[a.a. 22/23] Gaetano Antonucci & Nicola Pagliara
Project developed as part of course "Strumenti formali per la bioinformatica" of the Computer Science Department (Università degli Studi di Salerno)
The aim of this work is to replicate and improve the work cited in the Reference section below.
To create the environment used to perform the tests we used Anaconda (python 3.9) with the following packages:
| Package | Version |
|---|---|
| numpy | 1.23.5 |
| pandas | 1.5.2 |
| pytorch | 1.13.1 CUDA (ver. 11.6) |
| matplotlib | 3.5.3 |
| scikit-learn | 1.2.1 |
| scikit-image | 0.19.3 |
| imbalanced-learn | 0.10.1 |
| opencv | 4.7.0 |
Data was obtained from GDAC FireHose (managed by Broad Institute)
Just for simplicity we include the link supplied by B.Lyu and A. Haque from which we downloaded the raw data and the annotation file needed in preprocessing: https://drive.google.com/drive/folders/1LfOiyMgnoQy3jaJ37jLeARfw7riLwkyW
Our elaboration of data is available at: https://drive.google.com/drive/folders/1mn8Sis3rsQAAaAHoALNKTtFy3mwOoUYg
To run the application, please check the paths in every script and then run the scripts by numeric order.
NOTE: please, remind that raw_data.py was used only in binary and ternary test.
All tests was performed on a HP Server with motherboard HPE ProLiant ML350 Gen 10 in this setup:
| Operative System | Ubuntu 21.10 (64-bit) |
| CPU | Intel(R) Xeon(R) Gold 5218 CPU @ 2.30Ghz (x32) |
| RAM | 270 GB |
| GPU | NVIDIA Quadro RTX 4000 (8 GiB of dedicated memory) [CUDA ver. 11.6] |
Our application was structured in modules as follows:
Application's output files was organized as follows:
This work is based on the work available at: https://dl.acm.org/citation.cfm?id=3233588
Boyu Lyu and Anamul Haque. 2018. Deep Learning Based Tumor Type Classification Using Gene Expression Data. In Proceedings of the 2018 ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics (BCB '18). ACM, New York, NY, USA, 89-96. DOI: https://doi.org/10.1145/3233547.3233588