249,487 Hits in 6.0 sec

Identification and handling of artifactual gene expression profiles emerging in microarray hybridization experiments

L. Brodsky
2004 Nucleic Acids Research  
Kositzky in the programming of the quick version of the nested clustering algorithm. We also wish to thank L. Segel and A. Bolshoy for critical reading of the manuscript.  ...  Different user-de®ned options of detection (method 1, method 2 or a combination) and handling (®ltering versus correction) of the artifactual data will be available.  ...  Almost all the methods of advanced mathematical analysis of microarray hybridizations (clustering, extraction of genes essential for class separation, networking, etc.) deal with gene expression pro®les  ... 
doi:10.1093/nar/gnh043 pmid:14999086 pmcid:PMC390318 fatcat:c6azac7rovgsrnqmb5e5na3tvu

Evaluation of the Gene Expression Profiles Complex Proximity Metric Effectiveness Based on a Hybrid Technique of Gene Expression Data Extraction

Lyudmyla Yasinska-Damri, Igor Liakh, Sergii Babichev, Bohdan Durnyak
2021 International Workshop on Informatics & Data-Driven Medicine  
Gene expression data processing in order to develop the systems of complex diseases diagnostic or/and gene regulatory networks (GRN) reconstruction is one of the actual direction of modern bioinformatics  ...  One of the important stages of this problem solving is an extraction of mutually correlated gene expression profiles (GEP) considering the used proximity metric.  ...  As a simulation result, the authors proposed a stepwise algorithm for extracting highly and mutually expressed gene expression profiles for their further grouping into clusters.  ... 
dblp:conf/iddm/Yasinska-DamriL21 fatcat:gwdtfofkxzebvod3y5jfe46vk4

A two-stage methodology for gene regulatory network extraction from time–course gene expression data

2006 Expert systems with applications  
The discovery of gene regulatory networks (GRN) from timecourse gene expression data (gene trajectory data) is useful for (1) identifying important genes in relation to a disease or a biological function  ...  The proposed method is demonstrated on the human fibroblast response gene expression data.  ...  The proposed method is demonstrated on the human fibroblast response gene expression data.  ... 
doi:10.1016/j.eswa.2005.09.048 fatcat:34owjy5q4nh47abvj4gnqbvedi

Two way clustering of microarray data using a hybrid approach

Raul Malutan, Bogdan Belean, Pedro Gomez Vilda, Monica Borda
2011 2011 34th International Conference on Telecommunications and Signal Processing (TSP)  
In this paper a hybrid approach is used to obtain a two way unsupervised clustering for a corrected microarray data.  ...  Independent Component Analysis method can be used to correct the data affected by corruption processes or to filter the uncorrectable one and then clustering methods can group similar genes or classify  ...  Considering the results from the external validations methods we applied the hybrid bilcustering on the data using first a k-means clustering of the samples and an EM clustering for the genes.  ... 
doi:10.1109/tsp.2011.6043698 dblp:conf/tsp/MalutanBVB11 fatcat:o5jtzflo3ndojp2ps2magxboxm

A novel distance-based iterative sequential KNN algorithm for estimation of missing values in microarray gene expression data

Chandra Das, Shilpi Bose, Matangini Chattopadhyay, Samiran Chattopadhyay
2016 International Journal of Bioinformatics Research and Applications  
Unfortunately, many algorithms for gene expression analysis require a complete matrix of gene expression values as input.  ...  Based on this distance a modified K-means clustering algorithm is proposed to accurately predict missing values in microarray gene expression data.  ...  CONCLUSION In this paper, a new hybrid distance based modified K-mean clustering algorithm based imputation method is proposed for estimating missing values in DNA microarray data.  ... 
doi:10.1504/ijbra.2016.080719 fatcat:6x2mvljdlva77mppau4nfwvqqa

A Survey of Computational Methods Used in Microarray Data Interpretation [chapter]

Brian Tjaden, Jacques Cohen
2006 Applied Mycology and Biotechnology  
K-Means The k-means clustering method is a common and relatively simple heuristic method for partitioning data points into k clusters (Tavazoie et al. 1999) .  ...  The relative number of hybridized sequences for each set Si provides a relative measure of how much Gi is expressed for a given experiment.  ...  Gene expression data is available in a number of public repositories.  ... 
doi:10.1016/s1874-5334(06)80010-9 fatcat:regly7mkkff6floj7zfclgi6ay

Hybrid Clustering Method for DNA Microarray Data Analysis

Sungwoo Kwon, Chonghun Han
2002 Genome Informatics Series  
Case Study We applied the proposed hybrid clustering to analyze gene expression of the yeast gene data collected from yeast Saccharomyces cereviseiae [3].  ...  JR009C, YHR174W, YKL152C YJR009C,YHR174W, YKL152C hybrid clustering results hierarchal clustering result for each gene hybrid clustering results hierarchal clustering result for each gene  ... 
doi:10.11234/gi1990.13.258 fatcat:3x2b3iknp5fx3fbj3cmmmcc324

Clustering approach to detect mRNA-degradation patterns from DNA–microarray gene-expression data

Susanne Motameny, Röbbe Wünschiers
2012 Biosystems and Information technology  
DNA-microarray based gene-expression analysis is based on hybridization events between messenger RNA (mRNA) and single stranded DNA probes.  ...  If these probes are distributed over the whole length of the mRNA molecule, information about mRNA-degradation patterns can be gathered with data clustering methods.  ...  Acknowledgments We like to thank Rikard Axelsson for cell growth and RNA-purification, Michael Baum and Nicole Rittner for performing the DNA-microarray experiments, Long Li for data processing, and Ralf  ... 
doi:10.11592/bit.121001 fatcat:rzw2cncogfd43nbahfzltkhg3u

Tissue Gene Expression Analysis Using Arrayed Normalized cDNA Libraries

H. Eickhoff
2000 Genome Research  
The second clustering method was applied to identify genes or gene clusters that reveal tissue-specific gene expression.  ...  Expression Analysis Using Hierarchical Clustering Algorithms The analysis of gene expressions was done via two similar clustering methods.  ...  Libraries Tissue Gene Expression Analysis Using Arrayed Normalized cDNA  ... 
doi:10.1101/gr.10.8.1230 pmid:10958641 pmcid:PMC310898 fatcat:mg3v724xjjhfhdfkz7426xil7i

Analysis of Population Based Metaheuristic Used for Gene Clustering

Arpit Jain, Shikha Agrawal, Jitendra Agrawal, Sanjeev Sharma
2013 International Journal of Computer and Communication Engineering  
Gene Clustering is one of the most popular application in the field of Bioinformatics. It is a method of grouping gene into clusters, such that each cluster must have similar gene expression levels.  ...  The two most popular population based globalized search algorithms Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) are used for clustering gene expression data but the main drawback of these  ...  ACKNOWLEDGMENT The authors would like to thank the anonymous reviewers for their detailed, valuable comments and constructive suggestions.  ... 
doi:10.7763/ijcce.2013.v2.164 fatcat:zj4oxbfivfdchfktrjq2dwxtte

Hybrid Algorithm for Clustering Gene Expression Data

S. Jacophine Susmi, H. Khanna Nehemiah, A. Kannan, G. Saranya
2015 Research Journal of Applied Sciences Engineering and Technology  
Further, the optimal cluster obtained by hybrid framework achieves cluster accuracy of 81.3, 80.2 and 78.2 for leukemia, lung and thyroid gene expression data respectively.  ...  The clusters obtained from adaptive pillar clustering algorithm achieve a maximum cluster gain of 894.84, 812.4 and 756 for leukemia, lung and thyroid gene expression data, respectively.  ...  The k-values were executed with same number of clusters for all the three gene expression data which gave a maximum gain values for adaptive pillar algorithm.  ... 
doi:10.19026/rjaset.11.2032 fatcat:tdxshvv7uzewrmwigprz44dwdm

A glance at DNA microarray technology and applications

Amir Ata Saei, Yadollah Omidi
2011 BioImpacts  
This can be obtained with global/selected gene expression profiling.  ...  Studying the regulation patterns of genes in groups, using clustering and classification methods helps us understand different pathways in the cell, their functions, regulations and the way one component  ...  Acknowledgement Authors are grateful to the Ministry of Health, Care and Medical Education for the financial support.  ... 
doi:10.5681/bi.2011.011 pmid:23678411 pmcid:PMC3648957 fatcat:4ywgkf6i2zaxtg7z6lvzvhfjn4

An Examination of Machine Learning Algorithms for Missing Values Imputation

In gene expression studies missing values have been a common problem. It has an important consequence on the explanation of the final data.  ...  Our research paper presents a review of missing values imputation approaches. It represents the research and imputation of missing values in gene expression data.  ...  ACKNOWLEDGEMENTS We would like to thank Universiti Malaysia Pahang for supporting this work under the RDU Grant, Grant number: RDU180344 and RDU190113..  ... 
doi:10.35940/ijitee.l1081.10812s219 fatcat:ixexhti6jvcjbnkqj2jmrncqge

Transcriptomic data analysis and differential gene expression of antioxidant pathways in king penguin juveniles ( Aptenodytes patagonicus ) before and after acclimatization to marine life

Benjamin Rey, Cyril Dégletagne, Claude Duchamp
2016 Data in Brief  
method for the analysis of heterologous hybridization on microarrays" (Dégletagne et al., 2010) [1].  ...  For better clarity, these differentially expressed genes were clustered into six functional groups according to their role in controlling redox homeostasis.  ...  Supporting information Transparency data associated with this article can be found in the online version at http://dx.doi. org/10.1016/j.dib.2016.09.021.  ... 
doi:10.1016/j.dib.2016.09.021 pmid:27752524 pmcid:PMC5061121 fatcat:nxzkgj3tf5fj3m7hnqnqmhe62e

The Microarray Explorer tool for data mining of cDNA microarrays: application for the mammary gland

P. F. Lemkin
2000 Nucleic Acids Research  
With this program it is possible to (i) analyze the expression of individual genes, (ii) analyze the expression of gene families and clusters, (iii) compare expression patterns and (iv) directly access  ...  A key feature is the clone data filter for constraining a working set of clones to those passing a variety of user-specified logical and statistical tests.  ...  There will always be a need to apply new methods for analyzing data. Ermolaeva et al.  ... 
doi:10.1093/nar/28.22.4452 pmid:11071932 pmcid:PMC113879 fatcat:ugc6pew7znglvgzpjb7nv3yjty
« Previous Showing results 1 — 15 out of 249,487 results