ArticleFrontiers in genetics2020
sigFeature: Novel Significant Feature Selection Method for Classification of Gene Expression Data Using Support Vector Machine and
Article in Frontiers in genetics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
35 citing papers in PubMed.
- Honey bee nutritranscriptomics reveals key insights towards precision nutrition.Journal of advanced research · 2026Article
- Explainable machine learning-driven identification of heart failure biomarkers: a multi-model feature selection approach with SHAP-based interpretability.Scientific reports · 2026Article
- Cross-scale modeling reveals a TFRC-driven immunosuppressive macrophage niche in cervical cancer.Frontiers in immunology · 2026Article
- Pan-Cancer Detection Through DNA Methylation Profiling Using Enzymatic Conversion Library Preparation with Targeted Sequencing.International journal of molecular sciences · 2025Article
- Identification of hub gene and immune infiltration in Lyme disease revealed by weighted gene co-expression network analysis and machine learning.European journal of medical research · 2025Article
- Machine learning reveals distinct gene expression signatures across tissue states in stony coral tissue loss disease.Royal Society open science · 2025Article
- Genome-wide nucleosome footprints of plasma cfDNA predict preterm birth: A case-control study.PLoS medicine · 2025Article
- Machine learning identifies PYGM as a macrophage polarization-linked metabolic biomarker in rectal cancer prognosis.Frontiers in immunology · 2025Article
- Identification and validation of icaritin-associated prognostic genes in hepatocellular carcinoma through network pharmacology, bioinformatics analysis, and cellular experiments.Frontiers in immunology · 2025Article
- Article
- Article
- Targeting neddylation and sumoylation in chemoresistant triple negative breast cancer.NPJ breast cancer · 2024Article
- Whole-genome bisulfite sequencing identifies stage- and subtype-specific DNA methylation signatures in pancreatic cancer.iScience · 2024Article
- ASRpro: A machine-learning computational model for identifying proteins associated with multiple abiotic stress in plants.The plant genome · 2024Article
- Construction and verification of an endoplasmic reticulum stress-related prognostic model for endometrial cancer based on WGCNA and machine learning algorithms.Frontiers in oncology · 2024Article
- Integrated machine learning identifies a cellular senescence-related prognostic model to improve outcomes in uterine corpus endometrial carcinoma.Frontiers in immunology · 2024Article
- Epigenomic signature of major congenital heart defects in newborns with Down syndrome.Human genomics · 2023Article
- Epigenomic signature of major congenital heart defects in newborns with Down syndrome.medRxiv : the preprint server for health sciences · 2023Article
- Identifying Biomarkers Using Support Vector Machine to Understand the Racial Disparity in Triple-Negative Breast Cancer.Journal of computational biology : a journal of computational molecular cell biology · 2023Article
- Computation of the distribution of model accuracy statistics in machine learning: Comparison between analytically derived distributions and simulation-based methods.Health science reports · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Biological data are accumulating at a faster rate, but interpreting them still remains a problem. Classifying biological data into distinct groups is the first step in understanding them. Data classification in response to a certain treatment is an extremely important aspect for differentially expressed genes in making present/absent calls. Many feature selection algorithms have been developed including the support vector machine recursive feature elimination procedure (SVM-RFE) and its variants. Support vector machine RFEs are greedy methods that attempt to find superlative possible combinations leading to binary classification, which may not be biologically significant. To overcome this limitation of SVM-RFE, we propose a novel feature selection algorithm, termed as "sigFeature" (https://bioconductor.org/packages/sigFeature/), based on SVM and
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.