Feature selection and classification of urinary mRNA microarray data by iterative random forest to diagnose renal fibrosis: a two-stage study

Zhou, Le-Ting; Cao, Yuhan; Lv, Lin‐Li; Ma, Kun-Ling; Chen, Pingsheng; Ni, Haifeng; Lei, Xiang-Dong; Liu, Bi‐Cheng

doi:10.1038/srep39832

Cited by 23 publications

(17 citation statements)

References 29 publications

(39 reference statements)

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“…Using targeted microarrays, we identified urinary vimentin mRNA as a biomarker to predict renal fibrosis and verified its predictive ability in CKD patients [84] . Upon iterative random forest analysis of a targeted microarray, four fibrosis-associated mRNAs (tumor growth factor β1, matrix metallopeptidase 9, tissue inhibitor of metalloproteinases 2, and vimentin) in urinary sediments were identified as sensitive predictors of tubulointerstitial fibrosis [85] .…”

Section: Towards Personalized Medicine: Future Prospects and Challengesmentioning

confidence: 99%

Genomic biomarkers for chronic kidney disease: the first step towards personalized medicine?

Cao¹,

Zhou²,

Liu³

2019

JTGG

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With the prevalence of end stage renal disease steadily increasing, chronic kidney disease (CKD) represents an impending public healthcare challenge. Classical diagnostic biomarkers of CKD, including creatinine, have low sensitivity and specificity. Thus, novel diagnostic and prognostic biomarkers for patients at high risk of early-stage progression are urgently needed. Personalized medicine approaches generally stratify patients according to their biological or genomic make-up. Targeted clinical trials require more precise identification of these subgroups. The use of new biomarkers obtained via high-throughput technologies is expected in future, accompanied by vast improvements in computational power applied in genomics, proteomics, and metabolomics studies using biological fluids and renal biopsy tissue. Genomic biomarkers may not only provide additional information regarding the etiology and mechanisms underlying CKD progression, but may also enable early diagnosis and the selection of appropriate drugs, thereby personalizing therapy. This review discusses commonly used research methods in genomic medicine and summarizes currently available genomic biomarkers in inherited and acquired CKD.

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Section: Towards Personalized Medicine: Future Prospects and Challengesmentioning

confidence: 99%

Genomic biomarkers for chronic kidney disease: the first step towards personalized medicine?

Cao¹,

Zhou²,

Liu³

2019

JTGG

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“…Feature selection techniques have been successfully applied in many real-world applications, such as large-scale biological data analysis [ [24] , [25] , [26] ], text classification [ 27 ], information retrieval [ 28 ], near-infrared spectroscopy [ 29 ], mass spectroscopy data analysis [ 30 ], drug design [ 31 , 32 ], and especially the quantitative structure-activity relationship (QSAR) modeling [ 33 , 34 ]. In cancer research community, feature selection has also been widely applied in different omics data analyses: mRNA data [ 9 , 35 ], miRNA data [ 36 , 37 ], whole exome sequencing data [ 38 ], DNA-methylation data [ 39 , 40 ], and proteomics data [ 41 , 42 ]. Recently, some researchers have applied feature selection techniques on integrative analysis of multi-omics data.…”

Section: Feature Selection Techniquesmentioning

confidence: 99%

A Review of Matched-pairs Feature Selection Methods for Gene Expression Data Analysis

Liang

Yang

et al. 2018

Computational and Structural Biotechnology Journal

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With the rapid accumulation of gene expression data from various technologies, e.g., microarray, RNA-sequencing (RNA-seq), and single-cell RNA-seq, it is necessary to carry out dimensional reduction and feature (signature genes) selection in support of making sense out of such high dimensional data. These computational methods significantly facilitate further data analysis and interpretation, such as gene function enrichment analysis, cancer biomarker detection, and drug targeting identification in precision medicine. Although numerous methods have been developed for feature selection in bioinformatics, it is still a challenge to choose the appropriate methods for a specific problem and seek for the most reasonable ranking features. Meanwhile, the paired gene expression data under matched case-control design (MCCD) is becoming increasingly popular, which has often been used in multi-omics integration studies and may increase feature selection efficiency by offsetting similar distributions of confounding features. The appropriate feature selection methods specifically designed for the paired data, which is named as matched-pairs feature selection (MPFS), however, have not been maturely developed in parallel. In this review, we compare the performance of 10 feature-selection methods (eight MPFS methods and two traditional unpaired methods) on two real datasets by applied three classification methods, and analyze the algorithm complexity of these methods through the running of their programs. This review aims to induce and comprehensively present the MPFS in such a way that readers can easily understand its characteristics and get a clue in selecting the appropriate methods for their analyses.

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“…[ 13 ] Besides, by feature selection and classification of microarray data, we identified a four-mRNA signature, including TGFβ1, MMP9, TIMP2, and vimentin, as important features of TIF. [ 14 ]…”

Section: U Rine C Ontains R mentioning

confidence: 99%

Urinary Biomarkers for Chronic Kidney Disease with a Focus on Gene Transcript

Lyu

Feng

Liu

2017

Chinese Medical Journal

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Objective:In the upcoming era of precision medicine, searching for the early, noninvasive biomarkers has been the cornerstone and major challenge in the management of chronic kidney disease (CKD). Urine contains rich biological information which could be the ideal source for noninvasive biomarkers of CKD. This review will discuss the recent advance in urinary biomarker.Data Sources:This review was based on data in articles published in the PubMed databases up to June 20, 2017, with the following keywords: “Chronic kidney disease”, “Biomarker”, and “Urine”.Study Selection:Original articles and important reviews on urinary biomarker were selected for this review.Results:Urinary biomarker studies of CKD mainly focused on urine sediment, supernatant, and urinary extracellular vesicles. The gene transcript (microRNA [miRNA], messenger RNA [mRNA]) biomarkers have been recently shown with diagnostic potential for CKD reflecting kidney function and histological change. However, challenges regarding technique and data analysis need to be resolved before translation to clinic.Conclusions:Different fractions of urine contain rich information for biomarker discovery, among which urine (extracellular vesicles) mRNA, miRNA, might represent promising biomarker for CKD.

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Feature selection and classification of urinary mRNA microarray data by iterative random forest to diagnose renal fibrosis: a two-stage study

Cited by 23 publications

References 29 publications

Genomic biomarkers for chronic kidney disease: the first step towards personalized medicine?

Genomic biomarkers for chronic kidney disease: the first step towards personalized medicine?

A Review of Matched-pairs Feature Selection Methods for Gene Expression Data Analysis

Urinary Biomarkers for Chronic Kidney Disease with a Focus on Gene Transcript

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