Reverse-Complement Equivariant Networks for DNA Sequences
Authors: Vincent Mallet, Jean-Philippe Vert
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We show experimentally that the new architectures can outperform existing ones. We assess the performance of various equivariant architectures on a set of three binary prediction and four sequence prediction problems used by Zhou et al. [45] |
| Researcher Affiliation | Collaboration | Vincent Mallet Structural Bioinformatics Unit, Department of Structural Biology and Chemistry, Institut Pasteur, CNRS UMR3528, C3BI, USR3756 Mines Paris Tech, PSL University, Center for Computational Biology vincent.mallet96@gmail.com Jean-Philippe Vert Google Research, Brain team, Paris jpvert@google.com |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Finally, we implemented new linear and nonlinear equivariant layers and make all these equivariant layers available in Keras [5] and Pytorch [31].3 code available at https://github.com/Vincentx15/Equi-RC |
| Open Datasets | Yes | We assess the performance of various equivariant architectures on a set of three binary prediction and four sequence prediction problems used by Zhou et al. [45] to assess the performance of RCPS networks. The binary classification problems aim to predict if a DNA sequence binds to three transcription factors (TFs), based on genome-wide binarized TF-Ch IP-seq data for Max, Ctcf and Spi1 in the GM12878 lymphoblastoid cell-line [34]. |
| Dataset Splits | No | The paper mentions using a validation set for hyperparameter tuning, but does not provide specific details on the dataset splits (percentages or counts) in the main text. |
| Hardware Specification | Yes | All experiments were run on a single GPU (either a GTX1080 or a RTX6000), with 20 CPU cores. |
| Software Dependencies | No | The paper mentions Keras [5] and Pytorch [31] as frameworks used but does not provide specific version numbers for any software dependencies. |
| Experiment Setup | Yes | We train equivariant models with different combinations of hyperparameters on the training set and assess their performance on the validation set, repeating the process ten times with different random seeds. ... We combine the regular and '+1' dimensions with Re Lu activations and the '-1' dimensions with a tanh activation. |