A Crowdsourcing Approach to Developing and Assessing Prediction Algorithms for AML Prognosis

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Description

Acute Myeloid Leukemia (AML) is a fatal hematological cancer. The genetic abnormalities underlying AML are extremely heterogeneous among patients, making prognosis and treatment selection very difficult. While clinical proteomics data has the potential to improve prognosis accuracy, thus far, the

Acute Myeloid Leukemia (AML) is a fatal hematological cancer. The genetic abnormalities underlying AML are extremely heterogeneous among patients, making prognosis and treatment selection very difficult. While clinical proteomics data has the potential to improve prognosis accuracy, thus far, the quantitative means to do so have yet to be developed. Here we report the results and insights gained from the DREAM 9 Acute Myeloid Prediction Outcome Prediction Challenge (AML-OPC), a crowdsourcing effort designed to promote the development of quantitative methods for AML prognosis prediction. We identify the most accurate and robust models in predicting patient response to therapy, remission duration, and overall survival. We further investigate patient response to therapy, a clinically actionable prediction, and find that patients that are classified as resistant to therapy are harder to predict than responsive patients across the 31 models submitted to the challenge. The top two performing models, which held a high sensitivity to these patients, substantially utilized the proteomics data to make predictions. Using these models, we also identify which signaling proteins were useful in predicting patient therapeutic response.

Date Created
2016-06-28
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Hydrodynamics of Diamond-Shaped Gradient Nanopillar Arrays for Effective DNA Trans location Into Nano Channels

Description

Effective DNA translocation into nanochannels is critical for advancing genome mapping and future single-molecule DNA sequencing technologies. We present the design and hydrodynamic study of a diamond-shaped gradient pillar array connected to nanochannels for enhancing the success of DNA translocation

Effective DNA translocation into nanochannels is critical for advancing genome mapping and future single-molecule DNA sequencing technologies. We present the design and hydrodynamic study of a diamond-shaped gradient pillar array connected to nanochannels for enhancing the success of DNA translocation events. Single-molecule fluorescence imaging is utilized to interrogate the hydrodynamic interactions of the DNA with this unique structure, evaluate key DNA translocation parameters, including speed, extension, and translocation time, and provide a detailed mapping of the translocation events in nanopillar arrays coupled with 10 and 50 μm long channels. Our analysis reveals the important roles of diamond-shaped nanopillars in guiding DNA into as small as 30 nm channels with minimized clogging, stretching DNA to nearly 100% of their dyed contour length, inducing location-specific straddling of DNA at nanopillar interfaces, and modulating DNA speeds by pillar geometries. Importantly, all critical features down to 30 nm wide nanochannels are defined using standard photolithography and fabrication processes, a feat aligned with the requirement of high-volume, low-cost production.

Date Created
2015-02-01
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Gene regulatory networks: modeling, intervention and context

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Description
Biological systems are complex in many dimensions as endless transportation and communication networks all function simultaneously. Our ability to intervene within both healthy and diseased systems is tied directly to our ability to understand and model core functionality. The progress

Biological systems are complex in many dimensions as endless transportation and communication networks all function simultaneously. Our ability to intervene within both healthy and diseased systems is tied directly to our ability to understand and model core functionality. The progress in increasingly accurate and thorough high-throughput measurement technologies has provided a deluge of data from which we may attempt to infer a representation of the true genetic regulatory system. A gene regulatory network model, if accurate enough, may allow us to perform hypothesis testing in the form of computational experiments. Of great importance to modeling accuracy is the acknowledgment of biological contexts within the models -- i.e. recognizing the heterogeneous nature of the true biological system and the data it generates. This marriage of engineering, mathematics and computer science with systems biology creates a cycle of progress between computer simulation and lab experimentation, rapidly translating interventions and treatments for patients from the bench to the bedside. This dissertation will first discuss the landscape for modeling the biological system, explore the identification of targets for intervention in Boolean network models of biological interactions, and explore context specificity both in new graphical depictions of models embodying context-specific genomic regulation and in novel analysis approaches designed to reveal embedded contextual information. Overall, the dissertation will explore a spectrum of biological modeling with a goal towards therapeutic intervention, with both formal and informal notions of biological context, in such a way that will enable future work to have an even greater impact in terms of direct patient benefit on an individualized level.
Date Created
2013
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