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Unsupervised Clustering of Multivariate Time Series Microarray Experiments based on Incremental Non-Gaussian Analysis

Kam Swee Ng, Hyung-Jeong Yang, Soo-Hyung Kim, Sun-Hee Kim, Nguyen Thi Ngoc Anh
2012 International Journal of Contents  
In this paper, a non-Gaussian weight matrix obtained from an incremental model is proposed to extract useful features of multivariate time series microarrays.  ...  However, the unique nature of microarray data is usually in the form of large matrices of expression genes with high dimensions.  ...  Incremental Non-Gaussian Analysis for Microarrays Incremental non-Gaussian analysis for microarray expression, which integrates an incremental model with the concept of ICA to update the non-Gaussian weight  ... 
doi:10.5392/ijoc.2012.8.1.023 fatcat:gyevxuf2g5gxnizu63rx5oosyy

Granular Transcriptomic Signatures Derived from Independent Component Analysis of Bulk Nervous Tissue for Studying Labile Brain Physiologies [article]

Zeid M Rusan, Michael P Cary, Roland J Bainton
2020 bioRxiv   pre-print
Here we use the unsupervised machine learning method independent component analysis (ICA) on majority fresh- frozen, bulk tissue microarrays to define biologically pertinent gene expression signatures  ...  We optimize the gene expression signature definitions partly through repeated application of a stochastic ICA algorithm to a compendium of 3,346 microarrays from 221 experiments provided by the Drosophila  ...  Additional information Funding and conflicts of interest  ... 
doi:10.1101/2020.01.01.892281 fatcat:zbl55a7l6rhmhjjyph3d3lqaee

Mixture-model based estimation of gene expression variance from public database improves identification of differentially expressed genes in small sized microarray data

M. Kim, S. B. Cho, J. H. Kim
2009 Bioinformatics  
To model various experimental conditions of a public microarray database, we applied Gaussian mixture model and extracted bi-or tri-modal distributions of gene expression.  ...  The results may be a challenging evidence for usage of public microarray databases in microarray data analysis.  ...  R&D Project, Ministry of Health, Welfare and Family Affairs, Republic of Korea (0405-BC02-0604-0004).  ... 
doi:10.1093/bioinformatics/btp685 pmid:20015947 pmcid:PMC2820675 fatcat:p67yeivtabaazfdxsoznrozkue

The Exploration Machine – A Novel Method for Data Visualization [chapter]

Axel Wismüller
2009 Lecture Notes in Computer Science  
Although simple and computationally efficient, XOM enjoys a surprising flexibility to simultaneously contribute to several different domains of advanced machine learning, scientific data analysis, and  ...  visualization, such as structurepreserving dimensionality reduction and data clustering.  ...  After explaining the XOM algorithm, we present applications to the analysis of multidimensional biomedical data from different real-world domains: whole-genome microarray gene expression experiments and  ... 
doi:10.1007/978-3-642-02397-2_39 fatcat:rm35j32izvdadevc7pzqmf2zxq

A simple method for statistical analysis of intensity differences in microarray-derived gene expression data

A Kamb, M Ramaswami
2001 BMC Biotechnology  
Microarray experiments offer a potent solution to the problem of making and comparing large numbers of gene expression measurements either in different cell types or in the same cell type under different  ...  The method also provides a platform-independent view of important statistical properties of microarray data.  ...  These variances can be applied to non-control data to estimate p values for specific changes in gene expression.  ... 
pmid:11690545 pmcid:PMC59472 fatcat:dhphve7oqra4pmg56kr7uq6oti

Two-way incremental seriation in the temporal domain with three-dimensional visualization: Making sense of evolving high-dimensional datasets

Peter Wittek
2013 Computational Statistics & Data Analysis  
Combined with the convolution kernel, a three-dimensional visualization of dynamics is demonstrated on two data sets, a news collection and a set of microarray measurements.  ...  To achieve a meaningful visualization of high-dimensional data, a compactly supported convolution kernel is introduced, which is similar to filter kernels used in image reconstruction and geostatistics  ...  An application to microarray data Microarrays are commonly used in massive gene expression data analysis. They measure the expression levels of large number of genes simultaneously.  ... 
doi:10.1016/j.csda.2013.03.026 fatcat:pc3pupnl4vgydmtzvo7o4gf2e4

The fuzzy gene filter: A classifier performance assesment [article]

Meir Perez, Tshilidzi Marwala
2011 arXiv   pre-print
The Fuzzy Gene Filter (FGF) is an optimised Fuzzy Inference System designed to rank genes in order of differential expression, based on expression data generated in a microarray experiment.  ...  The FGF is compared to three of the most common gene ranking algorithms: t-test, Wilcoxon test and ROC curve analysis.  ...  In the context of microarray data, the expression values of a gene are ranked in ascending order [16] .  ... 
arXiv:1108.4545v1 fatcat:m2hsqefapvbu7dhmtt7niprjsa

A Comparative Study of Clustering and Biclustering of Microarray Data[

Haifa Ben Saber, Mourad Elloumi
2014 International Journal of Computer Science & Information Technology (IJCSIT)  
In this paper, we make a new survey on clustering and biclustering of gene expression data, also called microarray data.  ...  That is why, it is of utmost importance to make a simultaneous clustering of genes and conditions to identify clusters of genes that are coexpressed under clusters of conditions.  ...  Biclustering of microarray data can be helpful to discover co expression of genes and, hence, uncover genomic knowledge such as gene networks or gene interactions.  ... 
doi:10.5121/ijcsit.2014.6607 fatcat:t7wpao3o6bdyjcdkpkjtntkcri

Gene ranking using bootstrapped P-values

S. N. Mukherjee, S. J. Roberts, P. Sykacek, S. J. Gurr
2003 SIGKDD Explorations  
For example, a number of genes from one of the datasets, whose differential expression was subsequently confirmed by a more reliable biochemical analysis, are found to be ranked higher by the bootstrapped  ...  Some of the properties of microarray datasets make the task of finding these genes a challenging one.  ...  Acknowledgements SNM gratefully acknowledges the support of the Biotechnology and Biological Sciences Research Council (BBSRC); thanks also to Dr. Sayan Mukherjee.  ... 
doi:10.1145/980972.980976 fatcat:x32vbljlvfhfnmlap3quoauihi

A Simple Approach to Ranking Differentially Expressed Gene Expression Time Courses through Gaussian Process Regression

Alfredo A Kalaitzis, Neil D Lawrence
2011 BMC Bioinformatics  
Two basic forms of analysis recur for data of this type: removing inactive (quiet) genes from the study and determining which genes are differentially expressed.  ...  Therefore, we believe Gaussian processes should be a standard tool in the analysis of gene expression time series.  ...  Acknowledgements The authors would like to thank Diego di Bernardo for his useful feedback on the experimental data.  ... 
doi:10.1186/1471-2105-12-180 pmid:21599902 pmcid:PMC3116489 fatcat:zedt2mimcbbhlg4kpamyriwuee

Dealing with missing values in large-scale studies: microarray data imputation and beyond

T. Aittokallio
2009 Briefings in Bioinformatics  
The imputation methods are first reviewed in the context of gene expression microarray data, since most of the methods have been developed for estimating gene expression levels; then, we turn to other  ...  After nearly a decade since the publication of the first missing value imputation methods for gene expression microarray data, new imputation approaches are still being developed at an increasing rate.  ...  Cluster analysis is typically one of the first downstream analyses conducted for a gene expression microarray data set since it provides, for instance, hypotheses about functional roles of the genes for  ... 
doi:10.1093/bib/bbp059 pmid:19965979 fatcat:bnj6czor2rbhxdzc5noaodxqcm

Computational trans-omics approach characterised methylomic and transcriptomic involvements and identified novel therapeutic targets for chemoresistance in gastrointestinal cancer stem cells

Masamitsu Konno, Hidetoshi Matsui, Jun Koseki, Ayumu Asai, Yoshihiro Kano, Koichi Kawamoto, Naohiro Nishida, Daisuke Sakai, Toshihiro Kudo, Taroh Satoh, Yuichiro Doki, Masaki Mori (+1 others)
2018 Scientific Reports  
We expressed DNA methylation profiles as smooth functions using Gaussian functions to extract appropriate information from the data.  ...  The present study showed that 5mC methylation levels are inversely correlated with gene expression in a chemotherapy-resistant stem cell model of gastrointestinal cancer.  ...  Acknowledgements We thank the members of our laboratories for their fruitful discussions.  ... 
doi:10.1038/s41598-018-19284-3 pmid:29343747 pmcid:PMC5772492 fatcat:pqdfy5y63fhrvilghrrs2dujuu

GMMchi: Gene Expression Clustering Using Gaussian Mixture Modeling [article]

Ta-Chun Liu, Peter N. Kalugin, Jennifer L. Wilding, Walter F Bodmer
2022 bioRxiv   pre-print
from bulk gene expression data.  ...  We hypothesize that such mutations are likely to cluster with specific dichotomous shifts in the expression of the genes they most closely control, and propose GMMchi, a Python package that leverages Gaussian  ...  Non-normally Distributed Tail Problem The challenges faced with directly applying GMM to expression data analysis and the identification of bimodal distributions lie within the nature of gene expression  ... 
doi:10.1101/2022.02.14.480329 fatcat:mlbwamjhfjb6blyvh5nfxxvsda

Detecting Differential Gene Expression Using Affymetrix Microarrays

Todd Allen
2013 The Mathematica Journal  
Detecting Differential Gene Expression Using Affymetrix Microarrays 9 The majority of the data has an approximately Gaussian appearance (on a log scale), while still harboring extreme values on the rightward  ...  file 5 in [2] describes the 1,944 differentially expressed genes and 3,426 non-differentially expressed genes that were purposefully "spiked-in" to the microarray experiment.  ...  His interest in computational biology using Mathematica took shape during his postdoctoral research years at the University of Maryland, where he developed a custom cDNA microarray chip to study gene expression  ... 
doi:10.3888/tmj.15-11 fatcat:z7f2mmp6uncbleikhq3u6mqrxe

f-divergence cutoff index to simultaneously identify differential expression in the integrated transcriptome and proteome

Shaojun Tang, Martin Hemberg, Ertugrul Cansizoglu, Stephane Belin, Kenneth Kosik, Gabriel Kreiman, Hanno Steen, Judith Steen
2016 Nucleic Acids Research  
There are currently no tools available to identify differentially expressed genes (DEGs) across different 'omics' data types or multi-dimensional data including time courses.  ...  We show that fCI can be used across multiple diverse sets of data and can unambiguously find genes that show functional modulation, developmental changes or misregulation.  ...  ACKNOWLEDGEMENTS We are grateful to Alberto Riva, Sebastian Berger and Jan Muntel for critically reading of the manuscript and for fruitful discussions.  ... 
doi:10.1093/nar/gkw157 pmid:26980280 pmcid:PMC4889934 fatcat:n5urgpeyvrhrdie7je24l4cxqq
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