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"Signal” alignments play critical roles in many clinical setting. This is the case of mass spectrometry (MS) data, an important component of many types of proteomic analysis. A central problem occurs when one needs to integrate (MS) data produced by different sources, e.g., different equipment and/or laboratories. In these cases, some form of "data integration” or "data fusion” may be necessary in order to discard some source-specific aspects and improve the ability to perform a classification task such as inferring the "disease classes” of patients. The need for new high-performance data alignments methods is therefore particularly important in these contexts. In this paper, we propose an approach based both on an information theory perspective, generally used in a feature construction problem, and the application of a mathematical programming task (i.e., the weighted bipartite matching problem). We present the results of a competitive analysis of our method against other approaches. The analysis was conducted on data from plasma/ethylenediaminetetraacetic acid of "control” and Alzheimer patients collected from three different hospitals. The results point to a significant performance advantage of our method with respect to the competing ones tested.