ReviewComputational and structural biotechnology journal2020
Method development for cross-study microbiome data mining: Challenges and opportunities.
Review in Computational and structural biotechnology journal, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 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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Who cites it
16 citing papers in PubMed.
- TaxaCal: enhancing species-level profiling accuracy of 16S amplicon data.BMC bioinformatics · 2025Article
- Techniques for learning and transferring knowledge for microbiome-based classification and prediction: review and assessment.Briefings in bioinformatics · 2024Review
- On the limits of 16S rRNA gene-based metagenome prediction and functional profiling.Microbial genomics · 2024Article
- Flex Meta-Storms elucidates the microbiome local beta-diversity under specific phenotypes.Bioinformatics (Oxford, England) · 2023Article
- Exercise and microbiome: From big data to therapy.Computational and structural biotechnology journal · 2023Review
- Machine Learning Techniques for Antimicrobial Resistance Prediction ofComputational intelligence and neuroscience · 2023Article
- SKIOME Project: a curated collection of skin microbiome datasets enriched with study-related metadata.Database : the journal of biological databases and curation · 2022Article
- Elucidating the Beta-Diversity of the Microbiome: from Global Alignment to Local Alignment.mSystems · 2021Article
- Holo-omics for deciphering plant-microbiome interactions.Microbiome · 2021Review
- Microbiome Search Engine 2: a Platform for Taxonomic and Functional Search of Global Microbiomes on the Whole-Microbiome Level.mSystems · 2021Article
- Genomes OnLine Database (GOLD) v.8: overview and updates.Nucleic acids research · 2021Article
- Meta-Apo improves accuracy of 16S-amplicon-based prediction of microbiome function.BMC genomics · 2021Article
- Article
- Extending Association Rule Mining to Microbiome Pattern Analysis: Tools and Guidelines to Support Real Applications.Frontiers in bioinformatics · 2021Article
- Structure and Function of Oral Microbial Community in Periodontitis Based on Integrated Data.Frontiers in cellular and infection microbiology · 2021Article
- Towards multi-label classification: Next step of machine learning for microbiome research.Computational and structural biotechnology journal · 2021Review
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
During the past decade, tremendous amount of microbiome sequencing data has been generated to study on the dynamic associations between microbial profiles and environments. How to precisely and efficiently decipher large-scale of microbiome data and furtherly take advantages from it has become one of the most essential bottlenecks for microbiome research at present. In this mini-review, we focus on the three key steps of analyzing cross-study microbiome datasets, including microbiome profiling, data integrating and data mining. By introducing the current bioinformatics approaches and discussing their limitations, we prospect the opportunities in development of computational methods for the three steps, and propose the promising solutions to multi-omics data analysis for comprehensive understanding and rapid investigation of microbiome from different angles, which could potentially promote the data-driven research by providing a broader view of the "microbiome data space".
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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.