Browsing by Subject "Validation process"

Now showing 1 - 4 of 4

Open Access
Estimating the chance of success in IVF treatment using a ranking algorithm
(Springer, 2015) Güvenir, H. A.; Misirli, G.; Dilbaz, S.; Ozdegirmenci, O.; Demir, B.; Dilbaz, B.
In medicine, estimating the chance of success for treatment is important in deciding whether to begin the treatment or not. This paper focuses on the domain of in vitro fertilization (IVF), where estimating the outcome of a treatment is very crucial in the decision to proceed with treatment for both the clinicians and the infertile couples. IVF treatment is a stressful and costly process. It is very stressful for couples who want to have a baby. If an initial evaluation indicates a low pregnancy rate, decision of the couple may change not to start the IVF treatment. The aim of this study is twofold, firstly, to develop a technique that can be used to estimate the chance of success for a couple who wants to have a baby and secondly, to determine the attributes and their particular values affecting the outcome in IVF treatment. We propose a new technique, called success estimation using a ranking algorithm (SERA), for estimating the success of a treatment using a ranking-based algorithm. The particular ranking algorithm used here is RIMARC. The performance of the new algorithm is compared with two well-known algorithms that assign class probabilities to query instances. The algorithms used in the comparison are Naïve Bayes Classifier and Random Forest. The comparison is done in terms of area under the ROC curve, accuracy and execution time, using tenfold stratified cross-validation. The results indicate that the proposed SERA algorithm has a potential to be used successfully to estimate the probability of success in medical treatment.
Open Access
An ontology for collaborative construction and analysis of cellular pathways
(Oxford University Press, 2004-02-12) Demir, Emek; Babur, Özgün; Doğrusöz, Uğur; Gürsoy, Atilla; Ayaz, Aslı; Güleşır, Gürcan; Nişancı, Gürkan; Çetin Atalay, Rengül
Motivation: As the scientific curiosity in genome studies shifts toward identification of functions of the genomes in large scale, data produced about cellular processes at molecular level has been accumulating with an accelerating rate. In this regard, it is essential to be able to store, integrate, access and analyze this data effectively with the help of software tools. Clearly this requires a strong ontology that is intuitive, comprehensive and uncomplicated. Results: We define an ontology for an intuitive, comprehensive and uncomplicated representation of cellular events. The ontology presented here enables integration of fragmented or incomplete pathway information via collaboration, and supports manipulation of the stored data. In addition, it facilitates concurrent modifications to the data while maintaining its validity and consistency. Furthermore, novel structures for representation of multiple levels of abstraction for pathways and homologies is provided. Lastly, our ontology supports efficient querying of large amounts of data. We have also developed a software tool named pathway analysis tool for integration and knowledge acquisition (PATIKA) providing an integrated, multi-user environment for visualizing and manipulating network of cellular events. PATIKA implements the basics of our ontology. © Oxford University Press 2004; All rights reserved.
Open Access
A resampling-based meta-analysis for detection of differential gene expression in breast cancer
(BioMed Central, 2008) Gur-Dedeoglu, B.; Konu, O.; Kir, S.; Ozturk, A. R.; Bozkurt, B.; Ergul, G.; Yulug, I.G.
Background: Accuracy in the diagnosis of breast cancer and classification of cancer subtypes has improved over the years with the development of well-established immunohistopathological criteria. More recently, diagnostic gene-sets at the mRNA expression level have been tested as better predictors of disease state. However, breast cancer is heterogeneous in nature; thus extraction of differentially expressed gene-sets that stably distinguish normal tissue from various pathologies poses challenges. Meta-analysis of high-throughput expression data using a collection of statistical methodologies leads to the identification of robust tumor gene expression signatures. Methods: A resampling-based meta-analysis strategy, which involves the use of resampling and application of distribution statistics in combination to assess the degree of significance in differential expression between sample classes, was developed. Two independent microarray datasets that contain normal breast, invasive ductal carcinoma (IDC), and invasive lobular carcinoma (ILC) samples were used for the meta-analysis. Expression of the genes, selected from the gene list for classification of normal breast samples and breast tumors encompassing both the ILC and IDC subtypes were tested on 10 independent primary IDC samples and matched non-tumor controls by real-time qRT-PCR. Other existing breast cancer microarray datasets were used in support of the resampling-based meta-analysis. Results: The two independent microarray studies were found to be comparable, although differing in their experimental methodologies (Pearson correlation coefficient, R = 0.9389 and R = 0.8465 for ductal and lobular samples, respectively). The resampling-based meta-analysis has led to the identification of a highly stable set of genes for classification of normal breast samples and breast tumors encompassing both the ILC and IDC subtypes. The expression results of the selected genes obtained through real-time qRT-PCR supported the meta-analysis results. Conclusion: The proposed meta-analysis approach has the ability to detect a set of differentially expressed genes with the least amount of within-group variability, thus providing highly stable gene lists for class prediction. Increased statistical power and stringent filtering criteria used in the present study also make identification of novel candidate genes possible and may provide further insight to improve our understanding of breast cancer development.
Open Access
Role of the environmental spectrum in the decoherence and dephasing of multilevel quantum systems
(The American Physical Society, 2005) Hakioǧlu T.; Savran, K.
We examine the effect of multilevels on decoherence and dephasing properties of a quantum system consisting of a nonideal two level subspace, identified as the qubit, and a finite set of higher energy levels above this qubit subspace. The whole system is under interaction with an environmental bath through a Caldeira-Leggett type coupling. The model that we use is an rf-SQUID under macroscopic quantum coherence and coupled inductively to a flux noise characterized by an environmental spectrum. The model interaction can generate dipole couplings which can be appreciable between the qubit and the high levels. The decoherence properties of the qubit subspace is examined numerically using the master equation formalism of the system's reduced density matrix. We calculate the relaxation and dephasing times as the spectral parameters of the environment are varied. We observe that, these calculated time scales receive contribution from all available frequencies in the noise spectrum (even well above the system's resonant frequency scales) stressing the dominant role played by the nonresonant transitions. The relaxation and dephasing and the leakage times thus calculated, strongly depend on the appreciably interacting levels determined by the strength of the dipole coupling. Under the influence of these nonresonant and multilevel effects, the validity of the two level approximation is dictated not by the low temperature as conveniently believed, but by these multilevel dipole couplings as well as the availability of the environmental modes.