ArticleComputational and mathematical methods in medicine2015
NMFBFS: A NMF-Based Feature Selection Method in Identifying Pivotal Clinical Symptoms of Hepatocellular Carcinoma.
Article in Computational and mathematical methods in medicine, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- scFseCluster: a feature selection-enhanced clustering for single-cell RNA-seq data.Life science alliance · 2023Article
- A Novel Feature Selection Strategy Based on Salp Swarm Algorithm for Plant Disease Detection.Plant phenomics (Washington, D.C.) · 2023Article
- DapBCH: a disease association prediction model Based on Cross-species and Heterogeneous graph embedding.Frontiers in genetics · 2023Article
- Ensemble Learning-Based Feature Selection for Phage Protein Prediction.Frontiers in microbiology · 2022Article
- Mitral Valve Segmentation Using Robust Nonnegative Matrix Factorization.Journal of imaging · 2021Article
- New Approach for Risk Estimation Algorithms ofDisease markers · 2020Article
- Prediction of Proximal Junctional Kyphosis After Posterior Scoliosis Surgery With Machine Learning in the Lenke 5 Adolescent Idiopathic Scoliosis Patient.Frontiers in bioengineering and biotechnology · 2020Article
- Screening therapeutic targets of ribavirin in hepatocellular carcinoma.Oncology letters · 2018Article
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5 authors.
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Abstract
backgroundHepatocellular 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.
methodsThe 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.
resultsBased 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.
conclusionsCompared with other methods, NMFBFS has obvious advantages in identifying important clinical features of HCC.
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