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Wyszukujesz frazę "gene expression data" wg kryterium: Temat


Wyświetlanie 1-7 z 7
Tytuł:
Properties of a Singular Value Decomposition Based Dynamical Model of Gene Expression Data
Autorzy:
Simek, K.
Powiązania:
https://bibliotekanauki.pl/articles/908156.pdf
Data publikacji:
2003
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
informatyka
multiple gene expression
singular value decomposition
dynamical model of gene expression data
Opis:
Recently, data on multiple gene expression at sequential time points were analyzed using the Singular Value Decomposition (SVD) as a means to capture dominant trends, called characteristic modes, followed by the fitting of a linear discrete-time dynamical system in which the expression values at a given time point are linear combinations of the values at a previous time point. We attempt to address several aspects of the method. To obtain the model, we formulate a nonlinear optimization problem and present how to solve it numerically using the standard MATLAB procedures. We use freely available data to test the approach. We discuss the possible consequences of data regularization, called sometimes "polishing", on the outcome of the analysis, especially when the model is to be used for prediction purposes. Then, we investigate the sensitivity of the method to missing measurements and its abilities to reconstruct the missing data. Summarizing, we point out that approximation of multiple gene expression data preceded by SVD provides some insight into the dynamics, but may also lead to unexpected difficulties, like overfitting problems.
Źródło:
International Journal of Applied Mathematics and Computer Science; 2003, 13, 3; 337-345
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Selecting Differentially Expressed Genes for Colon Tumor Classification
Autorzy:
Fujarewicz, K.
Wiench, M.
Powiązania:
https://bibliotekanauki.pl/articles/908154.pdf
Data publikacji:
2003
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
medycyna
automatyka
colon tumor
gene expression data
microarrays
support vector machines
feature selection
classification
Opis:
DNA microarrays provide a new technique of measuring gene expression, which has attracted a lot of research interest in recent years. It was suggested that gene expression data from microarrays (biochips) can be employed in many biomedical areas, e.g., in cancer classification. Although several, new and existing, methods of classification were tested, a selection of proper (optimal) set of genes, the expressions of which can serve during classification, is still an open problem. Recently we have proposed a new recursive feature replacement (RFR) algorithm for choosing a suboptimal set of genes. The algorithm uses the support vector machines (SVM) technique. In this paper we use the RFR method for finding suboptimal gene subsets for tumor/normal colon tissue classification. The obtained results are compared with the results of applying other methods recently proposed in the literature. The comparison shows that the RFR method is able to find the smallest gene subset (only six genes) that gives no misclassifications in leave-one-out cross-validation for a tumor/normal colon data set. In this sense the RFR algorithm outperforms all other investigated methods.
Źródło:
International Journal of Applied Mathematics and Computer Science; 2003, 13, 3; 327-335
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Analysis of dynamics of gene exspression using singular value decomposition
Autorzy:
Simek, K.
Kimmel, M.
Powiązania:
https://bibliotekanauki.pl/articles/332865.pdf
Data publikacji:
2002
Wydawca:
Uniwersytet Śląski. Wydział Informatyki i Nauki o Materiałach. Instytut Informatyki. Zakład Systemów Komputerowych
Tematy:
wielokrotna ekspresja genów
dynamiczny model danych ekspresji genów
pojedynczy rozkład wartości
multiple gene expression
dynamic model of gene expression data
singular value decomposition
Opis:
Recently, data on multiple gene expression at sequential time points were analyzed, using Singular Value Decomposition (SVD) as a means to capture dominant trends, called characteristic modes, followed by fitting of a linear discrete-time dynamical model in which the expression values at a given time point are linear combinations of the values at a previous time point. We attempt to address several aspects of the method. To obtain the model we formulate a nonlinear optimization problem and present how to solve it numerically using standard MATLAB procedures. We use publicly available data to test the approach. Then, we investigate the sensitivity of the method to missing measurements and its possibilities to reconstruct missing data. Summarizing we point out that approximation of multiple gene expression data preceded by SVD provides some insight into the dynamics but may also lead to unexpected difficulties.
Źródło:
Journal of Medical Informatics & Technologies; 2002, 3; MI31-40
1642-6037
Pojawia się w:
Journal of Medical Informatics & Technologies
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Discovering diagnostic gene targets for early diagnosis of acute GVHD using methods of computational intelligence on gene expression data
Autorzy:
Fiasch'e, M.
Morabito, F. C.
Verma, A.
Kasabov, N.
Cuzzola, M.
Iacopino, P.
Powiązania:
https://bibliotekanauki.pl/articles/91634.pdf
Data publikacji:
2011
Wydawca:
Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
Tematy:
acute graft-versus-host disease
aGVHD
haematopoietic stem cell transplantation
HSCT
immunologic attack
diagnosis
computational intelligence
gene expression data
Opis:
This is an application paper of applying standard methods of computational intelligence to identify diagnostic gene targets and to use them for a successful diagnosis of a medical problem - acute graft-versus-host disease (aGVHD). This is the major complication after allogeneic haematopoietic stem cell transplantation (HSCT) in which functional immune cells of donor, recognize the recipient as ”foreign” and mount an immunologic attack. In this paper we analyzed gene-expression profiles of 47 genes associated with allo-reactivity in 59 patients submitted to HSCT. We have applied different dimensionality reduction techniques of the variable space, combined with different classifiers to detect the aGVHD at onset of clinical signs. This is a preliminary study which utilises both computational and biological evidence for the involvement of a limited number of genes for the diagnosis of aGVHD. Directions for further studies are also outlined in this paper.
Źródło:
Journal of Artificial Intelligence and Soft Computing Research; 2011, 1, 1; 81-89
2083-2567
2449-6499
Pojawia się w:
Journal of Artificial Intelligence and Soft Computing Research
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Measuring comparative statistical effectiveness of cancer subtype categorization using gene expression data
Autorzy:
Avila, Clemenshia P.
Deepa, C.
Powiązania:
https://bibliotekanauki.pl/articles/38708033.pdf
Data publikacji:
2024
Wydawca:
Instytut Podstawowych Problemów Techniki PAN
Tematy:
cancer subtype
gene expression data
machine learning
Deep Flexible Neural Forest
strategy
podtyp raka
dane dotyczące ekspresji genów
nauczanie maszynowe
głęboki las neuronowy
elastyczny las neuronowy
strategia
Opis:
This work focused on the analysis of various gene expression-based cancer subtype classification approaches. Correctly classifying cancer subtypes is critical for understanding cancer pathophysiology and effectively treating cancer patients by using gene expression data to categorize cancer subtypes. When dealing with limited samples and high-dimensional biological data, most classifiers may suffer from overfitting and lower precision. The goal of this research is to develop a machine learning (ML) system capable of classifying human cancer subtypes based on gene expression data in cancer cells. These issues can be solved using ML algorithms such as Transductive Support Vector Machines (TSVM), Boosting Cascade Deep Forest (BCD Forest), Enhanced Neural Network Classifier (ENNC), Deep Flexible Neural Forest (DFN Forest), Convolutional Neural Network (CNN), and Cascade Flexible Neural Forest (CFN Forest). In inferring the benefits and rawbacks of these strategies, such as DFN Forest and CFN Forest, the findings are 95%.
Źródło:
Computer Assisted Methods in Engineering and Science; 2024, 31, 2; 261-272
2299-3649
Pojawia się w:
Computer Assisted Methods in Engineering and Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Impact of DNA microarray data transformation on gene expression analysis - comparison of two normalization methods
Autorzy:
Schmidt, Marcin
Handschuh, Luiza
Zyprych, Joanna
Szabelska, Alicja
Olejnik-Schmidt, Agnieszka
Siatkowski, Idzi
Figlerowicz, Marek
Powiązania:
https://bibliotekanauki.pl/articles/1039855.pdf
Data publikacji:
2011
Wydawca:
Polskie Towarzystwo Biochemiczne
Tematy:
microarray
data normalization
enterocyte
transcriptome analysis
probiotic
adhesion
gene expression profiling
Opis:
Two-color DNA microarrays are commonly used for the analysis of global gene expression. They provide information on relative abundance of thousands of mRNAs. However, the generated data need to be normalized to minimize systematic variations so that biologically significant differences can be more easily identified. A large number of normalization procedures have been proposed and many softwares for microarray data analysis are available. Here, we have applied two normalization methods (median and loess) from two packages of microarray data analysis softwares. They were examined using a sample data set. We found that the number of genes identified as differentially expressed varied significantly depending on the method applied. The obtained results, i.e. lists of differentially expressed genes, were consistent only when we used median normalization methods. Loess normalization implemented in the two software packages provided less coherent and for some probes even contradictory results. In general, our results provide an additional piece of evidence that the normalization method can profoundly influence final results of DNA microarray-based analysis. The impact of the normalization method depends greatly on the algorithm employed. Consequently, the normalization procedure must be carefully considered and optimized for each individual data set.
Źródło:
Acta Biochimica Polonica; 2011, 58, 4; 573-580
0001-527X
Pojawia się w:
Acta Biochimica Polonica
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Reference genes for gene expression studies on non-small cell lung cancer
Autorzy:
Gresner, Peter
Gromadzinska, Jolanta
Wasowicz, Wojciech
Powiązania:
https://bibliotekanauki.pl/articles/1040589.pdf
Data publikacji:
2009
Wydawca:
Polskie Towarzystwo Biochemiczne
Tematy:
non-small cell lung cancer
reference genes
data normalization
real-time PCR
gene expression
Opis:
Study Objective: The aim of this study was to test a panel of 6 reference genes in order to identify and validate the most suitable reference genes for expression studies in paired healthy and non-small cell lung cancer tissues. Method: Quantitative real-time PCR followed by the NormFinder- and geNorm-based analysis was employed. The study involved 21 non-small cell lung cancer patients. Results: The analysis of experimental data revealed HPRT1 as the most stable gene followed by RPLP0 and ESD. In contrast, GAPDH was found to be the least stable gene. HPRT1 together with ESD was revealed as the pair of genes introducing the least systematic error into data normalization. Validation by bootstrap random sampling technique and by normalizing exemplary gene expression data confirmed the results. Conclusion: Although HPRT1 and ESD may by recommended for data normalization in gene expression studies on non-small cell lung cancer, the suitability of selected reference genes must be unconditionally validated prior to each study.
Źródło:
Acta Biochimica Polonica; 2009, 56, 2; 307-316
0001-527X
Pojawia się w:
Acta Biochimica Polonica
Dostawca treści:
Biblioteka Nauki
Artykuł
    Wyświetlanie 1-7 z 7

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