ArticleFrontiers in bioengineering and biotechnology2020
Robust Biomarker Screening Using Spares Learning Approach for Liver Cancer Prognosis.
Article in Frontiers in bioengineering and biotechnology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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4 citing papers in PubMed, 7 citations in OpenAlex.
- Exploring potential biomarkers and lead molecules in gastric cancer by network biology, drug repurposing and virtual screening strategies.Molecular diversity · 2025Article
- In silico functional, structural and pathogenicity analysis of missense single nucleotide polymorphisms in human MCM6 gene.Scientific reports · 2024Article
- Mutational Impacts on the N and C Terminal Domains of the MUC5B Protein: A Transcriptomics and Structural Biology Study.ACS omega · 2023Article
- Globally ncRNAs Expression Profiling of TNBC and Screening of Functional lncRNA.Frontiers in bioengineering and biotechnology · 2020Article
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
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Abstract
LncRNAs, miRNAs, mRNAs, methylation, and proteins exert profound biological functions and are widely applied as prognostic features in liver cancer. This study aims to identify prognostic biomarkers' signature for liver cancer. Samples with inadequate tumor purity were filtered out and the expression data from different resources were retrieved. The Spares learning approach was applied to select lncRNAs, miRNAs, mRNAs, methylation, and proteins' features based on their differentially expressed groups. The LASSO boosting technique was employed for the predictive model construction. A total of 200 lncRNAs, 200 miRNAs, 371 mRNAs, 371 methylations, and 184 proteins were observed to be differentially expressed. Five lncRNAs, 11 miRNAs, 30 mRNAs, 4 methylations, and 3 proteins were selected for further evaluation using the feature elimination technique. The highest accuracy of 89.32% is achieved as a result of training and learning by Spares learning methodology. Final outcomes revealed that 5 lncRNA, 11 miRNA, 30 mRNA, 4 methylation, and 3 protein signatures could be potential biomarkers for the prognosis of liver cancer patients.
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