An image representation based convolutional network for DNA classification
Authors: Bojian Yin, Marleen Balvert, Davide Zambrano, Alexander Schoenhuth, Sander Bohte
ICLR 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments show that the method outperforms several existing methods both in terms of prediction accuracy and training time. |
| Researcher Affiliation | Academia | 1Centrum Wiskunde & Informatica (CWI), Amsterdam, The Netherlands 2Department of Biology, University of Utrecht, The Netherlands |
| Pseudocode | No | The paper does not contain any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | Yes | The complete model is presented in Table 1, and code is available on Github (https://github.com/Bojian/Hilbert-CNN/tree/master). |
| Open Datasets | Yes | We test the performance of our approach using ten publicly available datasets from Pokholok et al. (2005). |
| Dataset Splits | Yes | A randomly chosen 90% of the dataset is used for training the network, 5% is used for validation and early stopping, and the remaining (5%) is used for evaluation. |
| Hardware Specification | No | The paper does not specify the hardware used for running the experiments (e.g., GPU/CPU models, memory details). |
| Software Dependencies | No | The LSTM model was implemented in Keras (Chollet et al., 2015), all other models were implemented in Tensorflow (Abadi et al., 2015). (No version numbers provided for Keras or Tensorflow). |
| Experiment Setup | Yes | The learning rate is set to 0.003, the batch-size was set to 300 samples and the maximum number of epochs is 10. ... The complete model is presented in Table 1. |