Inferring gene regulatory networks from expression data by discovering fuzzy dependency relationships

For one to infer the structures of a gene regulatory network (GRN), it is important to identify, for each gene in the GRN, which other genes can affect its expression and how they can affect it. For this purpose, many algorithms have been developed to generate hypotheses about the presence or absence of interactions between genes. These algorithms, however, cannot be used to determine if a gene activates or inhibits another. To obtain such information to better infer GRN structures, we propose a fuzzy data mining technique here. By transforming quantitative expression values into linguistic terms, it defines a measure of fuzzy dependency among genes. Using such a measure, the technique is able to discover interesting fuzzy dependency relationships in noisy, high dimensional time series expression data so that it can not only determine if a gene is dependent on another but also if a gene is supposed to be activated or inhibited. In addition, the technique can also predict how a gene in an unseen sample (i.e., expression data that are not in the original database) would be affected by other genes in it and this makes statistical verification of the reliability of the discovered gene interactions easier. For evaluation, the proposed technique has been tested using real expression data and experimental results show that the use of fuzzy-logic based technique in gene expression data analysis can be quite effective.