Getting Results: Extras

WNF14-7455Emily Grussing ’15 was in AP Calculus when she learned the news: a scientific paper she researched and co-authored had just been accepted for publication.

“It was the greatest thing. I didn’t expect to get published,” Ms. Grussing said. “It wasn’t something I even believed was possible.”

Ms. Grussing had spent the summer interning at Dartmouth College’s Geisel School of Medicine where she was charged with researching links between chemicals and cancers. To do so, Ms. Grussing learned coding basics, dove into online research, and analyzed and constructed scientific networks.

“I loved being in the environment of the lab,” Ms. Grussing said, adding, “I never had to do such self-learning in my life before.”

The resulting paper, which Ms. Grussing helped write, was accepted by the Pacific Symposium on Biocomputing, where it was presented on January 8. The paper will also be published on PubMed Central, an archive of scientific publications.

Although few high school students can claim authorship on a scientific paper, Ms. Grussing took the announcement in stride.

“I was really excited. I told some of my friends in math class,” she said. “And then I went to my room and did my math homework.”

The following is the paper’s abstract and two of the figures. See the full publication on PubMed:

Abstract

“A Bipartite Network Approach to Inferring Interactions Between Environmental Exposures and Human Diseases”

By Christian Darabos, Emily D. Grussing, Maria E. Cricco, Kenzie A. Clark, Jason H. Moore

Institute for the Quantitative Biomedical Sciences, The Geisel School of Medicine at Dartmouth College, Lebanon, NH

Environmental exposure is a key factor of understanding health and diseases. Beyond genetic propensities, many disorders are, in part, caused by human interaction with harmful substances in the water, the soil, or the air. Limited data is available on a disease or substance basis. However, we compile a global repository from literature surveys matching environmental chemical substances exposure with human disorders. We build a bipartite network linking 60 substances to over 150 disease phenotypes. We quantitatively and qualitatively analyze the network and its projections as simple networks. We identify mercury, lead and cadmium as associated with the largest number of disorders. Symmetrically, we show that breast cancer, harm to the fetus and non-Hodgkin’s lymphoma are associated with the most environmental chemicals. We conduct statistical analysis of how vertices with similar characteristics form the network interactions. This dyadicity and heterophilicity measures the tendencies of vertices with similar properties to either connect to one-another. We study the dyadic distribution of the substance classes in the networks show that, for instance, tobacco smoke compounds, parabens and heavy metals tend to be connected, which hint at common disease causing factors, whereas fungicides and phytoestrogens do not. We build an exposure network at the systems level. The information gathered in this study is meant to be complementary to the genome and help us understand complex diseases, their commonalities, their causes, and how to prevent and treat them.

Keywords: Exposure; Complex Diseases; Substances; Bipartite Network; Dyadicity; Heterophilicity; Human Phenotype Network.

Courtesy of Christian Darabos and Emily Grussing
Fig. 1. Schematic representation of a Bipartite Network (b) and its projection in the space of either vertex set (a) and (c).
Courtesy of Christian Darabos and Emily Grussing
Fig. 4. Projections. Nodes are colored according to their (majority) substance group according to the legend. (a) projection of the bipartite network onto the disease/trait space. Node sizes are proportionate to the number of substances associated. Edges are weighted by the number of shared substances. (b) projection of the bipartite network on the substances space. Node sizes are proportionate to the number of diseases associated. Edges are weighted as the number of shared diseases.

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