Journal ArticleDOI
Noise-robust soft clustering of gene expression time-course data
TLDR
To overcome the limitations of hard clustering, this work applied soft clustering which offers several advantages for researchers, including more noise robust and a priori pre-filtering of genes can be avoided.Abstract:
Clustering is an important tool in microarray data analysis. This unsupervised learning technique is commonly used to reveal structures hidden in large gene expression data sets. The vast majority of clustering algorithms applied so far produce hard partitions of the data, i.e. each gene is assigned exactly to one cluster. Hard clustering is favourable if clusters are well separated. However, this is generally not the case for microarray time-course data, where gene clusters frequently overlap. Additionally, hard clustering algorithms are often highly sensitive to noise. To overcome the limitations of hard clustering, we applied soft clustering which offers several advantages for researchers. First, it generates accessible internal cluster structures, i.e. it indicates how well corresponding clusters represent genes. This can be used for the more targeted search for regulatory elements. Second, the overall relation between clusters, and thus a global clustering structure, can be defined. Additionally, soft clustering is more noise robust and a priori pre-filtering of genes can be avoided. This prevents the exclusion of biologically relevant genes from the data analysis. Soft clustering was implemented here using the fuzzy c-means algorithm. Procedures to find optimal clustering parameters were developed. A software package for soft clustering has been developed based on the open-source statistical language R. The package called Mfuzz is freely available.read more
Citations
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Dissertation
Spot the difference : microarray analysis of gene expression changes in Alzheimer's and Parkinson's Disease
TL;DR: Intensity-based analysis of dual-color gene expression data as an alternative to ratio- based analysis to enhance reproducibility and a meta-analysis of microarray-based gene expression studies in Alzheimer's disease are presented.
Journal ArticleDOI
Islet autoantibody seroconversion in type-1 diabetes is associated with metagenome-assembled genomes in infant gut microbiomes
Li Zhang,Karen R. Jonscher,Zuyuan Zhang,Yi Xiong,Ryan S. Mueller,Jacob E Friedman,Chongle Pan +6 more
TL;DR: In this paper , Gut metagenomes were de-novo-assembled in 887 at-risk children in the Environmental Determinants of Diabetes in the Young (TEDDY) project.
Journal ArticleDOI
Fuzzy clustering of time series gene expression data with cubic-spline
Yu Wang,Maia Angelova,Akhtar Ali +2 more
TL;DR: A cubic smoothing spline fitted for the time course ex- pression profile, by which noise can be filtered and then groups genes into clusters by applying fuzzy c-means clustering on the resulting splines (FCMS).
Journal ArticleDOI
Time-series proteomic study of the response of HK-2 cells to hyperglycemic, hypoxic diabetic-like milieu
Alberto Valdés,María Castro-Puyana,Coral García-Pastor,Francisco Javier Lucio-Cazaña,María Luisa Marina +4 more
TL;DR: The exposure of HK-2 cells to high glucose and hypoxia reproduces some of the effects of diabetes on PTC and is proposed to propose new mechanisms and targets to be considered in the management of DKD.
Posted ContentDOI
Deep sequencing analysis of the circadian transcriptome of the jewel wasp Nasonia vitripennis
Nathaniel J. Davies,Eran Tauber +1 more
TL;DR: Although there was little similarity between cycling genes in Drosophila and Nasonia, the functions fulfilled by cycling transcripts were similar in both species, underscoring the importance of studying the clock in non-model organisms.
References
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Journal ArticleDOI
Cluster analysis and display of genome-wide expression patterns
TL;DR: A system of cluster analysis for genome-wide expression data from DNA microarray hybridization is described that uses standard statistical algorithms to arrange genes according to similarity in pattern of gene expression, finding in the budding yeast Saccharomyces cerevisiae that clustering gene expression data groups together efficiently genes of known similar function.
Book
Pattern Recognition with Fuzzy Objective Function Algorithms
TL;DR: Books, as a source that may involve the facts, opinion, literature, religion, and many others are the great friends to join with, becomes what you need to get.
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