ESM-1v
基于 ESM 框架的蛋白质变异效应预测方法，利用语言模型的似然度评估 氨基酸替换对蛋白质功能的影响。该方法无需训练即可在零样本模式下预测 致病性变异，性能接近实验测量。
| Property | Value |
|---|---|
| Purpose | 基于语言模型的蛋白质变异效应零样本预测 |
| Time Complexity | O(n^2 * d) |
| Space Complexity | O(n^2) |
| Year | 2021 |
| Difficulty | Intermediate |
| Languages | Python |
| Category | Protein Language Model |
Complexity Analysis
- Time Complexity:
O(n^2 * d) - Space Complexity:
O(n^2)
Performance Insight: The time complexity of this algorithm is polynomial. High space complexity; consider Hirschberg-style space-optimized variants for very long sequences.
Note: Complexity analysis is based on theoretical models. Actual runtime is affected by input scale, hardware, and implementation optimizations. Benchmark for your specific workload.
Literature & Implementation
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