Journal
IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS
Volume 22, Issue 5, Pages 1619-1629Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JBHI.2017.2769711
Keywords
Patient stratification; multiobjective algorithm; clustering
Categories
Funding
- Research Grants Council of the Hong Kong Special Administrative Region [CityU 21200816, CityU 11203217]
- City University of Hong Kong (CityU Project) [7200444/CS]
- Amazon Web Service (AWS) Research Grant
- Microsoft Azure Research Award
- National Natural Science Foundation of China [61603087]
- Natural Science Foundation of Jilin Province [20160101253JC]
- Fundamental Research Funds for Northeast Normal University [2412017FZ026]
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One of the main challenges in modern medicine is to stratify patients for personalized care. Many different clustering methods have been proposed to solve the problem in both quantitative and biologically meaningful manners. However, existing clustering algorithms suffer from numerous restrictions such as experimental noises, high dimensionality, and poor interpretability. To overcome those limitations altogether, we propose and formulate a multiobjective framework based on evolutionary multiobjective optimization to balance the feature relevance and redundancy for patient stratification. To demonstrate the effectiveness of our proposed algorithms, we benchmark our algorithms across 55 synthetic datasets based on a real human transcription regulation network model, 35 real cancer gene expression datasets, and two case studies. Experimental results suggest that the proposed algorithms perform better than the recent state-of-the-arts. In addition, time complexity analysis, convergence analysis, and parameter analysis are conducted to demonstrate the robustness of the proposed methods from different perspectives. Finally, the t-Distributed Stochastic Neighbor Embedding (t-SNE) is applied to project the selected feature subsets onto two or three dimensions to visualize the high-dimensional patient stratification data.
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