Detecting survival-associated biomarkers from heterogeneous populations

Saegusa, Takumi; Zhao, Zhiwei; Ke, Hongjie; Ye, Zhenyao; Xu, Zhongying; Chen, Shuo; Ma, Tianzhou

doi:10.1038/s41598-021-82332-y

Cited by 3 publications

(3 citation statements)

References 52 publications

(52 reference statements)

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“…Integrated feature selection algorithms universally have higher performance and generalization ability than single algorithms [13]. Holistic cancer research can find reliable biomarkers with high repeatability and reveals the potential of applications through feature selection methods [14]. This paper focus on the need for exploring reliable potential feature biomarkers through computer.…”

Section: Introductionmentioning

confidence: 99%

Selection of potential cancer biomarkers based on feature selection method

2023

Third International Conference on Intelligent Computing and Human-Computer Interaction (ICHCI 2022)

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Plentiful information is contained in the massive gene expression data obtained by DNA microarray technology. Efficient computer analysis methods are beneficial for extracting helpful information quickly and accurately and assist in understanding biological phenomena from enormous data. In this paper, through the integration of fisher-score in filter layer, recursive feature elimination and logistic regression in embedded layer, FSRL is proposed to search latent biomarkers in various cancers with high mortality rates. Compared with the currently popular online analysis tool GEO2R, FSRL has higher classification accuracy. FSRL performs better in evaluation index than five methods and dramatically enhances calculative efficiency. In prostate datasets, evaluation indicator such as accuracy of FSRL, is 40.1% higher than the average. Since biomarkers obtained through multiple cancer sets are more reliable and repeatable than single analysis, cluster analysis is conducted on six cancers with high mortality rates, and 13 genes are screened to form potential genomic biomarker modules. The genes are validated through literature review, GO analysis, and functional pathway retrieval to provide information on carcinogenic mechanisms. They can be used as a decision support system for potential biomarkers to help to narrow scope of biotic experiment scope and detect multiple cancers in targeted anti-cancer therapies.

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Section: Introductionmentioning

confidence: 99%

Selection of potential cancer biomarkers based on feature selection method

2023

Third International Conference on Intelligent Computing and Human-Computer Interaction (ICHCI 2022)

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“…The rate at which survival analysis is advancing and gaining popularity in every field of study is pretty impressive. The nature of data obtained in the area of Biostatistics has necessitated the growth in the volume of works done in the survival analysis [1][2][3][4][5]. Survival analysis is also of massive use in Engineering and Social sciences fields [6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Statistical Powers of Some Tests for Checking Homogeneity of Survival Distributions with Disjointed Ends in the Presence of Censoring

Teniola

Ayantunji²,

Yusuf³

et al. 2022

Int. J. Anal. Appl.

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This paper considered the comparison of some tests for assessing the overall homogeneity of Kaplan-Meier survival curves under low and high censoring rates when the curves are disjointed towards the end. The performances of these tests were measured by their statistical powers. Monte Carlo simulation study was conducted to evaluate and numerically compare the relative performances of Log-rank,Wilcoxon, Tarone-Ware, Peto-Peto, Modified Peto-Peto, the Fleming-Harrington (1,1), and the Babalola-Adeleke tests. The result obtained shows that the Babalola-Adeleke and Fleming-Harrington (1,1) tests have more robust performances than the other five popular tests with relatively high power in detecting differences when the censoring rates in the groups are both low and high. The highest overall average powers under low and high censoring rates were produced by Babalola-Adeleke and Fleming-Harrington (1,1) tests respectively. Hence, these two tests are the most suitable tests for diagnosing homogeneity of survival curves under these conditions.

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“…Ensemble classifiers can generally achieve greater precision and generalization ability than individual classifiers [ 19 ]. Biomarkers that are more reliable and reproducible, and reveal great potential on clinical application, can be more easily discovered through multiple analyses than through a single study [ 20 ]. In order to promote the classification capability of current feature selection methods, this paper creatively proposes a new feature selection algorithm named FRL by combining the advantages of filter methods and embedded methods ( Figure 1 ).…”

Section: Introductionmentioning

confidence: 99%

FRL: An Integrative Feature Selection Algorithm Based on the Fisher Score, Recursive Feature Elimination, and Logistic Regression to Identify Potential Genomic Biomarkers

Luo

Zhang

et al. 2021

BioMed Research International

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Accurate screening on cancer biomarkers contributes to health assessment, drug screening, and targeted therapy for precision medicine. The rapid development of high-throughput sequencing technology has identified abundant genomic biomarkers, but most of them are limited to single-cancer analysis. Based on the combination of Fisher score, Recursive feature elimination, and Logistic regression (FRL), this paper proposes an integrative feature selection algorithm named FRL to explore potential cancer genomic biomarkers on cancer subsets. Fisher score is initially used to calculate the weights of genes to rapidly reduce the dimension. Recursive feature elimination and Logistic regression are then jointly employed to extract the optimal subset. Compared to the current differential expression analysis tool GEO2R based on the Limma algorithm, FRL has greater classification precision than Limma. Compared with five traditional feature selection algorithms, FRL exhibits excellent performance on accuracy (ACC) and F1-score and greatly improves computational efficiency. On high-noise datasets such as esophageal cancer, the ACC of FRL is 30% superior to the average ACC achieved with other traditional algorithms. As biomarkers found in multiple studies are more reliable and reproducible, and reveal stronger association on potential clinical value than single analysis, through literature review and spatial analyses of gene functional enrichment and functional pathways, we conduct cluster analysis on 10 diverse cancers with high mortality and form a potential biomarker module comprising 19 genes. All genes in this module can serve as potential biomarkers to provide more information on the overall oncogenesis mechanism for the detection of diverse early cancers and assist in targeted anticancer therapies for further developments in precision medicine.

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Detecting survival-associated biomarkers from heterogeneous populations

Cited by 3 publications

References 52 publications

Selection of potential cancer biomarkers based on feature selection method

Selection of potential cancer biomarkers based on feature selection method

Statistical Powers of Some Tests for Checking Homogeneity of Survival Distributions with Disjointed Ends in the Presence of Censoring

FRL: An Integrative Feature Selection Algorithm Based on the Fisher Score, Recursive Feature Elimination, and Logistic Regression to Identify Potential Genomic Biomarkers

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