SignalP
基于深度神经网络的信号肽预测工具，能够准确识别蛋白质 N 端的信号肽序列及其剪切位点。 该方法利用深度学习模型显著提升了信号肽预测的灵敏度和精确度，适用于分泌蛋白的高通量筛选。
| Property | Value |
|---|---|
| Purpose | 利用深度学习预测蛋白质信号肽及剪切位点 |
| Time Complexity | O(n) |
| Space Complexity | O(n) |
| Year | 2019 |
| Category | Functional Annotation |
Complexity Analysis
- Time Complexity:
O(n) - Space Complexity:
O(n)
Performance Insight: The time complexity of this algorithm is linear (O(n)), scales linearly to TB-scale data and is suitable for streaming pipelines. Linear space can often be reduced by constant factors via sliding-window techniques.
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
Related Tools
TMHMM · Phobius · DeepSig