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.

Dataset

The data was obtained from a publicly available dataset, NSCLC-Radiogenomics (Bakr et al., 2018). The gene expression data was obtained from Gene Expression Omnibus (accession number: GSE103584).

The supplementary data are available at: http://dibresources.jcbose.ac.in/ssaha4/lcr-iccece-2023.

Team

• Sudipto Bhattacharjee (ttsudipto@gmail.com)
  Ph.D. Scholar,
  Department of Computer Science and Engineering, University of Calcutta, Kolkata, India.
  
• Dr. Banani Saha (bsaha_29@yahoo.com)
  Associate Professor,
  Department of Computer Science and Engineering, University of Calcutta, Kolkata, India.
• Dr. Sudipto Saha (ssaha4@jcbose.ac.in)
  Associate Professor,
  Department of Biological Sciences, Bose Institute, Kolkata, India.

Please contact Dr. Sudipto Saha regarding any further queries.