Search for: All records

Abstract The use of graph theory models is widespread in biological pathway analyses as it is often desired to evaluate the position of genes and proteins in their interaction networks of the biological systems. In this article, we argue that the common standard graph centrality measures do not sufficiently capture the informative topological organizations of the pathways, and thus, limit the biological inference. While key pathway elements may appear both upstream and downstream in pathways, standard directed graph centralities attribute significant topological importance to the upstream elements and evaluate the downstream elements as having no importance.We present a directed graph framework, Source/Sink Centrality (SSC), to address the limitations of standard models. SSC separately measures the importance of a node in the upstream and the downstream of a pathway, as a sender and a receiver of biological signals, and combines the two terms for evaluating the centrality. To validate SSC, we evaluate the topological position of known human cancer genes and mouse lethal genes in their respective KEGG annotated pathways and show that SSC-derived centralities provide an effective framework for associating higher positional importance to the genes with higher importance from a priori knowledge. While the presented work challenges some of the modeling assumptions in the common pathway analyses, it provides a straight-forward methodology to extend the existing models. The SSC extensions can result in more informative topological description of pathways, and thus, more informative biological inference. 

Early programming courses, such as CS1, are an important time to capture the interest of the students while imparting important technical knowledge. Yet many CS1 sections use contrived assignments and activities that tend to make students uninterested and doubt the usefulness of the content. We demonstrate that one can make an interesting CS1 experience for students by coupling interesting datasets with visual representations and interactive applications. Our approach enables teaching an engaging early programming course without changing the content of that course. This approach relies on the BRIDGES system that has been under development for the past 5 years; BRIDGES provides easy access to datasets and interactive applications. The assignments we present are all scaffolded to be directly integrated into most early programming courses to make routine topics more compelling and exciting. 

Computer Science students in algorithm courses often drop out and feel that what they are learning is disconnected from real life programming. Instructors, on the other hand, feel that algorithmic content is foundational for the long term development of students. The disconnect seems to stem from students not perceiving the importance of algorithmic paradigms, and how they impact performance in applications. We present the point of view that by solving real-world problems where algorithmic paradigms and complexity matter, students will become more engaged with the course and appreciate its importance. Our approach relies on a lean educational framework that provides simplified access to real life datasets and benchmarking features. The assignments we present are all scaffolded, and easily integrated into most algorithms courses. Feedback from using some of the assignments in various courses is presented to argue for the validity of the approach.