DOM Formula Assignment

A machine learning pipeline for assigning molecular formulas to dissolved organic matter (DOM) mass spectrometry data using K-Nearest Neighbors (KNN) models.

Overview

This research implements an automated pipeline for formula assignment in DOM (Dissolved Organic Matter) mass spectrometry analysis. It uses KNN models to predict molecular formulas based on mass-to-charge (m/z) and m/z error (for filtering and picking up the formula), supporting multiple training datasets, distance metrics, and K-neighbor configurations.

• Python 3.8+
• Required packages: pandas, numpy, scikit-learn, matplotlib, seaborn, wordcloud, joblib

Basic Usage

from pipeline import run_main
# Run all standard pipelines (L1, L3, L1-L3, Synthetic)
results = run_main()

Data

The data is organized into the following folders and files: The L1 is the data from the 7T with mass resolution of 1 PPM, L2 is the data from the 9.4T instrument with mass resolution of 0.2-0.4 PPM, and L3 is the data from 21T with mass resolution of 0.15 PPM. The synthetic_data folder contains synthetically generated formulas for training.

• readme.txt
• train.txt (L1 with Mobility Features)
• test.txt (L2-v2 with Mobility Features)
• DOM_testing_set (L2)
• DOM_testing_set_Peaklists (L2-Peaklists)
• DOM_training_set_ver2 (L1)
• DOM_training_set_ver3 (L3)
• synthetic_data (Synthetic)

Model Configurations

The pipeline includes four standard model configurations:

1. Model-L1

• Training Data: DOM_training_set_ver2 (L1 data)
• Description: Model trained on L1 mass spectrometry data

2. Model-L3

• Training Data: DOM_training_set_ver3 (L3 data)
• Description: Model trained on L3 mass spectrometry data

3. Model-L1-L3 (Ensemble)

• Training Data: Both ver2 and ver3
• Description: Ensemble model combining L1 and L3 data

4. Model-Synthetic (Ensemble)

• Training Data: Combined L1, L3, and synthetic data
• Description: Enhanced with synthetically generated formulas

Each model is trained with multiple configurations:

• K values: 1, 3 (number of nearest neighbors)
• Distance metrics: Euclidean (p=2), Manhattan (p=1)

This results in 16 model variants (4 models × 2 K values × 2 metrics).

Output Files

Per-Test-File Results (results_*.csv):

Evaluation Summary (evaluation_summary_stats.csv):

Peak List Predictions (peak_list/*.csv):

Usage Examples

Example 1: Basic Pipeline Execution

from pipeline import run_main

# Run all standard pipelines (7T, 21T, Combined, Synthetic)
results = run_main()

Example 2: Custom Pipeline

from pipeline import PipelineManager

manager = PipelineManager()

# Train with custom data sources 
results = manager.run_custom_pipeline(
    version_name="Custom_Model",
    training_folders=[
        "data/custom_training/curated_formulas.csv"  # Additional file
    ],
    model_path="models/custom_model.joblib",
    result_dir="output/custom/test_results",
    k_neighbors=3,
    metric='minkowski',
    p=2  # Euclidean distance
)

License and Usage Terms

This model and associated code are released under the CC-BY-NC-ND 4.0 license and may only be used for non-commercial, academic research purposes with proper attribution. Any commercial use, sale, or other monetization of this model and its derivatives, which include models trained on outputs from the model or datasets created from the model, is prohibited and requires prior approval. If you are a commercial entity, please contact the corresponding author.

Contact

For any additional questions or comments, contact Fahad Saeed (fsaeed@fiu.edu).