Lluberes, Marie

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    A probabilistic approach to gene expression analysis
    (2017) Lluberes, Marie; Seguel, Jaime; College of Engineering; Vélez, Bienvenido; Cabrera Ríos, Mauricio; Colón, Omar; Department of Electrical and Computer Engineering; Santiago, Aidsa
    Technology development has considerably increased the collection and storage of biological data. Nevertheless, the challenge of transforming such data into information, prevails. Such transformation demands the involvement of several disciplines, gathered under the umbrella of Bioinformatics. One of those main challenges under Bioinformatics’s extensive research area is learning the connections that govern gene activity, or gene regulatory networks (GRN). This is a very large scale problem, both because of the amount of variables involved as per the amount of possible interactions among them. Because of this, one very effective, accepted approach to inferring these networks is the use of Boolean representations of GRN. This model takes inputs from the binary domain; therefore, gene expression –which is measured as real data– needs first to be binary quantized with the use of a threshold. But both GRN and gene expression precise mathematical models are unknown; hence, their modeling is based on conjectures, biased at times. As a consequence, different models render different results. We study the effect of the differences that some binary quantization methods have on the resulting binarized gene expression. We call this model uncertainty. Furthermore, the discretization of gene expression subjects the threshold to changes as well. The number of measurements for the study of a gene may be bound, as a result of budgetary constraints, for instance. Have more data become available, this impacts the gene’s expected behavior. We study the effect that these changes on discretization have on a gene’s binarization, under different methods. We call this discretization uncertainty. While these uncertainties may persist due to, as aforementioned, the lack of a precise model, a unified approach may contribute to mitigate their impact. We propose a multi-algorithmic approach, with aggregation rules and voting mechanisms on several methods to countereffect model uncertainty. Rather than relying on a particular number of measurements, we use the gene’s threshold expected behavior to choose its binarization through statistical analysis, considering threshold variations, on an attempt to countereffect discretization uncertainty. This unified approach of statistical analysis and aggregation rules is presented as a framework that allows a customized selection of the methods. Finally, in order to measure the impact of these changes, I propose a simple evaluation method for network binarization changes. The proposed method provides specific metrics for evaluation on each network state individually for the detection of troubled binarizations. Existing network inference methods do not provide information on the binarization of each gene, making difficult to discern if the differences are due to selected binarizaton methods or to the learning mechanism of the implementation.