1,577 research outputs found
A visual analytics framework for cluster analysis of DNA microarray data
Prova tipográficaCluster analysis of DNA microarray data is an important but difficult task in knowledge discovery processes.
Many clustering methods are applied to analysis of data for gene expression, but none of them
is able to deal with an absolute way with the challenges that this technology raises. Due to this, many
applications have been developed for visually representing clustering algorithm results on DNA microarray
data, usually providing dendrogram and heat map visualizations. Most of these applications focus
only on the above visualizations, and do not offer further visualization components to the validate the
clustering methods or to validate one another. This paper proposes using a visual analytics framework
in cluster analysis of gene expression data. Additionally, it presents a new method for finding cluster
boundaries based on properties of metric spaces. Our approach presents a set of visualization components
able to interact with each other; namely, parallel coordinates, cluster boundary genes, 3D cluster
surfaces and DNA microarray visualizations as heat maps. Experimental results have shown that our
framework can be very useful in the process of more fully understanding DNA microarray data. The
software has been implemented in Java, and the framework is publicly available at http://www.
analiticavisual.com/jcastellanos/3DVisualCluster/3D-VisualCluster.This work has been partially funded by the Spanish Ministry of Science and Innovation, the Plan E from the Spanish Government, the European Union from the ERDF (TIN2009-14057-C03-02)
Machine Learning
Machine Learning can be defined in various ways related to a scientific domain concerned with the design and development of theoretical and implementation tools that allow building systems with some Human Like intelligent behavior. Machine learning addresses more specifically the ability to improve automatically through experience
A new framework for identifying combinatorial regulation of transcription factors: A case study of the yeast cell cycle
AbstractBy integrating heterogeneous functional genomic datasets, we have developed a new framework for detecting combinatorial control of gene expression, which includes estimating transcription factor activities using a singular value decomposition method and reducing high-dimensional input gene space by considering genomic properties of gene clusters. The prediction of cooperative gene regulation is accomplished by either Gaussian Graphical Models or Pairwise Mixed Graphical Models. The proposed framework was tested on yeast cell cycle datasets: (1) 54 known yeast cell cycle genes with 9 cell cycle regulators and (2) 676 putative yeast cell cycle genes with 9 cell cycle regulators. The new framework gave promising results on inferring TF–TF and TF-gene interactions. It also revealed several interesting mechanisms such as negatively correlated protein–protein interactions and low affinity protein–DNA interactions that may be important during the yeast cell cycle. The new framework may easily be extended to study other higher eukaryotes
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