Paired-Sample and Pathway-Anchored MLOps Framework for Robust Transcriptomic Machine Learning in Small Cohorts: Model Classification Study | JoVE Visualize

Area of Science:

Genomics and Computational Biology
Machine Learning in Medicine
Rare Disease Research

Background:

Over 90% of human diseases are rare, affecting millions globally and posing challenges for research.
Low disease prevalence limits cohort sizes, hindering the development of robust transcriptome-based machine learning (ML) classifiers.
Standard ML models require large cohorts (>100 participants) for accuracy, which is infeasible for rare diseases with small patient groups, leading to overfitting.

Purpose of the Study:

To develop an ML classification method that overcomes cohort size limitations in rare disease research.
To integrate paired-sample transcriptome dynamics, N-of-1 pathway analytics, and MLOps for robust classification.
To enhance the accuracy and interpretability of ML models for high-dimensional transcriptomic data.

Main Methods:

Utilized within-subject paired-sample transcriptome data to control for individual variability and improve signal-to-noise ratio.
Implemented N-of-1 pathway-level analytics to reduce high-dimensional transcriptomic profiles into interpretable biological features.
Integrated reproducible machine learning operations (MLOps) for automated versioning, monitoring, and hyperparameter tuning to enhance model generalization.

Main Results:

Achieved 90% precision and recall in breast cancer classification and 92% precision with 90% recall in rhinovirus infection classification.
Paired-sample dynamics improved precision by up to 12% and recall by 13% in breast cancer, and 5% in rhinovirus.
MLOps workflows increased accuracy by ~14.5% compared to traditional methods, identifying key biological pathways for disease classification.

Conclusions:

The integrated approach of intrasubject dynamics, pathway-level feature reduction, and MLOps effectively addresses cohort size limitations in rare disease transcriptomic classification.
This method offers a scalable and interpretable solution for analyzing high-dimensional transcriptomic data in rare diseases.
Future research will focus on applying these advances to diverse therapeutic areas and small cohort study designs.