DLAM: Essential Protein Predictions from Multi-Source Biological Data

DLAM is a deep learning model that predicts protein essentiality by integrating four biological cues and a weighted protein-protein interaction network. The 2.7 MB Excel file contains results from evaluations on the DIP and BioGRID datasets, showing strong performance metrics like ROC-AUC and AP. Authored by Shunxian Zhou and last updated in April 2026, it is shared under a CC-BY-4.0 license.

Use Cases

• Training deep learning models for protein essentiality prediction based on integrated biological cues.
• Benchmarking new prediction algorithms against DLAM's reported performance metrics.
• Analyzing the relationship between protein interaction topology and essentiality.
• Studying class imbalance handling in biological classification tasks.

Strengths

• Model performance is quantitatively reported with metrics like ROC-AUC, AP, and F1-score.
• Evaluation was conducted on two established biological datasets: DIP and BioGRID.
• Results are shared under a permissive CC-BY-4.0 license.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• The dataset is small at 2.7 MB, suggesting it contains results or summary data rather than raw training data.

Provenance

Source

figshare, authored by Shunxian Zhou.

Collection Method

Results from a computational study introducing the DLAM deep learning framework.

Time Range

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Freshness

Last updated 2026-04-14 22:01:27; freshness should be verified.

Geography

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