem are clustered into number of clusters, and will evolve parallelly by Differential Evolution (DE) towards the global objective of the problem. For optimization problems which are based on the connectivity of variables involved as in most of the power system planning processes, the convergence speed will greatly improve if variables in a cluster are physically connected. Further, this will ensure global convergence of the problem. Hence, this paper proposes a NashDE algorithm in which the variables are clustered based on their connectivity. Markov Clustering algorithm (mcl) is used for clustering the variables based on their connectivity. This is the first paper of such kind which addresses WAMS planning problem by Evolutionary Game Theory. Simulations are carried out for IEEE 14 and 30 bus test system using Python programming and the results are presented graphically.

关键词： PMU PDC WAMS NashDE Differential Evolution Evolutionary Game Theory mcl algorithm Dijkstra algorithm

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Clustering Approach to Collaborative Filtering Using Social Networks

Clustering Approach to Collaborative Filtering Using Social ...

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2013 IEEE 4th International Conference on Electronics Information and Emergency Communication

作者： Emir Cogo Dzenana Donko Faculty of Electrical Engineering University of Sarajevo Bosnia and Herzegovina

ISBN: (纸本)9781467349345

This paper presents results of using clustering to improve results of collaborative filtering. Clusters of users are created using friendship links within a social network using Markov Chain algorithm （mcl）. Clusters are then used to make prediction of user choices using item based collaborative filtering with cosine similarity. Using the results from analyzing different cluster sizes, new algorithm was proposed that saves time and memory resources.

关键词： Clustering Graph clustering Collaborative filtering Social networks mcl algorithm

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