scholarly journals Systematic Management and Analysis of Yeast Gene Expression Data

2000 ◽  
Vol 10 (4) ◽  
pp. 431-445 ◽  
Author(s):  
J. Aach
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Expression Data ◽  
Yeast Gene

scholarly journals A fuzzy logic approach to analyzing gene expression data

2000 ◽  
Vol 3 (1) ◽  
pp. 9-15 ◽  
Author(s):  
PETER J. WOOLF ◽  
YIXIN WANG
Keyword(s):  
Gene Expression ◽  
Fuzzy Logic ◽  
Gene Expression Data ◽  
Expression Data ◽  
Heuristic Rules ◽  
Yeast Gene ◽  
Data Set ◽  
Fuzzy Logic Approach ◽  
Logic Approach ◽  
Novel Algorithm

Woolf, Peter J., and Yixin Wang. A fuzzy logic approach to analyzing gene expression data. Physiol Genomics 3: 9–15, 2000.—We have developed a novel algorithm for analyzing gene expression data. This algorithm uses fuzzy logic to transform expression values into qualitative descriptors that can be evaluated by using a set of heuristic rules. In our tests we designed a model to find triplets of activators, repressors, and targets in a yeast gene expression data set. For the conditions tested, the predictions made by the algorithm agree well with experimental data in the literature. The algorithm can also assist in determining the function of uncharacterized proteins and is able to detect a substantially larger number of transcription factors than could be found at random. This technology extends current techniques such as clustering in that it allows the user to generate a connected network of genes using only expression data.

scholarly journals A weighted Mutual Information Biclustering algorithm for gene expression data

2017 ◽  
Vol 14 (3) ◽  
pp. 643-660 ◽  
Author(s):  
Yidong Li ◽  
Wenhua Liu ◽  
Yankun Jia ◽  
Hairong Dong
Keyword(s):  
Gene Expression ◽  
Mutual Information ◽  
Gene Expression Data ◽  
High Dimensional ◽  
Expression Data ◽  
Clustering Methods ◽  
Yeast Gene ◽  
Huge Amount ◽  
Local Characteristics ◽  
Complex Linear

Microarrays are one of the latest breakthroughs in experimental molecular biology, which have already provided huge amount of high dimensional genetic data. Traditional clustering methods are difficult to deal with this high dimensional data, whose a subset of genes are co-regulated under a subset of conditions. Biclustering algorithms are introduced to discover local characteristics of gene expression data. In this paper, we present a novel biclustering algorithm, which calledWeighted Mutual Information Biclustering algorithm (WMIB) to discover this local characteristics of gene expression data. In our algorithm, we use the weighted mutual information as new similarity measure which can be simultaneously detect complex linear and nonlinear relationships between genes, and our algorithm proposes a new objective function to update weights of each bicluster, which can simultaneously select the conditions set of each bicluster using some rules.We have evaluated our algorithm on yeast gene expression data, the experimental results show that our algorithm can generate larger biclusters with lower mean square residues simultaneously.

Analysis of High Dimensionality Yeast Gene Expression Data Using Data Mining

2012 ◽  
Vol 197 ◽  
pp. 515-522
Author(s):  
Mazin Aouf ◽  
Liwan Liyanage
Keyword(s):  
Gene Expression ◽  
Data Mining ◽  
Hydrogen Peroxide ◽  
Transcription Factors ◽  
Gene Expression Data ◽  
Process Model ◽  
Expression Data ◽  
Yeast Gene ◽  
Promoter Regions ◽  
Biological Studies

Data Mining is the process of discovering interesting knowledge from large amounts of data stored either in databases, data warehouses, or other information repositories. From biological studies, the Yeast Proteome Database (YPD) is a model for the organization and presentation of genome-wide functional data. Accordingly, a yeast gene expression which is a unicellular DNA is selected which contains 6103 genes and the database combined with a number of related dataset to create a general dataset. DNA-binding transcriptional regulators interpret the genome’s regulatory code by binding to specific sequences to induce or repress gene expression. The gene products including RNA and protein are responsible for the development and functioning of all living membranes by 2 steps process, transcription and translation. Various transcription factors control gene transcription by binding to the promoter regions. Translation is the production of proteins from mRNA produced in transcription. In this study, out of the 169 transcription factors known to access yeast, we are considering those thought to be involved in the response of Hydrogen Peroxide (H2O2). They are 22 transcription factors. Each one is partitioned to 3 parts: TF with No H2O2, TF with Low H2O2 and TF with High H2O2. The aim of this paper was to enhance the effectiveness of the integration of hydrogen peroxide response data related to yeast gene expression data to obtain a protein response process model and to label a set of important genes related to this approach.

scholarly journals Proportionality: a valid alternative to correlation for relative data

2014 ◽  
Author(s):  
David Lovell ◽  
Vera Pawlowsky-Glahn ◽  
Juan José Egozcue ◽  
Samuel Marguerat ◽  
Jürg Bähler
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Molecular Mechanisms ◽  
Compositional Data ◽  
Life Sciences ◽  
Measurement Methods ◽  
Expression Data ◽  
Yeast Gene ◽  
Measure Of Association ◽  
Yeast Genes

In the life sciences, many measurement methods yield only the relative abundances of different components in a sample. With such relative---or compositional---data, differential expression needs careful interpretation, and correlation---a statistical workhorse for analyzing pairwise relationships---is an inappropriate measure of association. Using yeast gene expression data we show how correlation can be misleading and present proportionality as a valid alternative for relative data. We show how the strength of proportionality between two variables can be meaningfully and interpretably described by a new statistic φ which can be used instead of correlation as the basis of familiar analyses and visualization methods, including co-expression networks and clustered heatmaps. While the main aim of this study is to present proportionality as a means to analyse relative data, it also raises intriguing questions about the molecular mechanisms underlying the proportional regulation of a range of yeast genes.

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