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Defining an informativeness metric for clustering gene expression data

Jessica C. Mar, Christine A. Wells, John Quackenbush
2011 Computer applications in the biosciences : CABIOS  
ACKNOWLEDGEMENTS We acknowledge the assistance of Drs Jiyuan An, Alistair Chalk and Nick Matigian, The National Centre for Adult Stem Cell Research, Griffith University, who provided valuable help in assembling  ...  the experimental dataset for this article.  ...  To best use phenotypic class information to our advantage, we define our informativeness metric based on simple ANOVA statistics that come from comparing gene expression profiles between phenotypic groups  ... 
doi:10.1093/bioinformatics/btr074 pmid:21330289 pmcid:PMC3072547 fatcat:lnz5ddansrefhh65urixo5zmgu

Data Pre-processing for More Effective Gene Clustering

Jingyu Hou, Yi-Ping Phoebe Chen
2009 2009 International Joint Conference on Computational Sciences and Optimization  
, the pre-processed data sets make the clustering results stable across clustering algorithms with different similarity metrics, the important information of genes and features is kept, and the clustering  ...  This work proposes an innovative data pre-processing approach to identify noise data in the data sets and eliminate or reduce the impact of the noise data on gene clustering, With the proposed algorithm  ...  Conclusions This paper proposed an innovative data preprocessing algorithm for improving gene clustering results.  ... 
doi:10.1109/cso.2009.328 dblp:conf/cso/HouC09 fatcat:rdz6epuynvcnvc6aw6z7san2ma

Smoothing Gene Expression Using Biological Networks

Yue Fan, Mark Kon, Shinuk Kim, Charles DeLisi
2010 2010 Ninth International Conference on Machine Learning and Applications  
In this paper, we describe three novel machine learning algorithms for regularizing (smoothing) microarray expression values defined on gene sets with known prior network or metric structures, and which  ...  Gene expression (microarray) data have been used widely in bioinformatics.  ...  Recently, methods for denoising gene expression data have drawn attention from researchers.  ... 
doi:10.1109/icmla.2010.85 dblp:conf/icmla/FanKKD10 fatcat:ceqntvvqqrg3lb5peb4iv7le2m

Spectral Preprocessing for Clustering Time-Series Gene Expressions

Wentao Zhao, Erchin Serpedin, Edward R. Dougherty
2009 EURASIP Journal on Bioinformatics and Systems Biology  
Based on gene expression profiles, genes can be partitioned into clusters, which might be associated with biological processes or functions, for example, cell cycle, circadian rhythm, and so forth.  ...  This paper proposes a novel clustering preprocessing strategy which combines clustering with spectral estimation techniques so that the time information present in time series gene expressions is fully  ...  The clustering performance is represented by an information theoretic quantity, that is, mutual information, which is defined between the obtained partition of all measured genes and the set of 104 genes  ... 
doi:10.1155/2009/713248 pmid:19381338 pmcid:PMC3171439 fatcat:6gqhsmxy4ff65iwww4cspafwv4

A rank-based marker selection method for high throughput scRNA-seq data

Alexander H. S. Vargo, Anna C. Gilbert
2020 BMC Bioinformatics  
RankCorr proceeds by ranking the mRNA counts data before linearly separating the ranked data using a small number of genes.  ...  Results We introduce RankCorr, a fast method with strong mathematical underpinnings that performs multi-class marker selection in an informed manner.  ...  Thanks also to the members of the Michigan Center for Single-Cell Genomic Data Analytics for their advice and feedback.  ... 
doi:10.1186/s12859-020-03641-z pmid:33097004 fatcat:3vz5b5erqbeqbng4mxc22byntm

Exploiting inter-gene information for microarray data integration

Kuan-ming Lin, Jaewoo Kang
2007 Proceedings of the 2007 ACM symposium on Applied computing - SAC '07  
Microarray data integration is an important yet challenging problem.  ...  Motivated by this observation, we propose in this paper a formal data model for microarray integration using inter-gene information and effective filtering, which generalizes the previous two frameworks  ...  Once the metrics are defined, we can associate each Xm with an n-by-n interrelation matrix Km by (Km)ij = dm(Xmi, Xmj ). We demonstrate this data model via the cube illustration shown in Fig. 1a .  ... 
doi:10.1145/1244002.1244032 dblp:conf/sac/LinK07 fatcat:f6dd32ppxfbh5ejyfexwpmgsle

Tumor classification using phylogenetic methods on expression data

Richard Desper, Javed Khan, Alejandro A. Schäffer
2004 Journal of Theoretical Biology  
problem of accurately classifying an unknown tumor, given expression data from the unknown tumor and from a learning set.  ...  To solve the class discovery problem, we impose a metric on a set of tumors as a function of their gene expression levels, and impose a tree structure on this metric, using standard tree fitting methods  ...  Acknowledgements Our thanks go to John Powell for his assistance installing CLUSTER.  ... 
doi:10.1016/j.jtbi.2004.02.021 pmid:15178197 fatcat:7urg6jw34bfopkopuu2bwgybtq

Metric for Measuring the Effectiveness of Clustering of DNA Microarray Expression

Raja Loganantharaj, Satish Cheepala, John Clifford
2006 BMC Bioinformatics  
The differentially expressed genes are filtered first and then clustered based on the expression profiles of the genes.  ...  The metric can also be used in other domains that use two different parametric spaces; one for clustering and the other one for measuring the effectiveness.  ...  Acknowledgements The first author thanks the partial support from the Louisiana Governor's Information Technology Initiatives (GITI) for this work.  ... 
doi:10.1186/1471-2105-7-s2-s5 pmid:17118148 pmcid:PMC1683560 fatcat:hrg6yolenffe3bharcpxt46iqy

Analysis of Time-Series Gene Expression Data: Methods, Challenges, and Opportunities

I.P. Androulakis, E. Yang, R.R. Almon
2007 Annual Review of Biomedical Engineering  
Gene arrays measuring the level of mRNA expression of thousands of genes simultaneously provide a method of high-throughput data collection necessary for obtaining the scope of data required for understanding  ...  Unraveling the coherent complex structures of transcriptional dynamics is the goal of a large family of computational methods aiming at upgrading the information content of time-course gene expression  ...  I.P.A. and E.Y. acknowledge support from the National Science Foundation under an NSF-BES 0519563 Metabolic Engineering Grant and the Environmental Protection Agency under grant EPA-GAD R 832721-101.  ... 
doi:10.1146/annurev.bioeng.9.060906.151904 pmid:17341157 pmcid:PMC4181347 fatcat:mrmy3ydbyncpnpqintxh4vw7xu

Incorporating Biological Domain Knowledge into Cluster Validity Assessment [chapter]

Nadia Bolshakova, Francisco Azuaje, Pádraig Cunningham
2006 Lecture Notes in Computer Science  
gene expression data analysis.  ...  This paper presents an approach for assessing cluster validity based on similarity knowledge extracted from the Gene Ontology (GO) and databases annotated to the GO.  ...  A great variety of clustering algorithms have been developed for gene expression data.  ... 
doi:10.1007/11732242_2 fatcat:twnhefjlifdrdjvjgwua6pcoki


2006 International journal on artificial intelligence tools  
This metric allows the formation of data clusters that maximize the mutual information for transactions of the same cluster and to minimize it between different clusters.  ...  Therefore, the fuzzy association rules are extracted locally on a per cluster basis. The paper focuses on the application of the techniques for mining the gene expression data.  ...  the analysis of Gene Expression Data".  ... 
doi:10.1142/s0218213006002643 fatcat:4i64ac26gnh23ntmgipynk2jke

genesorteR: Feature Ranking in Clustered Single Cell Data [article]

Mahmoud M Ibrahim, Rafael Kramann
2019 bioRxiv   pre-print
Detecting a gene by model-based differential expression analysis does not necessarily satisfy those two conditions and is typically computationally expensive for large cell numbers.  ...  We present genesorteR, an R package that ranks features in single cell data in a manner consistent with the expected definition of marker genes. genesorteR is orders of magnitude faster than current implementations  ...  Costa for critical reading of the manuscript. Funding.  ... 
doi:10.1101/676379 fatcat:bu5y7bcq4vfxfjrr4pfrbastwu

Meta-Analysis of cortical inhibitory interneurons markers landscape and their performances in scRNA-seq studies [article]

Lorenzo Martini, Roberta Bardini, Stefano Di Carlo
2021 bioRxiv   pre-print
We defined metrics based on the relations between unsupervised cluster results and the marker expression.  ...  Specifically, we calculated the specificity, the fraction of cells expressing, and some metrics derived from decision tree analysis like entropy gain and impurity reduction.  ...  Metrics definition To evaluate the performance of the marker genes, we first need to define a set of metrics designed for this task.  ... 
doi:10.1101/2021.11.03.467049 fatcat:lc7kd7rdkncrtgyppneuzkfpuu

Trustworthiness and metrics in visualizing similarity of gene expression

Samuel Kaski, Janne Nikkilä, Merja Oja, Jarkko Venna, Petri Törönen, Eero Castrén
2003 BMC Bioinformatics  
The conjecture from the methodological results is that the self-organizing map can be recommended to complement the usual hierarchical clustering for visualizing and exploring gene expression data.  ...  The trustworthiness of hierarchical clustering, multidimensional scaling, and the self-organizing map were compared in visualizing similarity relationships among gene expression profiles.  ...  Leo Lahti for help with some of the simulations.  ... 
doi:10.1186/1471-2105-4-48 pmid:14552657 pmcid:PMC272927 fatcat:cz3epqoicnfhndb2q4kdyhacka

CluGene: A Bioinformatics Framework for the Identification of Co-Localized, Co-Expressed and Co-Regulated Genes Aimed at the Investigation of Transcriptional Regulatory Networks from High-Throughput Expression Data

Tania Dottorini, Pietro Palladino, Nicola Senin, Tania Persampieri, Roberta Spaccapelo, Andrea Crisanti, Christian Schönbach
2013 PLoS ONE  
Genome high-throughput technologies produce a huge amount of information pertaining gene expression and regulation; however, the complexity of the available data is often overwhelming and tools are needed  ...  for co-regulation; finally, co-regulation seems more frequent for genes mapped to proximal chromosome regions.  ...  The authors thank the anonymous reviewers for the constructive comments. Author Contributions  ... 
doi:10.1371/journal.pone.0066196 pmid:23823315 pmcid:PMC3688840 fatcat:nzkq4ja37zcchkmkz6udn5ihdm
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