Erten, Cesim

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Erten, Cesim
C.,Erten
C. Erten
Cesim, Erten
Erten, Cesim
C.,Erten
C. Erten
Cesim, Erten
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Doç. Dr.
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Cesım@khas.edu.tr
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Computer Engineering
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Former Staff
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Scholarly Output

17

Articles

9

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0

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2

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2009 - 200912010 - 20113
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Author: Sözdinler, Melih

Scholarly Output Search Results

Now showing 1 - 4 of 4
  • Thumbnail Image
    Conference Object
    Citation - Scopus: 0
    An Integrated Model for Visualizing Biclusters From Gene Expression Data and Ppi Networks
    (2010) Aladağ, Ahmet Emre; Erten, Cesim; Erten, Cesim; Sözdinler, Melih; Computer Engineering
    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.
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    Article
    Citation - WoS: 4
    Citation - Scopus: 7
    Improving Performances of Suboptimal Greedy Iterative Biclustering Heuristics Via Localization
    (Oxford University Press, 2010) Erten, Cesim; Erten, Cesim; Sözdinler, Melih; Computer Engineering
    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.
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    Article
    Citation - WoS: 1
    Citation - Scopus: 0
    Reliability-Oriented Bioinformatic Networks Visualization
    (Oxford University Press, 2011) Aladağ, Ahmet Emre; Erten, Cesim; Erten, Cesim; Sözdinler, Melih; Computer Engineering
    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.
  • Thumbnail Image
    Conference Object
    Citation - WoS: 5
    Citation - Scopus: 7
    Biclustering Expression Data Based on Expanding Localized Substructures
    (Springer-Verlag Berlin, 2009) Erten, Cesim; Erten, Cesim; Sözdinler, Melih; Computer Engineering
    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.