https://github.com/ttsudipto/sdldpred
SDLDpred - Symptom-based Drugs of Lifestyle-related Diseases prediction
https://github.com/ttsudipto/sdldpred
birch bisecting-kmeans clustering css drug-prediction drug-symptom-associations html js kmeans lifestyle-diseases machine-learning mean-shift php scikit-learn semantic-similarity symptoms web-application
Last synced: 2 months ago
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SDLDpred - Symptom-based Drugs of Lifestyle-related Diseases prediction
- Host: GitHub
- URL: https://github.com/ttsudipto/sdldpred
- Owner: ttsudipto
- License: gpl-3.0
- Created: 2023-05-19T08:48:11.000Z (about 3 years ago)
- Default Branch: main
- Last Pushed: 2024-07-01T10:26:43.000Z (almost 2 years ago)
- Last Synced: 2025-07-22T04:49:38.783Z (11 months ago)
- Topics: birch, bisecting-kmeans, clustering, css, drug-prediction, drug-symptom-associations, html, js, kmeans, lifestyle-diseases, machine-learning, mean-shift, php, scikit-learn, semantic-similarity, symptoms, web-application
- Language: Python
- Homepage: http://bicresources.jcbose.ac.in/ssaha4/sdldpred/
- Size: 20.1 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# SDLDpred - Symptom-based Drugs of Lifestyle-related Diseases prediction
SDLDpred is a web-based tool to predict drugs of lifestyle-related diseases using symptoms
as features.
It uses an unsupervised machine learning model trained using Bisecting K-Means algorithm to
perform the prediction. The model was trained with *novel drug-symptom associations* computed
from the disease-symptom and drug-disease association data of *143 lifestyle-related diseases*,
*1271 drugs* and *305 symptoms*.
**Cite as:**
>Bhattacharjee, S., Saha, B., & Saha, S. (2024). Symptom-based drug prediction of
lifestyle-related chronic diseases using unsupervised machine learning techniques. *Computers
in Biology and Medicine*, 174, 108413.
[https://doi.org/10.1016/j.compbiomed.2024.108413](https://doi.org/10.1016/j.compbiomed.2024.108413).
## Using the tool
SDLDpred is available at: [http://bicresources.jcbose.ac.in/ssaha4/sdldpred](http://bicresources.jcbose.ac.in/ssaha4/sdldpred).
To know more about the datasets and the methodology, please refer to the
[About](http://bicresources.jcbose.ac.in/ssaha4/pulmopred/about.html) page. Please refer to
the [Help](http://bicresources.jcbose.ac.in/ssaha4/pulmopred/help.html) page for understanding
the inputs and outputs to the web application.
## Development
Python libraries used :
* numpy (Version `1.24.1`)
* scikit-learn (Version `1.2.1`)
* joblib (Version `1.2.0`)
* scipy (Version `1.10.1`)
* ssmpy (Version `0.2.5`)
R libraries used :
* GOSemSim (Version `2.26.0`)
* clusterProfiler (Version `4.8.1`)
* fmcsR (Version `1.42.0`)
* ggplot2 (Version `3.4.2`)
* ggpubr (Version `0.6.0`)
* patchwork (Version `1.1.2`)
* pheatmap (Version `1.0.12`)
The web application is deployed in an Apache HTTP server.
## Team
* **Sudipto Bhattacharjee** *([ttsudipto@gmail.com](mailto:ttsudipto@gmail.com))*
Ph.D. Scholar,
Department of Computer Science and Engineering,
University of Calcutta, Kolkata, India.
* **Dr. Banani Saha** *([bsaha_29@yahoo.com](mailto:bsaha_29@yahoo.com))*
Associate Professor,
Department of Computer Science and Engineering,
University of Calcutta, Kolkata, India.
* **Dr. Sudipto Saha** *([ssaha4@jcbose.ac.in](mailto:ssaha4@jcbose.ac.in))*
Associate Professor,
Department of Biological Sciences,
Bose Institute, Kolkata, India.
*Please contact Dr. Sudipto Saha regarding any further queries.*
*This tool is strictly for research use only. It should be used for medical purposes only by consulting with doctors.*