Deep Recurrent Neural Network-Based Identification of Precursor microRNAs
Authors: Seunghyun Park, Seonwoo Min, Hyun-Soo Choi, Sungroh Yoon
NeurIPS 2017 | Conference PDF | Archive PDF | Plain Text | 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. |