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Multi-task regression learning for survival analysis via prior information guided transductive matrix completion

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Abstract

Survival analysis aims to predict the occurrence time of a particular event of interest, which is crucial for the prognosis analysis of diseases. Currently, due to the limited study period and potential losing tracks, the observed data inevitably involve some censored instances, and thus brings a unique challenge that distinguishes from the general regression problems. In addition, survival analysis also suffers from other inherent challenges such as the high-dimension and small-sample-size problems. To address these challenges, we propose a novel multi-task regression learning model, i.e., prior information guided transductive matrix completion (PigTMC) model, to predict the survival status of the new instances. Specifically, we use the multi-label transductive matrix completion framework to leverage the censored instances together with the uncensored instances as the training samples, and simultaneously employ the multi-task transductive feature selection scheme to alleviate the overfitting issue caused by high-dimension and small-sample-size data. In addition, we employ the prior temporal stability of the survival statuses at adjacent time intervals to guide survival analysis. Furthermore, we design an optimization algorithm with guaranteed convergence to solve the proposed PigTMC model. Finally, the extensive experiments performed on the real microarray gene expression datasets demonstrate that our proposed model outperforms the previously widely used competing methods.

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China (Grant Nos. 61872190, 61772285, 61572263 and 61906098), in part by the Natural Science Foundation of Jiangsu Province (BK20161516), and in part by the Open Fund of MIIT Key Laboratory of Pattern Analysis and Machine Intelligence of NUAA.

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Authors and Affiliations

  1. Jiangsu Key Laboratory of Big Data Security and Intelligent Processing, Nanjing University of Posts and Telecommunications, Nanjing, 210023, China

    Lei Chen, Kai Shao, Xianzhong Long & Lingsheng Wang

  2. MIIT Key Laboratory of Pattern Analysis and Machine Intelligence, Nanjing University of Aeronautics and Astronautics, Nanjing, 211106, China

    Lei Chen

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  1. Lei Chen
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  2. Kai Shao
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  3. Xianzhong Long
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  4. Lingsheng Wang
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Correspondence to Lei Chen.

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Lei Chen received the Master’s Degree in computer software and theory from Nanjing University of Aeronautics and Astronautics, China in 2005, and the PhD Degree in communication and information system from Nanjing University of Posts and Telecommunications, China in 2014. He is currently a professor in the School of Computer Science, Nanjing University of Posts and Telecommunications, China. He was a visiting researcher at The University of North Carolina at Chapel Hill, USA, from June 2016 to June 2017. His research interests include machine learning, pattern recognition, and medical image analysis.

Kai Shao earned his bachelor’s degree in computer science from the Nanjing University of Information Science and Technology, China in 2017. From 2017, he continued his study in computer science in the Nanjing University of Posts and Telecommunications, China as a master candidate. His research focuses on the machine learning and data mining.

Xianzhong Long obtained his PhD degree from Shanghai Jiao Tong University, China in June 2014. He received his BS degree from Henan Polytechnic University, China in 2007 and MS degree from Xihua University, China in 2010, both in computer science. Now, he is an assistant professor at Nanjing University of Posts and Telecommunications, China. His research interests are computer vision, machine learning, and image processing, specifically on image classification, object recognition, and clustering.

Lingsheng Wang earned his bachelor’s degree in computer science from the Nanjing University of Posts and Telecommunications (NJUPT), China in 2018. From 2018, he continued his study in computer science in NJUPT as a master candidate. His research focuses on the machine learning and data mining.

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Chen, L., Shao, K., Long, X. et al. Multi-task regression learning for survival analysis via prior information guided transductive matrix completion. Front. Comput. Sci. 14, 145312 (2020). https://doi.org/10.1007/s11704-019-8374-z

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