Comparing Clustering Techniques for Real Microarray Data

The clustering of genes detected as significant or differentially expressed provides useful information to biologists about functions and functional relationship of genes. There are variant types of clustering methods that can be applied in genomic data. These are mainly divided into the two groups, namely, hierarchical and partitional methods. In this paper, as the novelty, we perform a detailed clustering analysis for the recently collected boron microarray dataset to investigate biologically more interesting results and to construct a basis for the selection of the most effective method in the analysis of different microarray datum. In the calculation, we implement the agglomerative hierarchical clustering among hierarchical techniques and use the k-means and the PAMSAM methods within partitional clustering approaches, and finally use a recently improved method, called HIPAM, which is not only a hierarchical but also partitional approach. In the assessment, we compare and discuss the significant genes of the boron data whose estimated signals are found by the FGX normalization method.


Integer linear programming based solutions for construction of biological networks
Eren Özsoy, Öykü; Can, Tolga; Department of Health Informatics (2014)
Inference of gene regulatory or signaling networks from perturbation experiments and gene expression assays is one of the challenging problems in bioinformatics. Recently, the inference problem has been formulated as a reference network editing problem and it has been show that finding the minimum number of edit operations on a reference network in order to comply with perturbation experiments is an NP-complete problem. In this dissertation, we propose linear programming based solutions for reconstruction o...
Mathematical Modeling and Approximation of Gene Expression Patterns
Yılmaz, Fatih; Öktem, Hüseyin Avni (2004-09-03)
This study concerns modeling, approximation and inference of gene regulatory dynamics on the basis of gene expression patterns. The dynamical behavior of gene expressions is represented by a system of ordinary differential equations. We introduce a gene-interaction matrix with some nonlinear entries, in particular, quadratic polynomials of the expression levels to keep the system solvable. The model parameters are determined by using optimization. Then, we provide the time-discrete approximation of our time...
Inference of Gene Regulatory Networks Via Multiple Data Sources and a Recommendation Method
Ozsoy, Makbule Gulcin; Polat, Faruk; Alhajj, Reda (2015-11-12)
Gene regulatory networks (GRNs) are composed of biological components, including genes, proteins and metabolites, and their interactions. In general, computational methods are used to infer the connections among these components. However, computational methods should take into account the general features of the GRNs, which are sparseness, scale-free topology, modularity and structure of the inferred networks. In this work, observing the common aspects between recommendation systems and GRNs, we decided to ...
Application of copulas in graphical models for inference of biological systems
Dokuzoğlu, Damla; Purutçuoğlu Gazi, Vilda; Department of Statistics (2016)
Naturally, genes interact with each other by forming a complicated network and the relationship between groups of genes can be showed by different functions as gene networks. Recently, there has been a growing concern in uncovering these complex structures from gene expression data by modeling them mathematically. The Gaussian graphical model (GGM) is one of the very popular parametric approaches for modelling the underlying types of biochemical systems. In this study, we evaluate the performance of this pr...
Short Time Series Microarray Data Analysis and Biological Annotation
Sökmen, Zerrin; Atalay, Mehmet Volkan; Atalay, Rengül (2008-01-01)
Significant gene list is the result of microarray data analysis should be explained for the purpose of biological functions. The aim of this study is to extract the biologically related gene clusters over the short time series microarray gene data by applying unsupervised methods and automatically perform biological annotation of those clusters. In the first step of the study, short time series microarray expression data is clustered according to similar expression profiles. After that, several biological d...
Citation Formats
V. Purutçuoğlu Gazi, “Comparing Clustering Techniques for Real Microarray Data,” 2012, Accessed: 00, 2020. [Online]. Available: