S
Saeed Salem
Researcher at North Dakota State University
Publications - 66
Citations - 1946
Saeed Salem is an academic researcher from North Dakota State University. The author has contributed to research in topics: Cluster analysis & Structural alignment. The author has an hindex of 16, co-authored 64 publications receiving 1769 citations. Previous affiliations of Saeed Salem include Rensselaer Polytechnic Institute.
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Link prediction using supervised learning
TL;DR: This research identifies a set of features that are key to the superior performance under the supervised learning setup, and shows that a small subset of features always plays a significant role in the link prediction job.
Journal ArticleDOI
Proteomic and phospho-proteomic profile of human platelets in basal, resting state: Insights into integrin signaling
Amir H. Qureshi,Amir H. Qureshi,Vineet Chaoji,Dony Maiguel,Mohd Hafeez Faridi,Constantinos J. Barth,Saeed Salem,Mudita Singhal,Darren Stoub,Bryan Krastins,Mitsunori Ogihara,Mohammed J. Zaki,Vineet Gupta,Vineet Gupta +13 more
TL;DR: This work employed a combination of proteomic profiling and computational analyses of isolated human platelets to create a platelet protein-protein interaction (PPI) network and applied novel contextual information about the phosphorylation step to introduce limited directionality in the PPI graph.
Proceedings ArticleDOI
ORIGAMI: Mining Representative Orthogonal Graph Patterns
TL;DR: This paper presents ORIGAMI, an effective algorithm for mining the set of representative orthogonal patterns, and shows that the method is able to extract high quality patterns even in cases where existing enumerative graph mining methods fail to do so.
Proceedings ArticleDOI
Agent-Oriented Designs for a Self Healing Smart Grid
Steve Bou Ghosn,Prakash Ranganathan,Saeed Salem,Jingpeng Tang,Davin Loegering,Kendall E. Nygard +5 more
TL;DR: An agent-oriented architecture for a simulation which can help in understanding Smart Grid issues and in identifying ways to improve the electrical grid, and focuses primarily on the self-healing problem.
Journal ArticleDOI
Robust partitional clustering by outlier and density insensitive seeding
TL;DR: ROBIN is a novel method for initial seed selection in k-means types of algorithms that imposes constraints on the chosen seeds that lead to better clustering when k-Means converges and consistently outperforms existing approaches in terms of the clustering quality.