GreenGenomicsLab / TARA-XGBoost-Bidirectional

TARA-XGBoost-Bidirectional

Bidirectional XGBoost ensemble models linking marine environmental variables to microalgal protein domain (Pfam) abundance profiles from the TARA Oceans metagenomic dataset.

Model Description

Forward Models (Environment → Pfam)

• Input : 32 Google Earth Engine oceanographic variables
• Output : CLR-transformed abundance of 100 top-variance Pfam domains
• 100 independent XGBoost regressors (one per target domain)
• Median test R² = 0.20 (IQR: 0.16–0.29; max R² = 0.54)

Reverse Models (Pfam → Environment)

• Input : 9,611 Pfam domain abundances (100 PCA components, 72.2% variance)
• Output : 31 environmental variables
• 31 independent XGBoost regressors (one per target variable)
• Best targets: MODIS SST (R² = 0.61), mean SST (R² = 0.61), bathymetry (R² = 0.53)
• Independent-subset validation: bathymetry R² = 0.25 across disjoint ocean basins

XGBoost Hyperparameters

Files

• xgboost_forward_models_20260124_104452.joblib — 100 forward models (49 MB)
• xgboost_reverse_models_20260124_104452.joblib — 31 reverse models (15 MB)
• model_manifest_20260124_104452.json — Feature lists and hyperparameters

Usage

import joblib

# Load reverse models (Pfam → Environment)
reverse_bundle = joblib.load("xgboost_reverse_models_20260124_104452.joblib")

# Load forward models (Environment → Pfam)
forward_bundle = joblib.load("xgboost_forward_models_20260124_104452.joblib")

Dataset

Trained on AlgaGPT-extracted proteomes from 2,044 TARA Oceans metagenomic assemblies. Environmental variables from Google Earth Engine (GEE) for 1,279 samples with complete metadata.

Related Models

• GreenGenomicsLab/algaGPT — AlgaGPT protein classification model
• GreenGenomicsLab/LA4SR-Pythia70m-b-ckpt-55000 — LA4SR-Pythia classification model
• GreenGenomicsLab/TARA-ELF-NET — Deep neural network bidirectional models

Citation

LA4SR classification models:

Nelson DR, Jaiswal AK, Ismail NS, Mystikou A, Salehi-Ashtiani K. Patterns . 2024;6(11).

License

Apache 2.0