ArticleDatabase : the journal of biological databases and curation2018
BioDataome: a collection of uniformly preprocessed and automatically annotated datasets for data-driven biology.
Article in Database : the journal of biological databases and curation, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- A clinically annotated transcriptomic atlas of nervous system tumors.Neuro-oncology · 2025Article
- BioVDB: biological vector database for high-throughput gene expression meta-analysis.Frontiers in artificial intelligence · 2024Article
- A characteristic cerebellar biosignature for bipolar disorder, identified with fully automatic machine learning.IBRO neuroscience reports · 2023Article
- Ten simple rules for using public biological data for your research.PLoS computational biology · 2023Article
- Learning biologically-interpretable latent representations for gene expression data: Pathway Activity Score Learning Algorithm.Machine learning · 2023Article
- A comprehensive survey on computational learning methods for analysis of gene expression data.Frontiers in molecular biosciences · 2022Review
- Accurate Blood-Based Diagnostic Biosignatures for Alzheimer's Disease via Automated Machine Learning.Journal of clinical medicine · 2020Article
- Article
- A data driven approach reveals disease similarity on a molecular level.NPJ systems biology and applications · 2019Article
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5 authors.
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Abstract
Biotechnology revolution generates a plethora of omics data with an exponential growth pace. Therefore, biological data mining demands automatic, 'high quality' curation efforts to organize biomedical knowledge into online databases. BioDataome is a database of uniformly preprocessed and disease-annotated omics data with the aim to promote and accelerate the reuse of public data. We followed the same preprocessing pipeline for each biological mart (microarray gene expression, RNA-Seq gene expression and DNA methylation) to produce ready for downstream analysis datasets and automatically annotated them with disease-ontology terms. We also designate datasets that share common samples and automatically discover control samples in case-control studies. Currently, BioDataome includes ∼5600 datasets, ∼260 000 samples spanning ∼500 diseases and can be easily used in large-scale massive experiments and meta-analysis. All datasets are publicly available for querying and downloading via BioDataome web application. We demonstrate BioDataome's utility by presenting exploratory data analysis examples. We have also developed BioDataome R package found in: https://github.com/mensxmachina/BioDataome/.Database URL: http://dataome.mensxmachina.org/.
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