title: NMFBFS: A NMF-Based Feature Selection Method in Identifying Pivotal Clinical Symptoms of Hepatocellular Carcinoma.
abstract: BACKGROUND
  Hepatocellular carcinoma (HCC) is a highly aggressive malignancy. Traditional Chinese Medicine (TCM), with the characteristics of syndrome differentiation, plays an important role in the comprehensive treatment of HCC. This study aims to develop a nonnegative matrix factorization- (NMF-) based feature selection approach (NMFBFS) to identify potential clinical symptoms for HCC patient stratification.
  
  
  METHODS
  The NMFBFS approach consisted of three major steps. Firstly, statistics-based preliminary feature screening was designed to detect and remove irrelevant symptoms. Secondly, NMF was employed to infer redundant symptoms. Based on NMF-derived basis matrix, we defined a novel similarity measurement of intersymptoms. Finally, we converted each group of redundant symptoms to a new single feature so that the dimension was further reduced.
  
  
  RESULTS
  Based on a clinical dataset consisting of 407 patient samples of HCC with 57 symptoms, NMFBFS approach detected 8 irrelevant symptoms and then identified 16 redundant symptoms within 6 groups. Finally, an optimal feature subset with 39 clinical features was generated after compressing the redundant symptoms by groups. The validation of classification performance shows that these 39 features obviously improve the prediction accuracy of HCC patients.
  
  
  CONCLUSIONS
  Compared with other methods, NMFBFS has obvious advantages in identifying important clinical features of HCC.
authors.affiliation: Machine Learning & Systems Biology Lab, School of Electronics and Information Engineering, Tongji University, 4800 Caoan Road, Shanghai 201804, China ; School of Information Engineering, Zhejiang A&F University, 88 Huancheng North Road, Linan 311300, China.
authors.forename: Zhiwei
authors.initials: Z
authors.lastname: Ji
authors.affiliation: Department of Clinical Laboratory, Tongde Hospital of Zhejiang Province, 234th Gucui Road, Hangzhou 310012, China.
authors.forename: Guanmin
authors.initials: G
authors.lastname: Meng
authors.affiliation: Machine Learning & Systems Biology Lab, School of Electronics and Information Engineering, Tongji University, 4800 Caoan Road, Shanghai 201804, China.
authors.forename: Deshuang
authors.initials: D
authors.lastname: Huang
authors.affiliation: Department of Traditional Chinese Medicine, Changzheng Hospital, Second Military Medical University, 415 Fengyang Road, Shanghai 200003, China.
authors.forename: Xiaoqiang
authors.initials: X
authors.lastname: Yue
authors.affiliation: Machine Learning & Systems Biology Lab, School of Electronics and Information Engineering, Tongji University, 4800 Caoan Road, Shanghai 201804, China ; The Advanced Research Institute of Intelligent Sensing Network, Tongji University, 4800 Caoan Road, Shanghai 201804, China ; The Key Laboratory of Embedded System and Service Computing, Tongji University, 4800 Caoan Road, Shanghai 201804, China.
authors.forename: Bing
authors.initials: B
authors.lastname: Wang
mesh_terms: D006528:Carcinoma, Hepatocellular / Q000175:diagnosis* / Q000628:therapy; D019295:Computational Biology; D003198:Computer Simulation; D016208:Databases, Factual / Q000706:statistics & numerical data; D003936:Diagnosis, Computer-Assisted / Q000379:methods / Q000706:statistics & numerical data; D005260:Female; D006801:Humans; D008113:Liver Neoplasms / Q000175:diagnosis* / Q000628:therapy; D008297:Male; D008516:Medicine, Chinese Traditional