# COVID-19 Bioinformatics scRNA Target Challenge BHI — Data

来源：https://www.kaggle.com/competitions/covid-19-bioinformatics-scrna-target-challenge-bhi/data

本地 Meta Kaggle 快照；不是本次实时页面。

# Dataset Description

# Description

The dataset “scBALF-COVID-19” is derived from public data[1] and contains a set of Broncho Alveolar Lavage Fluid (BALF) cells from patients categorized clinically as having mild, severe, or no COVID-19 infection.

1. In detail, scBALF-COVID-19 contains scRNA-Seq data from three types of patients categorized clinically as having: mild infection (M# of cells), severe infection (S# of cells), or no infection (N# of cells).

2. The dataset consists of one count matrix with T total number of cells and G total number of genes (Figure 1a) and a labeling matrix with labels for each cell as normal, mild, or severe (Figure 1b).

3. The dataset has been normalized for technical differences between patients (batch normalization) as well as sequencing depth differences between cells in each patient. This means that no normalization is required but participants may implement further normalization in order to improve results.

# Usage

1. We suggest utilizing the scBALF-COVID-19 dataset to develop an AI model to identify COVID-19 severity (Figure 1c).

2. The dataset is imbalanced between the three classes, reflecting a practical problem in applying machine or deep learning techniques.

3. The challenge encourages the design of interpretable machine learning models in order to obtain putative molecular markers of COVID-19 severity.

#Performance Evaluation:
Performance Evaluation:
1)     Please register your team 
2)     Each member must register 
3)     The performance of each model will be judged by a committee of judges. The winners will be decided based on:
a. Mean ROC, that is treat every label as one vs. rest and compute ROC and then average them.
b. Interpretability of the developed models to identify molecular signatures. 
4)     Results of the model classifier must be posted here
 

•	 Each competitor/group should submit through this website an up to 4-page paper summarizing the method and results, following the standard IEEE format for conference paper submissions. 

•	Provide the URL to your GitHub repo, where your code can be accessed, and the prediction results can be reproduced.

•	 Submit the prediction results on the test set (in CSV format).
  
References:
 ADDIN EN.REFLIST [1]       M. Liao et al., "Single-cell landscape of bronchoalveolar immune cells in patients with COVID-19," Nat Med, vol. 26, no. 6, pp. 842-844, Jun 2020, doi: 10.1038/s41591-020-0901-9.