PhageCGRNet: CNN Architecture Configuration for Phage Host Prediction

A detailed configuration file for the PhageCGRNet Convolutional Neural Network model. The file, created by Ting Wang and last updated in April 2026, describes a novel method for predicting phage hosts by combining k-mer frequency and positional information into a 3D feature matrix. The model was tested on two benchmark datasets and reportedly achieved higher accuracy at species and genus taxonomy levels compared to other methods.

Use Cases

• Replicating the PhageCGRNet model architecture based on the described configuration.
• Training a CNN for phage host prediction using combined k-mer frequency and positional features.
• Benchmarking new phage classification methods against the PhageCGRNet approach.
• Studying feature engineering techniques for genomic sequences using Chaos Game Representation.

Strengths

• Model configuration is explicitly linked to a published method that reportedly outperformed other state-of-the-art techniques.
• File is licensed under CC-BY-4.0, permitting open sharing and adaptation.
• Dataset is very small (5.5 KB), ensuring quick download and inspection.

Limitations

• Row count is unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.
• The dataset contains only the model configuration (5.5 KB), not the training data or results.

Provenance

Source

figshare, author Ting Wang.

Collection Method

Likely contains the architectural parameters and configuration for the PhageCGRNet CNN model described in the associated research.

Time Range

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Freshness

Last updated 2026-04 17:24:58; freshness should be verified.

Geography

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