f-Information Measures for Selection of Discriminative Genes from Microarray Data

2014 ◽  
pp. 131-153
Author(s):  
Pradipta Maji ◽  
Sushmita Paul
Keyword(s):  
Microarray Data ◽  
Information Measures ◽  
Selection Of

scholarly journals Transcriptional profiling in an MPNST-derived cell line and normal human Schwann cells

2004 ◽  
Vol 1 (2) ◽  
pp. 135-147 ◽  
Author(s):  
PHILIP R. LEE ◽  
JONATHAN E. COHEN ◽  
ELISABETTA A. TENDI ◽  
ROBERT FARRER ◽  
GEORGE H. DE VRIES ◽  
...  
Keyword(s):  
Cell Line ◽  
Schwann Cells ◽  
Western Blotting ◽  
Microarray Data ◽  
Cdna Microarrays ◽  
Type I ◽  
Nerve Sheath Tumor ◽  
Wide Range ◽  
Normal Human ◽  
Selection Of

cDNA microarrays were utilized to identify abnormally expressed genes in a malignant peripheral nerve sheath tumor (MPNST)-derived cell line, T265, by comparing the mRNA abundance profiles with that of normal human Schwann cells (nhSCs). The findings characterize the molecular phenotype of this important cell-line model of MPNSTs, and elucidate the contribution of Schwann cells in MPNSTs. In total, 4608 cDNA sequences were screened and hybridizations replicated on custom cDNA microarrays. In order to verify the microarray data, a large selection of differentially expressed mRNA transcripts were subjected to semi-quantitative reverse transcription PCR (LightCycler). Western blotting was performed to investigate a selection of genes and signal transduction pathways, as a further validation of the microarray data. The data generated from multiple microarray screens, semi-quantitative RT–PCR and Western blotting are in broad agreement. This study represents a comprehensive gene-expression analysis of an MPNST-derived cell line and the first comprehensive global mRNA profile of nhSCs in culture. This study has identified ∼900 genes that are expressed abnormally in the T265 cell line and detected many genes not previously reported to be expressed in nhSCs. The results provide crucial information on the T265 cells that is essential for investigation using this cell line in experimental studies in neurofibromatosis type I (NF1), and important information on normal human Schwann cells that is applicable to a wide range of studies on Schwann cells in cell culture.

scholarly journals MicroArray Data Simulator For Improved Selection of Differentially Expressed Genes

2003 ◽  
Vol 2 (4) ◽  
pp. 383-391 ◽  
Author(s):  
Sunil Singhal ◽  
Chris G. Kyvernitis ◽  
Steven W. Johnson ◽  
Larry R. Kaiser ◽  
Michael N. Liebman ◽  
...  
Keyword(s):  
Differentially Expressed Genes ◽  
Microarray Data ◽  
Differentially Expressed ◽  
Selection Of

scholarly journals Approach to The Selection of Significant Features in Solving Biomedical Problems of Binary Classification of Microarray Data

2020 ◽  
Vol 15 (1) ◽  
pp. 4-19 ◽  
Author(s):  
I.Y. Boyko ◽  
D.S. Anisimov ◽  
L.L. Smolyakova ◽  
M.A. Ryazanov
Keyword(s):  
Feature Selection ◽  
Microarray Data ◽  
Binary Classification ◽  
Biological Information ◽  
Features Selection ◽  
Data Set ◽  
Early Diagnosis Of Cancer ◽  
Latent Structures ◽  
Classification Quality ◽  
Selection Of

In modern biomedical research aimed at finding methods for early diagnosis of cancer, microarrays containing certain biological information about patients are used. Based on these data, patients are assigned to one of two classes, corresponding to the presence and absence of some diagnosis. When solving this problem, one of the steps that have a decisive influence on the quality of classification is the significant features selection. This paper proposes a criterion for the selection of significant features, based on the ledge-coefficient of correlation. The ledge-coefficient was previously used to estimate the degree of interrelation of numerical and binary features. For two sets of microarray data, comparative examples of their binary classification are presented using three feature selection algorithms, three dimensionality reduction methods, six classification models. The use of the ledge-criterion for feature selection made it possible to obtain a classification quality comparable to the results of using common methods of feature selection, such as t-test and U-test. For the data set of the peptide microarrays considered in the paper, the effectiveness of applying the projection method to latent structures had previously been identified. The use of this method in combination with the significant features’ selection using the ledge-criterion made it possible to obtain a higher classification quality measure.

scholarly journals GPseudoRank: a permutation sampler for single cell orderings

2017 ◽  
Author(s):  
Magdalena E Strauß ◽  
John E Reid ◽  
Lorenz Wernisch
Keyword(s):  
Single Cell ◽  
Microarray Data ◽  
Network Inference ◽  
Biological Process ◽  
Supplementary Information ◽  
Established Method ◽  
Data Set ◽  
Point Estimates ◽  
Biological Development ◽  
Selection Of

AbstractMotivationA number of pseudotime methods have provided point estimates of the ordering of cells for scRNA-seq data. A still limited number of methods also model the uncertainty of the pseudotime estimate. However, there is still a need for a method to sample from complicated and multi-modal distributions of orders, and to estimate changes in the amount of the uncertainty of the order during the course of a biological development, as this can support the selection of suitable cells for the clustering of genes or for network inference.ResultsIn an application to a microarray data set our proposed method, GPseudoRank, identifies two modes of the distribution, each of them corresponding to point estimates of orders obtained by a different established method. In an application to scRNA-seq data we demonstrate the potential of GPseudoRank to identify phases of lower and higher pseudotime uncertainty during a biological process. GPseudoRank also correctly identifies cells precocious in their antiviral response.Availability and implementationOur method is available on github: https://github.com/magStra/GPseudoRank.Contactmagdalena.strauss@mrc-bsu.cam.ac.ukSupplementary informationSupplementary materials are available.

scholarly journals On the Feature Selection of Microarray Data for Cancer Detection based on Random Forest Classifier

2020 ◽  
Vol 12 (3) ◽  
Author(s):  
Tita Nurul Nuklianggraita ◽  
Adiwijaya Adiwijaya ◽  
Annisa Aditsania
Keyword(s):  
Random Forest ◽  
Cancer Detection ◽  
Microarray Data ◽  
Random Forest Classifier ◽  
World Health ◽  
Selection Operator ◽  
Health Organization ◽  
Microarray Technique ◽  
Selection Of ◽  
Better Than

Cancer is a disease that can affect all organs of humans. Based on data from the World Health Organization (WHO) fact sheet in 2018, cancer deaths have reached 9.6 million. One known way to detect cancer that is with Microarray Technique, but the microarray data have large dimensions due to the number of features that are very much compared to the number of samples. Therefore, dimension reduction should be made to produce optimum accuracy. In this paper, we compare Minimum Redundancy Maximum Relevance (MRMR) and Least Absolute Shrinkage and Selection Operator (LASSO) to reduce dimension of microarray data. Moreover, by using Random Forest (RF) Classifier, the performance of classification (cancer detection) is compared. Based on simulation, it can be concluded that LASSO is better than MRMR because it can produce an evaluation of 100% in lung and ovarian cancer, 92% colon cancer, 93% prostate tumor and 83% central nervous system.

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