PROJECT TITLE :

Using Formal Concept Analysis to Identify Negative Correlations in Gene Expression Data

ABSTRACT:

Recently, several biological studies reported that 2 groups of genes tend to indicate negatively correlated or opposite expression tendency in many biological processes or pathways. The negative correlation between genes might imply an necessary biological mechanism. During this study, we have a tendency to proposed a FCA-primarily based negative correlation algorithm (NCFCA) that can effectively establish opposite expression tendency between two gene teams in gene expression data. Once applying it to expression information of cell cycle-regulated genes in yeast, we found that six minichromosome maintenance family genes showed the alternative changing tendency with eight core histone family genes. Furthermore, we confirmed that the negative correlation expression pattern between these 2 families could be conserved in the cell cycle. Finally, we have a tendency to discussed the explanations underlying the negative correlation of six minichromosome maintenance (MCM) family genes with eight core histone family genes. Our results revealed that negative correlation is a vital and potential mechanism that maintains the balance of biological systems by repressing some genes while inducing others. It can thus give new understanding of gene expression and regulation, the causes of diseases, etc.


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