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..
Deep Recurrent Neural Network-Based Identification of Precursor microRNAs
Authors: Seunghyun Park, Seonwoo Min, Hyun-Soo Choi, Sungroh Yoon
NeurIPS 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In our experiments with recent benchmarks, the proposed approach outperformed the compared state-of-the-art alternatives in terms of various performance metrics. |
| Researcher Affiliation | Academia | Seunghyun Park Electrical and Computer Engineering Seoul National University Seoul 08826, Korea School of Electrical Engineering Korea University Seoul 02841, Korea |
| Pseudocode | Yes | The pseudocode of our approach is available as Appendix A, in the supplementary material. |
| Open Source Code | Yes | The source code for the proposed method is available at https://github.com/eleventh83/deep Mi RGene. |
| Open Datasets | Yes | We used three public benchmark datasets [4] named human, cross-species, and new. The positive pre-mi RNA sequences in all three datasets were obtained from mi RBase [25] (release 18). For the negative training sets, we obtained noncoding RNAs other than pre-mi RNAs and exonic regions of protein-coding genes from NCBI (http://www.ncbi.nlm.nih.gov), f RNAdb [23], NONCODE [24], and sno RNA-LBME-db [26]. |
| Dataset Splits | Yes | Using the remaining 90% of each dataset, we carried out five-fold cross-validation for training and model selection. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU/CPU models, memory, or processor types used for running the experiments. |
| Software Dependencies | No | The paper mentions tools like RNAfold and the Adam optimizer but does not specify version numbers for any software dependencies. |
| Experiment Setup | Yes | In the LSTM layers, a dropout parameter for input gates and another for recurrent connection were both set to 0.1. In the FC layers, we set the dropout parameter to 0.1. [...] The number of hidden nodes in the LSTM (d1, d2) and the FC (d3, d4) layers were determined by cross validation as d1 = 20, d2 = 10, d3 = 400, and d4 = 100. The mini-batch size and training epochs were set to 128 and 300 respectively. |