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--- |
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license: mit |
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task_categories: |
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- feature-extraction |
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tags: |
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- biology |
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--- |
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# SequenceDistanceDataset (SDD) |
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**A Cross-Domain Benchmark for Sequence Similarity Analysis** |
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## Overview |
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A comprehensive benchmarking dataset for evaluating distance metrics in two domains: |
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- 🧬 **Biological Sequences** (Proteins from UniProt/UniRef) |
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- 🗺️ **Movement Trajectories** (GPS data from 3 cities) |
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Designed to support research in similarity search, metric learning, and cross-domain analysis. |
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--- |
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## Key Features |
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✅ **Precomputed Distance Matrices** |
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- Eliminates computation overhead for direct benchmarking |
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- Includes both training and test sets |
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✅ **Curated Data Splits** |
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- Standardized `train`/`query`/`base` splits for retrieval tasks |
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- Reproducible evaluation protocols |
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✅ **Diverse Metrics** |
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| Domain | Metrics | |
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|-----------------|--------------------------------------| |
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| **Proteins** | Edit Distance (ED), Needleman-Wunsch (NW) | |
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| **Trajectories**| DTW, Hausdorff, Fréchet, EDR, EDwP | |
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✅ **Real-World Scale** |
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- Contains large mobility datasets (e.g., Porto taxi trajectories) |
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- Represents diverse geographical contexts (Beijing, Chengdu, Portugal) |
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--- |
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## Dataset Structure |
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### Protein Domain |
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```plaintext |
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Protein/ |
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├── {UniProt, UniRef}/ |
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│ ├── train_list.txt |
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│ ├── query_list.txt |
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│ ├── base_list.txt |
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│ └── {ED, NW}/ |
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│ ├── {train/test}_distance_matrix_result |
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``` |
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### Trajectory Domain |
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```plaintext |
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Trajectory/ |
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├── {Geolife, Porto, Chengdu, TrajCL_Porto}/ |
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│ ├── train/ |
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│ ├── query/ |
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│ ├── base/ |
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│ └── {DTW, Haus, DFD, EDR, EDwP}/ |
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│ ├── {train/test}_distance_matrix_result |
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``` |
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## Use Cases |
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### 🔬 Bioinformatics |
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- Compare protein alignment algorithms |
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- Study sequence homology detection |
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### 🚕 Urban Computing |
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- Evaluate trajectory similarity methods |
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- Analyze mobility pattern variations |
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### 🤖 Machine Learning |
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- Train distance metric learning models |
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- Benchmark neural embedding methods |
