This paper focuses on the discovery of a computational design map of disparate heterogeneous outcomes from bioinformatics experiments in pig (porcine) studies to help identify key variables impacting the experiment outcomes. Specifically we aim to connect discoveries from disparate laboratory experimentation in the area of trauma, blood loss and blood clotting using data science methods in a collaborative ensemble setting. Trauma related grave injuries cause exsanguination and death, constituting up to 50% of deaths especially in the armed forces. Restricting blood loss in such scenarios usually requires the presence of first responders, which is not feasible in certain cases. Moreover, a traumatic event may lead to a cytokine storm, reflected in the cytokine variables. Hemostatic nanoparticles have been developed to tackle these kinds of situations of trauma and blood loss. This paper highlights a collaborative effort of using data science methods in evaluating the outcomes from a lab study to further understand the efficacy of the nanoparticles. An intravenous administration of hemostatic nanoparticles was executed in pigs that had to undergo hemorrhagic shock and blood loss and other immune response variables, cytokine response variables are measured. Thus, through various hemostatic nanoparticles used in the intervention, multiple data outcomes are produced and it becomes critical to understand which nanoparticles are critical and what variables are key to study further variations in the lab. We propose a collaborative data mining framework which combines the results from multiple data mining methods to discover impactful features. We used frequent patterns observed in the data from these experiments. We further validate the connections between these frequent rules by comparing the results with decision trees and feature ranking. Both the frequent patterns and the decision trees help us identify the critical variables that stand out in the lab studies and need further validation and follow up in future studies. The outcomes from the data mining methods help produce a computational design map of the experimental results. Our preliminary results from such a computational design map provided insights in determining which features can help in designing the most effective hemostatic nanoparticles.

This paper focuses on the analysis of time series representation of blood loss and cytokines in animals experiencing trauma to understand the temporal progression of factors affecting survivability of the animal. Trauma related grave injuries cause exsanguination and lead to death. 50% of deaths especially in the armed forces are due to trauma injuries. Restricting blood loss usually requires the presence of first responders, which is not feasible in certain cases. Hemostatic nanoparticles have been developed to tackle these kinds of situations to help achieve efficient blood coagulation. Hemostatic nanoparticles were administered into trauma induced porcine animals (pigs) to observe impact on the cytokine and blood loss experienced by them. In this paper we present temporal models to study the impact of the hemostatic nanoparticles and provide snapshots about the trend in cytokines and blood loss in the porcine data to study their progression over time. We utilized Piecewise Aggregate Approximation, Similarity based Merging and clustering to evaluate the impact of the different hemostatic nanoparticles administered. In some cases the fluctuations in the cytokines may be too small. So in addition we highlight situations where temporal modelling that produces a smoothed time series may not be useful as it may remove out the noise and miss the overall fluctuations resulting from the nanoparticles. Our results indicate certain nanoparticles stand out and lead to novel hypothesis formation.