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Analysis 3: Aging-Specific Test Set

📂 Source Code & Notebooks: pipeline/08_evaluation_analysis/Agingonly_testing_data

Evaluation of KGs on Aging-Specific Test Set

3.1. Purpose

The purpose of this analysis is to evaluate how integrating aging-specific data with general biomedical knowledge and orthology mappings in EvoAge helps predict aging test sets. By comparing EvoAge against Aging-only graphs on the same aging test split (Aging_specific_1to1_KG_test_10.txt), we quantify how the broader semantic context and cross-species links in EvoAge help enrich and improve the representation of aging-specific biology.

3.2. Pipeline Overview

All pipeline components for this analysis are structured under: 📂 Agingonly_testing_data/

Agingonly_testing_data/
│
├── Aging_testing_data_on_allKGs.sh           ← Sequentially runs dglke_eval link prediction
├── Aging_testing_data_on_allKGs.log          ← Combined output logs of evaluation runs
└── Aging_testing_data_on_allKG_compling.py   ← Script that parses logs to compile rescal_testing_summary.csv

3.3. Evaluation Setup

  • Models Evaluated: RESCAL models trained on:
  • EvoAge 1-to-1 (trained on EvoAge_1to1_KG_train_90.txt + valid_10.txt)
  • Aging 1-to-1 (trained on Aging_specific_1to1_KG_train_80.txt + valid_10.txt)
  • EvoAge 1:1∪1:M (trained on EvoAge_121_12M_KG_train_90.txt + valid_10.txt)
  • Aging 1:1∪1:M (trained on Aging_specific_121_12M_KG_train_90.txt + valid_10.txt)
  • Shared Test Set: Aging_specific_1to1_KG_test_10.txt (148,311 triples).
  • Parameters: Hidden dimension of 64, gamma margin of 12.0, evaluated via CPU link prediction with 22 threads, using 16 negative samples.
  • Log compiler script: Aging_testing_data_on_allKG_compling.py extracts the test metrics from the log files and outputs a compiled CSV summary.