https://github.com/ttsudipto/recurrence-pred-genomics

ML-based prediction of NSCLC recurrence with gene expression data
https://github.com/ttsudipto/recurrence-pred-genomics

boruta gene-expression imbalanced-learn machine-learning mcfs multilayer-perceptron non-small-cell-lung-cancer python r random-forest recurrence-prediction rna-seq scikit-learn smote support-vector-machine

Last synced: about 1 month ago
JSON representation

ML-based prediction of NSCLC recurrence with gene expression data

• Host: GitHub
• URL: https://github.com/ttsudipto/recurrence-pred-genomics
• Owner: ttsudipto
• License: gpl-3.0
• Created: 2022-08-26T06:08:47.000Z (almost 4 years ago)
• Default Branch: main
• Last Pushed: 2024-07-23T12:44:42.000Z (almost 2 years ago)
• Last Synced: 2026-04-30T16:33:26.218Z (about 1 month ago)
• Topics: boruta, gene-expression, imbalanced-learn, machine-learning, mcfs, multilayer-perceptron, non-small-cell-lung-cancer, python, r, random-forest, recurrence-prediction, rna-seq, scikit-learn, smote, support-vector-machine
• Language: Python
• Homepage:
• Size: 11.1 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

README

          
# ML-based prediction of non-small cell lung cancer (NSCLC) recurrence with gene expression data

 **Cite as:**

>Bhattacharjee, S., Saha, B., & Saha, S. (2023). Prediction of Recurrence in

Non Small Cell Lung Cancer Patients with Gene Expression Data Using Machine

Learning Techniques. In *2023 International Conference on Computer, Electrical

& Communication Engineering (ICCECE)*, 1–8.

[https://doi.org/10.1109/ICCECE51049.2023.10085448](https://doi.org/10.1109/ICCECE51049.2023.10085448).

## Dataset

The data was obtained from a publicly available dataset, *NSCLC-Radiogenomics*

([Bakr *et al.*, 2018](https://doi.org/10.1038/sdata.2018.202)). The gene

expression data was obtained from Gene Expression Omnibus (accession number:

[GSE103584](https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE103584)).

The supplementary data are available at:

[http://dibresources.jcbose.ac.in/ssaha4/lcr-iccece-2023](http://dibresources.jcbose.ac.in/ssaha4/lcr-iccece-2023).

## 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.*