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Acute myeloid leukemia (AML) is an aggressive and lethal cancer of the blood, which leads to the death of over 11,000 patients in the United States each year. Research on identifying, characterizing, and treating AML is crucial in the fight against this deadly disease. Recent studies have examined the role of CLEC11A in cancer, including AML. However, there have been conflicting reports related to tumor progression and survival. Because survival is based on a variety of factors, including classification of the tumor, genetic risk factors, and demographics, it is imperative that we determine what role CLEC11A may have in cancer survival. Therefore, utilizing data from the Genomic Data Commons, we analyzed CLEC11A methylation in 108 AML patients compared to FAB classification, cytogenetic risk factors, age, race, and gender. Our results show statistically significant correlations between methylation of CLEC11A and FAB classification as well as poor genetic risk factors. However, no difference was observed in CLEC11A methylation when compared to demographic data. Our results, matched with a known biological function of CLEC11A in early hematopoiesis, indicate that CLEC11A may be an important marker for AML diagnosis and prognosis and provide relevant data in the ongoing search for novel therapeutics to improve AML survival. 

We propose a novel supervised learning approach for political ideology prediction (PIP) that is capable of predicting out-of-distribution inputs. This problem is motivated by the fact that manual data-labeling is expensive, while self-reported labels are often scarce and exhibit significant selection bias. We propose a novel statistical model that decomposes the document embeddings into a linear superposition of two vectors; a latent neutral context vector independent of ideology, and a latent position vector aligned with ideology. We train an end-to-end model that has intermediate contextual and positional vectors as outputs. At deployment time, our model predicts labels for input documents by exclusively leveraging the predicted positional vectors. On two benchmark datasets we show that our model is capable of outputting predictions even when trained with as little as 5% biased data, and is significantly more accurate than the state-of-the-art. Through crowd-sourcing we validate the neutrality of contextual vectors, and show that context filtering results in ideological concentration, allowing for prediction on out-of-distribution examples. 

Abstract We develop a broadly applicable class of coevolving latent space network with attractors (CLSNA) models, where nodes represent individual social actors assumed to lie in an unknown latent space, edges represent the presence of a specified interaction between actors, and attractors are added in the latent level to capture the notion of attractive and repulsive forces. We apply the CLSNA models to understand the dynamics of partisan polarization in US politics on social media, where we expect Republicans and Democrats to increasingly interact with their own party and disengage with the opposing party. Using longitudinal social networks from the social media platforms Twitter and Reddit, we quantify the relative contributions of positive (attractive) and negative (repulsive) forces among political elites and the public, respectively.