Human trafficking is a transnational complex societal and economic issue. While human trafficking has been studied in a variety of contexts, including criminology, sociological, and clinical domains, to date there has been very little coverage in the operations research (OR) and analytics community. This paper highlights how operations research and analytics techniques can be used to address the growing issue of human trafficking. It is intended to give insight to operations research and analytics professionals into the unique concerns, problems, and challenges in human trafficking; the relevance of OR and analytics to key pillars of human trafficking including prevention, protection, and prosecution; and to discuss opportunities for OR and analytics to make a difference in the human trafficking domain. We maintain that a profound need exists to explore how operations research and analytics can be effectively leveraged to combat human trafficking, and set forth this call to action to inhibit its pervasiveness.

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Motivation: A major limitation in modeling protein interactions is the difficulty of assessing the over-fitting of the training set. Recently, an experimentally based approach that integrates crystallographic information of C2H2 zinc finger–DNA complexes with binding data from 11 mutants, 7 from EGR finger I, was used to define an improved interaction code (no optimization). Here, we present a novel mixed integer programming (MIP)-based method that transforms this type of data into an optimized code, demonstrating both the advantages of the mathematical formulation to minimize over- and under-fitting and the robustness of the underlying physical parameters mapped by the code.Results: Based on the structural models of feasible interaction networks for 35 mutants of EGR–DNA complexes, the MIP method minimizes the cumulative binding energy over all complexes for a general set of fundamental protein–DNA interactions. To guard against over-fitting, we use the scalability of the method to probe against the elimination of related interactions. From an initial set of 12 parameters (six hydrogen bonds, five desolvation penalties and a water factor), we proceed to eliminate five of them with only a marginal reduction of the correlation coefficient to 0.9983. Further reduction of parameters negatively impacts the performance of the code (under-fitting). Besides accurately predicting the change in binding affinity of validation sets, the code identifies possible context-dependent effects in the definition of the interaction networks. Yet, the approach of constraining predictions to within a pre-selected set of interactions limits the impact of these potential errors to related low-affinity complexes.Contact: ccamacho@pitt.edu; droleg@pitt.eduSupplementary information: Supplementary data are available at Bioinformatics online.

Using Machine Learning and Integer Optimization to Help Improve Employment Outcomes of Resettled Refugees

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