Heart failure is a complex, complicated disease which is not yet fully understood. We used the Module Map algorithm to uncover groups of genes that have a similar pattern of expression under various conditions of heart stress. These groups of genes are called modules and may serve as computational predictions of biological pathways for the various clinical situations. The Module Map algorithm allows a large scale analysis of genes expressed. We applied this algorithm to 700 different mouse experiments downloaded from the Gene Expression Omnibus (GEO) database, which identified 884 modules. The analysis reconstructed partially known principles that play a role in governing the response of heart to stress, thus demonstrating the strength of the method. We have shown a role of genes related to the immune system in conditions of heart remodeling and failure. We have also shown changes in the expression of genes involved with energy metabolism and changes in the expression of contractile proteins of the heart following MI. When focusing on another module we noted a new correlation between genes related to osteogenesis and heart failure, including Runx2 and Ahsg, whose role in heart failure was unknown so far. Despite a lack of prior biological knowledge, the Module Map algorithm has reconstructed known pathways, which demonstrates the strength of this new method for analyzing gene profiles related to clinical phenomenon. The method and the analysis presented are a new avenue to uncover the correlation of clinical conditions to the molecular level.