NMFBFS: A NMF-Based Feature Selection Method in Identifying Pivotal Clinical Symptoms of Hepatocellular Carcinoma

Ji, Zhiwei; Meng, Guanmin; Huang, De-Shuang; Yue, Xiao-Qiang; Wang, Bing

doi:10.1155/2015/846942

Cited by 22 publications

(11 citation statements)

References 49 publications

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“…The clinical utility of the model was also evaluated with F1 score, which is a necessary synthesized indicator by conveying the balance between the precision and the recall in imbalanced dataset ( Chawla et al, 2002 ). At last, the model with the best prognostic performance was considered as the final prediction model to obtain the feature importance in PJK occurrence by ranking factor influences ( Ji et al, 2015 ). Python version 3.5 (Python Software Foundation, Wilmington, DE, United States) was used for modeling.…”

Section: Methodsmentioning

confidence: 99%

Prediction of Proximal Junctional Kyphosis After Posterior Scoliosis Surgery With Machine Learning in the Lenke 5 Adolescent Idiopathic Scoliosis Patient

Lan

Peng

et al. 2020

Front. Bioeng. Biotechnol.

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Objective To build a model for proximal junctional kyphosis (PJK) prognostication in Lenke 5 adolescent idiopathic scoliosis (AIS) patients undergoing long posterior instrumentation and fusion surgery by machine learning and analyze the risk factors for PJK. Materials and Methods In total, 44 AIS patients (female/male: 34/10; PJK/non-PJK: 34/10) who met the inclusion criteria between January 2013 and December 2018 were retrospectively recruited from West China Hospital. Thirty-seven clinical and radiological features were acquired by two independent investigators. Univariate analyses between PJK and non-PJK groups were carried out. Twelve models were built by using four types of machine learning algorithms in conjunction with two oversampling methods [the synthetic minority technique (SMOTE) and random oversampling]. Area under the receiver operating characteristic curve (AUC) was used for model discrimination, and the clinical utility was evaluated by using F1 score and accuracy. The risk factors were simultaneously analyzed by a Cox regression and machine learning. Results Statistical differences between PJK and non-PJK groups were as follows: gender ( p = 0.001), preoperative factors [thoracic kyphosis ( p = 0.03), T1 slope angle (T1S, p = 0.078)], and postoperative factors [T1S ( p = 0.097), proximal junctional angle ( p = 0.003), upper instrumented vertebra (UIV) – UIV + 1 ( p = 0.001)]. Random forest using SMOTE achieved the best prediction performance with AUC = 0.944, accuracy = 0.909, and F1 score = 0.667 on independent testing dataset. Cox model revealed that male gender and larger preoperative T1S were independent prognostic factors of PJK (odds ratio = 10.701 and 57.074, respectively). Gender was also at the first place in the importance ranking of the model with best performance. Conclusion The random forest using SMOTE model has the great value for predicting the individual risk of developing PJK after long instrumentation and fusion surgery in Lenke 5 AIS patients. Moreover, the combination of the outcomes of a Cox model and the feature ranking extracted by machine learning is more valuable than any one alone, especially in the interpretation of risk factors.

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Section: Methodsmentioning

confidence: 99%

Prediction of Proximal Junctional Kyphosis After Posterior Scoliosis Surgery With Machine Learning in the Lenke 5 Adolescent Idiopathic Scoliosis Patient

Lan

Peng

et al. 2020

Front. Bioeng. Biotechnol.

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“…Additionally, the general prognosis was poor with an overall survival rate between 3 and 5% in 2006 ( 4 ). Symptoms of HCC include yellow skin, bloating from fluid in the abdomen, easy bruising from blood clotting abnormalities, loss of appetite, unintentional weight loss, nausea, vomiting and tiredness ( 5 , 6 ). The primary risk factors for HCC were hepatitis C, hepatitis B, alcoholism, aflatoxin and cirrhosis of the liver ( 7 – 10 ).…”

Section: Introductionmentioning

confidence: 99%

Screening therapeutic targets of ribavirin in hepatocellular carcinoma

Luo

et al. 2018

Oncol Lett

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The objective of the present study was to screen the key genes of ribavirin in hepatocellular carcinoma (HCC) and provide novel therapeutic targets for HCC treatment. The mRNA expression datasets of GSE23031 and GSE74656, as well as the microRNA (miRNA) expression dataset of GSE22058 were downloaded from the Gene Expressed Omnibus database. In the GSE23031 dataset, there were three HCC cell lines treated with PBS and three HCC cell lines treated with ribavirin. In the GSE74656 dataset, five HCC tissues and five carcinoma adjacent tissues were selected. In the GSE22058 dataset, 96 HCC tissues and 96 carcinoma adjacent tissues were selected. The differentially expressed genes (DEGs) and differentially expressed miRNAs were identified via the limma package of R. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis was performed with the Database for Annotation, Visualization and Integrated Discovery. The target mRNAs of DEMs were obtained with TargetScan. A total of 559 DEGs (designated DEG-Ribavirin) were identified in HCC cells treated with ribavirin compared with PBS and 632 DEGs (designated DEG-Tumor) were identified in HCC tissues compared with carcinoma adjacent tissues. A total of 220 differentially expressed miRNAs were identified in HCC tissues compared with carcinoma adjacent tissues. In addition, 121 GO terms and three KEGG pathways of DEG-Ribavirin were obtained, and 383 GO terms and 25 KEGG pathways of DEG-Tumor were obtained. A total of five key miRNA-mRNA regulated pairs were identified, namely miR-183→CCNB1, miR-96→DEPDC1, miR-96→NTN4, miR-183→NTN4 and miR-145→NTN4. The present study indicated that certain miRNAs (including miR-96, miR-145 and miR-183) and mRNAs (including NAT2, FBXO5, CCNB1, DEPDC1 and NTN4) may be associated with the effects of ribavirin on HCC. Furthermore, they may provide novel therapeutic targets for HCC treatment.

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“…For example, in [16], authors proposed an algorithm based on particle swarm optimization rules, significantly reducing feature space in identification of potential clinical syndromes of Hepatocellular carcinoma. For similar task, different algorithm was later proposed, based on nonnegative matrix factorization [17].…”

Section: Introductionmentioning

confidence: 99%

Empirical Comparison of the Feature Evaluation Methods Based on Statistical Measures

Łysiak

Szmajda

2021

IEEE Access

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One of the most important classification problems is selecting proper features, i.e. features that describe the classified object in the most straightforward way possible. Then, one of the biggest challenges of the feature selection is the evaluation of the feature's quality. There is a plethora of feature evaluation methods in the literature. This paper presents the results of a comparison between nine selected feature evaluation methods, both existing in literature and newly defined. To make a comparison, features from ten various sets were evaluated by every method. Then, from every feature set, best subset (according to each method) was chosen. Those subsets then were used to train a set of classifiers (including decision trees and forests, linear discriminant analysis, naive Bayes, support vector machines, k nearest neighbors and an artificial neural network). The maximum accuracy of those classifiers, as well as the standard deviation between their accuracies, were used as a quality measures of each particular method. Furthermore, it was determined, which method is the most universal in terms of the data set, i.e. for which method, obtained accuracies were dependent on the feature set the least. Finally, computation time of each method was compared. Results indicated that for applications with limited computational power, method based on the average overlap between feature's values seem best suited. It led to high accuracies and proved to be fast to compute. However, if the data set is known to be normally distributed, method based on two-sample t-test may be preferable.

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NMFBFS: A NMF-Based Feature Selection Method in Identifying Pivotal Clinical Symptoms of Hepatocellular Carcinoma

Cited by 22 publications

References 49 publications

Prediction of Proximal Junctional Kyphosis After Posterior Scoliosis Surgery With Machine Learning in the Lenke 5 Adolescent Idiopathic Scoliosis Patient

Prediction of Proximal Junctional Kyphosis After Posterior Scoliosis Surgery With Machine Learning in the Lenke 5 Adolescent Idiopathic Scoliosis Patient

Screening therapeutic targets of ribavirin in hepatocellular carcinoma

Empirical Comparison of the Feature Evaluation Methods Based on Statistical Measures

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