ArticleComputational and structural biotechnology journal2017
A random forest classifier for detecting rare variants in NGS data from viral populations.
Article in Computational and structural biotechnology journal, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 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
5 citing papers in PubMed.
- SARS-CoV-2 wastewater genomic surveillance: approaches, challenges, and opportunities.Genome biology · 2026Review
- Algorithms for Short-Read Viral Haplotype Reconstruction: Challenges, Solutions, and Perspectives.Methods in molecular biology (Clifton, N.J.) · 2025Review
- Classification of group A rotavirus VP7 and VP4 genotypes using random forest.Frontiers in genetics · 2023Article
- Efficient collection of a large number of mutations by mutagenesis of DNA damage response defective animals.Scientific reports · 2021Article
- Epidemiological data analysis of viral quasispecies in the next-generation sequencing era.Briefings in bioinformatics · 2021Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We propose a random forest classifier for detecting rare variants from sequencing errors in Next Generation Sequencing (NGS) data from viral populations. The method utilizes counts of varying length of
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.