Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
An image representation based convolutional network for DNA classification
Authors: Bojian Yin, Marleen Balvert, Davide Zambrano, Alexander Schoenhuth, Sander Bohte
ICLR 2018 | Venue PDF | 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 Tensor๏ฌow (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. |