CrossBind: Collaborative Cross-Modal Identification of Protein Nucleic-Acid-Binding Residues
Authors: Linglin Jing, Sheng Xu, Yifan Wang, Yuzhe Zhou, Tao Shen, Zhigang Ji, Hui Fang, Zhen Li, Siqi Sun
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experimental results demonstrate that our approach outperforms the next best state of the art methods, Graph Site and Graph Bind, on DNA and RNA datasets by 10.8/17.3% in terms of the harmonic mean of precision and recall (F1 Score) and 11.9/24.8% in Matthews correlation coefficient (MCC), respectively. |
| Researcher Affiliation | Collaboration | 1Shanghai Artifcial Intelligence Laboratory 2Department of Computer Science, Loughborough University 3Research Institute of Intelligent Complex Systems, Fudan University 4 SSE & FNII, The Chinese University of Hong Kong (Shenzhen) 5Shanghai Jiao Tong University |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | We release the code at https://github.com/BEAM-Labs/Cross Bind. |
| Open Datasets | Yes | We utilized two benchmark datasets, DNA 129 and RNA 117 dataset, from a previous study for training and testing our method. These datasets were obtained from the Bio Lip database (Yang, Roy, and Zhang 2012) and consist of experimentally determined complex structures. |
| Dataset Splits | Yes | We trained and validated our method on the training dataset, with a validation ratio of 0.1. |
| Hardware Specification | No | The paper does not specify any hardware details such as GPU models, CPU types, or memory used for running the experiments. |
| Software Dependencies | No | The paper mentions "Adam optimizer" and "cosine annealing" but does not specify version numbers for any software dependencies, libraries, or programming languages used (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | Yes | In all experiments, we used the Adam optimizer with a weight decay of 1 10 4 and employed cosine annealing as the learning rate scheduler. We set the initial learning rate to 1 10 3 for the ALS module and 1 10 4 for the cross-modal module. In the RPF module, we ranked the nearest neighbors and selected the top five amino acids for the propensity filter using prediction logits with thresholds of [-0.8, 0.8], corresponding to a positive over a negative propensity. |