Data-driven Insights into Human Disease

Kiley Graim, Ph.D.

Assistant Professor, Dept. of Computer and Information Science and Engineering

Abstract: Big data provides insights into diseases that would otherwise be undetectable. My lab develops machine learning models integrating diverse sources of large-scale genomic data to address key human health and disease questions. These models probe complex biological networks to answer questions arising from basic and translational research. I will discuss how data, often seemingly unrelated to the scientific question, significantly boosts our ability to understand disease and its complex underlying biology. We will discuss several applications of these principles to disease, and how this has changed our understanding of the biological mechanisms driving those diseases.

Dr. Kiley Graim is an assistant professor in Computer & Information Science & Engineering. Her lab develops machine learning models that integrate diverse large-scale genomics data to address key questions in human health and disease.

Core Standards

SC.912.N.1.2 Describe and explain what characterizes science and its methods.

SC.912.N.1.3 Recognize that the strength or usefulness of a scientific claim is evaluated through scientific argumentation, which depends on critical and logical thinking, and the active consideration of alternative scientific explanations to explain the data presented.

SC.912.N.1.4 Identify sources of information and assess their reliability according to the strict standards of scientific investigation.

SC.912.N.1.6 Describe how scientific inferences are drawn from scientific observations and provide examples from the content being studied.

SC.912.N.2.4 Explain that scientific knowledge is both durable and robust and open to change. Scientific knowledge can change because it is often examined and re-examined by new investigations and scientific argumentation. Because of these frequent examinations, scientific knowledge becomes stronger, leading to its durability.

SC.912.N.2.5 Describe instances in which scientists’ varied backgrounds, talents, interests, and goals influence the inferences and thus the explanations that they make about observations of natural phenomena and describe that competing interpretations (explanations) of scientists are a strength of science as they are a source of new, testable ideas that have the potential to add new evidence to support one or another of the explanations.

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