---
license: afl-3.0
task_categories:
- tabular-classification
pretty_name: BioDataome
size_categories:
- n<1k
- 1K<n<10K
tags:
- biology
---

# BioDataome

This is an aggregate dataset which allows you to download any and all data from the [BioDataome project](http://dataome.mensxmachina.org/).

## What is BioDataome?
BioDataome is a collection of uniformly preprocessed and automatically annotated datasets for data-driven biology. The processed data can be accessed via the BioDataome website in .csv format and the BioDataome package via github. BioDataome package contains all the functions used to download, preprocess and annotate gene expression and methylation microarray data from Gene Expression Omnibus, as well as RNASeq data from recount.

## Usage
```python
import datasets
ds = datasets.load_dataset("wwydmanski/biodataome", "GSE24849")['train']
split_ds = ds.train_test_split(test_size=0.1)
train_ds, test_ds = split_ds['train'], split_ds['test']

# there is probably a better way to do this, but this seems to work the fastest
y_train = train_ds.to_pandas()['metadata'].apply(lambda x: x['class'])
X_train = pd.DataFrame.from_records(train_ds.to_pandas()['data'])

y_test = test_ds.to_pandas()['metadata'].apply(lambda x: x['class'])
X_test = pd.DataFrame.from_records(test_ds.to_pandas()['data'])

```

Please refer to the [original metadata](http://dataome.mensxmachina.org/) for the list of available datasets.

## Disclaimer

BioDataome and its content are provided as is without any warranty of any kind, that BioDataome or any documents available from this server will be error free. In no event will its members be liable for any damages, arising out of, resulting from, or in any way connected with the use of BioDataome or documents available from this server.

BioDataome is restricted to research and educational use. The information you may retrieve and recover from BioDataome is not designed to diagnose, prevent, or treat any condition or disease

Part of research that led to the development of BioDataome has received funding from the European Research Council under the European Union's Seventh Framework Programme (FP/2007-2013) / ERC Grant Agreement n. 617393.

Part of the analyses results and the implementation of the web interface were funded by the “ELIXIR-GR: Managing and Analysing Life Sciences Data (MIS: 5002780)” Project, co-financed by Greece and the European Union - European Regional Development Fund.