Y
Yi Pan
Researcher at Georgia State University
Publications - 731
Citations - 18335
Yi Pan is an academic researcher from Georgia State University. The author has contributed to research in topics: Cluster analysis & Parallel algorithm. The author has an hindex of 62, co-authored 695 publications receiving 14706 citations. Previous affiliations of Yi Pan include Hong Kong University of Science and Technology & Chinese Academy of Sciences.
Papers
More filters
Journal ArticleDOI
CytoNCA: a cytoscape plugin for centrality analysis and evaluation of protein interaction networks.
TL;DR: CytoNCA, a Cytoscape plugin integrating calculation, evaluation and visualization analysis for multiple centrality measures, is presented, an excellent tool for calculating centrality, evaluating and visualizing biological networks.
Proceedings ArticleDOI
A study of the routing and spectrum allocation in spectrum-sliced Elastic Optical Path networks
Yang Wang,Xiaojun Cao,Yi Pan +2 more
TL;DR: This work comprehensively study the routing and spectrum allocation (RSA) problem in the SLICE network, and formulate the RSA problem using the Integer Linear Programming (ILP) formulations to optimally minimize the maximum number of sub-carriers required on any fiber of a SLice network.
Journal ArticleDOI
Drug repositioning based on comprehensive similarity measures and Bi-Random walk algorithm
TL;DR: A novel computational method named MBiRW is proposed, which utilizes some comprehensive similarity measures and Bi-Random walk (BiRW) algorithm to identify potential novel indications for a given drug, and outperforms several recent computational drug repositioning approaches.
Journal ArticleDOI
A survey of MRI-based brain tumor segmentation methods
TL;DR: The preprocessing operations and the state of the art methods of MRI-based brain tumor segmentation are introduced, the evaluation and validation of the results are discussed, and an objective assessment is presented.
Journal ArticleDOI
Identification of Essential Proteins Based on Edge Clustering Coefficient
TL;DR: The experimental results on the three different networks show that the number of essential proteins discovered by NC universally exceeds that discovered by the six other centrality measures: DC, BC, CC, SC, EC, and IC.