Evaluation of Signaling Cascades Based on the Weights from Microarray and ChIP-seq Data

Isik, Zerrin
Atalay, Mehmet Volkan
Atalay, Rengül
In this study, we combined the ChIP-seq and the transcriptome data and integrated these data into signaling cascades. Integration was realized through a framework based on data- and model-driven hybrid approach. An enrichment model was constructed to evaluate signaling cascades which resulted in specific cellular processes. We used ChIP-seq and microarray data from public databases which were obtained from HeLa cells under oxidative stress having similar experimental setups. Both ChIP-seq and array data were analyzed by percentile ranking for the sake of simultaneous data integration on specific genes. Signaling cascades from KEGG pathway database were subsequently scored by taking sum of the individual scores of the genes involved within the cascade. This scoring information is then transferred to en route of the signaling cascade to form the final score.Signaling cascade model based framework that we describe in this study is a novel approach which calculates scores for the target process of the analyzed signaling cascade, rather than assigning scores to gene product nodes


Determination of the effect of polyadenylation SLR values on microarray data classification
Aslan, Ümit; Can, Tolga; Department of Computer Engineering (2014)
Microarray data classification is generally used to predict unknown sample outcomes by the help of models created using the preprocessed and categorized microarray data that includes gene expression values. Preparation of microarray experiments, design of Affymetrix chips and availability of previous microarray experiments give the opportunity to extract a new kind of data; differential expressions of proximal and distal probes (Short to Long Ratio -SLR- values), which is used to predict the alternative pol...
Improvement of Hyperspectral Classification Accuracy with Limited Training Data Using Meanshift Segmentation
Özdemir, Okan Bilge; Çetin, Yasemin (2014-04-25)
In this study, the performance of hyperspectral classification algorithms with limited training data investigated. Support Vector Machines (SVM) with Gaussian kernel is used. Principle Component Analysis (PCA) is employed for preprocessing and meanshift segmentation is used to incorporate spatial information with spectral information to observe the effect spatial information. Pattern search algorithm is used to optimize meanshift segmentation parameters. The performance of the algorithm is demonstrated on h...
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...
Evaluation of environmental parameters in logistic regression models for landslide susceptibility mapping
Süzen, Mehmet Lütfi (Informa UK Limited, 2012-01-01)
The aim of this study was to determine how well the landslide susceptibility parameters, obtained by data-dependent statistical models, matched with the parameters used in the literature. In order to achieve this goal, 20 different environmental parameters were mapped in a well-studied landslide-prone area, the Asarsuyu catchment in northwest Turkey. A total of 4400 seed cells were generated from 47 different landslides and merged with different attributes of 20 different environmental causative variables i...
Investigation and comparison of the preprocessing algorithms for microarrayanalysis for robust gene expression calculation and performance analysis of technical replicates
İLK, HAKKI GÖKHAN; İlk Dağ, Özlem; KONU KARAKAYALI, ÖZLEN; ÖZDAĞ, Hilal (2006-04-19)
Preprocessing of microarray data involves the necessary steps of background correction, normalization and summarization of the raw intensity data obtained from cDNA or oligo-arrays before statistical analysis. Several algorithms, namely RMA, dChip, and MAS5 exist for the preprocessing of Affymetrix microarray data. Previous studies have identified RMA as one of most accurate algorithms while MAS5 was characterized with lower accuracy and sensitivity levels. In this study, performance of different preprocess...
Citation Formats
Z. Isik, M. V. Atalay, and R. Atalay, “Evaluation of Signaling Cascades Based on the Weights from Microarray and ChIP-seq Data,” 2009, vol. 8, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/53798.