Erten, Cesim

Erten, Cesim

Name Variants

Erten, Cesim
C.,Erten
C. Erten
Cesim, Erten
Erten, Cesim
C.,Erten
C. Erten
Cesim, Erten

Job Title

Doç. Dr.

Email Address

Cesım@khas.edu.tr

Full item page

Scholarly Output

17

Articles

9

Citation Count

0

Supervised Theses

2 Scholarly Output Search Results

Now showing 1 - 10 of 15

Citation Count: 13
Force-Directed Approaches To Sensor Localization
(Association for Computing Machinery, 2010) Efrat, Alon; Forrester, David; Iyer, Anand; Kobourov, Stephen G.; Erten, Cesim; Kılış, Ozan
As the number of applications of sensor networks increases so does the interest in sensor network localization that is in recovering the correct position of each node in a network of sensors from partial connectivity information such as adjacency range or angle between neighboring nodes. In this article we consider the anchor-free localization problem in sensor networks that report possibly noisy range information and angular information about the relative order of each sensor's neighbors. Previously proposed techniques seem to successfully reconstruct the original positions of the nodes for relatively small networks with nodes distributed in simple regions. However these techniques do not scale well with network size and yield poor results with nonconvex or nonsimple underlying topology. Moreover the distributed nature of the problem makes some of the centralized techniques inapplicable in distributed settings. To address these problems we describe a multiscale dead-reckoning (MSDR) algorithm that scales well for large networks can reconstruct complex underlying topologies and is resilient to noise. The MSDR algorithm takes its roots from classic force-directed graph layout computation techniques. These techniques are augmented with a multiscale extension to handle the scalability issue and with a dead-reckoning extension to overcome the problems arising with nonsimple topologies. Furthermore we show that the distributed version of the MSDR algorithm performs as well as if not better than its centralized counterpart as shown by the quality of the layout measured in terms of the accuracy of the computed pairwise distances between sensors in the network.
Citation Count: 93
Spinal: Scalable Protein Interaction Network Alignment
(Oxford University Press, 2013) Aladağ, Ahmet Emre; Erten, Cesim
Motivation: Given protein-protein interaction (PPI) networks of a pair of species a pairwise global alignment corresponds to a one-to-one mapping between their proteins. Based on the presupposition that such a mapping provides pairs of functionally orthologous proteins accurately the results of the alignment may then be used in comparative systems biology problems such as function prediction/verification or construction of evolutionary relationships. Results: We show that the problem is NP-hard even for the case where the pair of networks are simply paths. We next provide a polynomial time heuristic algorithm SPINAL which consists of two main phases. In the first coarse-grained alignment phase we construct all pairwise initial similarity scores based on pairwise local neighborhood matchings. Using the produced similarity scores the fine-grained alignment phase produces the final one-to-one mapping by iteratively growing a locally improved solution subset. Both phases make use of the construction of neighborhood bipartite graphs and the contributors as a common primitive. We assess the performance of our algorithm on the PPI networks of yeast fly human and worm. We show that based on the accuracy measures used in relevant work our method outperforms the state-of-the-art algorithms. Furthermore our algorithm does not suffer from scalability issues as such accurate results are achieved in reasonable running times as compared with the benchmark algorithms.
Citation Count: 7
Rednemo: Topology-Based Ppi Network Reconstruction Via Repeated Diffusion With Neighborhood Modifications
(Oxford University Press, 2017) Alkan, Ferhat; Erten, Cesim
Motivation: Analysis of protein-protein interaction (PPI) networks provides invaluable insight into several systems biology problems. High-throughput experimental techniques together with computational methods provide large-scale PPI networks. However a major issue with these networks is their erroneous nature
Citation Count: 4
Improving Performances of Suboptimal Greedy Iterative Biclustering Heuristics Via Localization
(Oxford University Press, 2010) Erten, Cesim; Sözdinler, Melih
Motivation: Biclustering gene expression data is the problem of extracting submatrices of genes and conditions exhibiting significant correlation across both the rows and the columns of a data matrix of expression values. Even the simplest versions of the problem are computationally hard. Most of the proposed solutions therefore employ greedy iterative heuristics that locally optimize a suitably assigned scoring function. Methods: We provide a fast and simple pre-processing algorithm called localization that reorders the rows and columns of the input data matrix in such a way as to group correlated entries in small local neighborhoods within the matrix. The proposed localization algorithm takes its roots from effective use of graph-theoretical methods applied to problems exhibiting a similar structure to that of biclustering. In order to evaluate the effectivenesss of the localization pre-processing algorithm we focus on three representative greedy iterative heuristic methods. We show how the localization pre-processing can be incorporated into each representative algorithm to improve biclustering performance. Furthermore we propose a simple biclustering algorithm Random Extraction After Localization (REAL) that randomly extracts submatrices from the localization pre-processed data matrix eliminates those with low similarity scores and provides the rest as correlated structures representing biclusters. Results: We compare the proposed localization pre-processing with another pre-processing alternative non-negative matrix factorization. We show that our fast and simple localization procedure provides similar or even better results than the computationally heavy matrix factorization pre-processing with regards to H-value tests. We next demonstrate that the performances of the three representative greedy iterative heuristic methods improve with localization pre-processing when biological correlations in the form of functional enrichment and PPI verification constitute the main performance criteria. The fact that the random extraction method based on localization REAL performs better than the representative greedy heuristic methods under same criteria also confirms the effectiveness of the suggested pre-processing method.
Citation Count: 0
An Integrated Model for Visualizing Biclusters From Gene Expression Data and Ppi Networks
(2010) Aladağ, Ahmet Emre; Erten, Cesim; Sözdinler, Melih
We provide a model to integrate the visualization of biclusters extracted from gene expresion data and the underlying PPI networks. Such an integration conveys the biologically relevant interconnection between these two structures inferred from biological experiments. We model the reliabilities of the structures using directed graphs with vertex and edge weights. The resulting graphs are drawn using appropriate weighted modifications of the algorithms necessary for the layered drawings of directed graphs. We provide applications of the proposed visualization model on the S. cerevisiae dataset. Copyright 2010 ACM.
Citation Count: 0
Fully Decentralized, Collaborative Multilateration Primitives for Uniquely Localizing Wsns
(Springer, 2009) Cakiroglu, Arda; Erten, Cesim
We provide primitives for uniquely localizing WSN nodes. The goal is to maximize the number of uniquely localized nodes assuming a fully decentralized model of computation. Each node constructs a cluster of its own and applies unique localization primitives on it. These primitives are based on constructing a special order for multilaterating the nodes within the cluster. The proposed primitives are fully collaborative and thus the number of iterations required to compute the localization is fewer than that of the conventional iterative multilateration approaches. This further limits the messaging requirements. With relatively small clusters and iteration counts we can localize almost all the uniquely localizable nodes. © 2009 Springer Berlin Heidelberg.
Citation Count: 1
Reliability-Oriented Bioinformatic Networks Visualization
(Oxford University Press, 2011) Aladağ, Ahmet Emre; Erten, Cesim; Sözdinler, Melih
We present our protein-protein interaction (PPI) network visualization system RobinViz (reliability-oriented bioinformatic networks visualization). Clustering the PPI network based on gene ontology (GO) annotations or biclustered gene expression data providing a clustered visualization model based on a central/peripheral duality computing layouts with algorithms specialized for interaction reliabilities represented as weights completely automated data acquisition processing are notable features of the system.
Global Alignment of Metabolic Pathways and Protein-Protein Interaction Networks
(Kadir Has Üniversitesi, 2014) Abaka, Gamze; Erten, Cesim
Metabolic pathways and protein interaction networks are essential at almost every function for living organisms. Simply, while reactions produce life energy within cells, protein interaction networks provide biological functions. Also, abnormal reactions or interactions cause various disorders. Thus, in bioinformatics, most of the studies are based on these networks in order to find hopeful results for these disorders and biological challenges. Solving alignment problem is one of these studies such that it tries to find similar reactions, proteins or functions. In this thesis, we focus on that problem within both metabolic pathways and protein interaction networks. Firstly, we propose a constrained alignment algorithm, CAMPways, for one-to-many alignment of metabolic pathways and we extend the framework, CAPPI, for one-to-one protein interaction network alignment with necessary changes. Afterwards, we provide the computational intractability of the problem and finally we compare our algorithm with different algorithms on actual metabolic pathways and protein interaction networks.
Citation Count: 1
Fully Decentralized and Collaborative Multilateration Primitives for Uniquely Localizing Wsns
(Springer International Publishing Ag, 2010) Çakıroğlu, Arda; Erten, Cesim
We provide primitives for uniquely localizing WSN nodes. The goal is to maximize the number of uniquely localized nodes assuming a fully decentralized model of computation. Each node constructs a cluster of its own and applies unique localization primitives on it. These primitives are based on constructing a special order for multilaterating the nodes within the cluster. The proposed primitives are fully collaborative and thus the number of iterations required to compute the localization is fewer than that of the conventional iterative multilateration approaches. This further limits the messaging requirements. With relatively small clusters and iteration counts we can localize almost all the uniquely localizable nodes.
Citation Count: 5
Biclustering Expression Data Based on Expanding Localized Substructures
(Springer-Verlag Berlin, 2009) Erten, Cesim; Sözdinler, Melih
Biclustering gene expression data is the problem of extracting submatrices of genes and conditions exhibiting significant correlation across both the rows and the columns of a data matrix of expression values. We provide a method LEB (Localize-and-Extract Biclusters) which reduces the search space into local neighborhoods within the matrix by first localizing correlated structures. The localization procedure takes its roots from effective use of graph-theoretical methods applied to problems exhibiting a similar structure to that of biclustering. Once interesting structures are localized the search space reduces to small neighborhoods and the biclusters are extracted from these localities. We evaluate the effectiveness of our method with extensive experiments both using artificial and real datasets.