This is the webpage made for the Data Analysis Project of the Software Applications course of Genomics.
Here you can explore the website.
The COVID-19 DisGeNET data collection is the result of applying state-of-the art text mining tools developed by MedBioinformatics solutions to the LitCovid dataset Chen et al., 2020, to identify mentions of diseases, signs and symptoms. The LitCovid dataset contains a selection of papers referring to Coronavirus 19 disease.
The goal of the project is to write a program able to analyse two datasets about diseases and genes obtained from the COVID-19 DisGeNET data collection and present the results in a web-based user interface.
To install the program download the .zip and extract it, then open a terminal window from the installation folder and execute:
pip install -r requirements.txt
To start the program type in the terminal:
python main.py
-
Flask : to manage the website.
-
Flaskpaginate : to render the pagination in some webpages.
-
Flaskcaching : to make use of cache files.
-
Pandas : to execute operations on the datasets.
-
Bulma : css framework used for the website.
The program is divided in five components:
-
main.py Is the part that start the execution of the program
-
mediator.py Is the part1 described in the project specifications which connects part2 and part3
-
functions.py Is the part2 described in the project specifications which contains all the operations to perform on the two datasets
-
website.py Is the part3 described in the project specifications which creates the webpage, get the inputs from the user and presents the results
-
settings.py Contains the global variables of the program like the path to the datasets
This is the homepage
By cliking on Functions you can get a list of all the functions this webapp is capable of:
For example you can get all the distinct correlations bewteen genes and diseases:
Or you can search all the diseases associated with a gene and viceversa:
-
Alberto Notarnicola
-
Alessandro Poletti
-
Isidora Gocmanac
-
Shanuka Tenahandi