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Full-Text Articles in Computer Sciences
Data Mining The Functional Characterizations Of Proteins To Predict Their Cancer-Relatedness, Peter Revesz, Christopher Assi
Data Mining The Functional Characterizations Of Proteins To Predict Their Cancer-Relatedness, Peter Revesz, Christopher Assi
School of Computing: Faculty Publications
This paper considers two types of protein data. First, data about protein function described in a number of ways, such as, GO terms and PFAM families. Second, data about whether individual proteins are experimentally associated with cancer by an anomalous elevation or lowering of their expressions within cancerous cells. We combine these two types of protein data and test whether the first type of data, that is, the functional descriptors, can predict the second type of data, that is, cancer-relatedness. By using data mining and machine learning, we derive a classifier algorithm that using only GO term and PFAM family …
Redistricting Using Constrained Polygonal Clustering, Deepti Joshi, Leen-Kiat Soh, Ashok Samal
Redistricting Using Constrained Polygonal Clustering, Deepti Joshi, Leen-Kiat Soh, Ashok Samal
School of Computing: Faculty Publications
Redistricting is the process of dividing a geographic area consisting of spatial units—often represented as spatial polygons—into smaller districts that satisfy some properties. It can therefore be formulated as a set partitioning problem where the objective is to cluster the set of spatial polygons into groups such that a value function is maximized [1]. Widely used algorithms developed for point-based data sets are not readily applicable because polygons introduce the concepts of spatial contiguity and other topological properties that cannot be captured by representing polygons as points. Furthermore, when clustering polygons, constraints such as spatial contiguity and unit distributedness should …