ArticleCommunications biology2019
GECKO is a genetic algorithm to classify and explore high throughput sequencing data.
Article in Communications biology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- MKMC enables reference-free transcriptomic analysis using k-mer representations.bioRxiv : the preprint server for biology · 2026Article
- A survey of k-mer methods and applications in bioinformatics.Computational and structural biotechnology journal · 2024Review
- RNA methylation and cellular response to oxidative stress-promoting anticancer agents.Cell cycle (Georgetown, Tex.) · 2023Review
- New progress in the role of microRNAs in the diagnosis and prognosis of triple negative breast cancer.Frontiers in molecular biosciences · 2023Review
- The contribution of uncharted RNA sequences to tumor identity in lung adenocarcinoma.NAR cancer · 2022Article
- PseudoGA: cell pseudotime reconstruction based on genetic algorithm.Nucleic acids research · 2021Article
- In silico design of novel aptamers utilizing a hybrid method of machine learning and genetic algorithm.Molecular diversity · 2021Review
- Article
- iMOKA: k-mer based software to analyze large collections of sequencing data.Genome biology · 2020Article
- The Maintenance of Mitochondrial DNA Integrity and Dynamics by Mitochondrial Membranes.Life (Basel, Switzerland) · 2020Review
Corrections and comments
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Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Comparative analysis of high throughput sequencing data between multiple conditions often involves mapping of sequencing reads to a reference and downstream bioinformatics analyses. Both of these steps may introduce heavy bias and potential data loss. This is especially true in studies where patient transcriptomes or genomes may vary from their references, such as in cancer. Here we describe a novel approach and associated software that makes use of advances in genetic algorithms and feature selection to comprehensively explore massive volumes of sequencing data to classify and discover new sequences of interest without a mapping step and without intensive use of specialized bioinformatics pipelines. We demonstrate that our approach called GECKO for GEnetic Classification using
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Registered trials
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