MLASDO: a software tool to detect and explain clinical and omics inconsistencies applied to the Parkinson's Progression Markers Initiative cohort

2025-07-04

AI Summary

Clinical phenotypes and omics profiles often exhibit discordance in medical cohorts, hindering early diagnosis and precise molecular subtyping. To address this, we propose a three-stage machine learning framework: (1) identifying transcriptomic outliers using support vector machines; (2) integrating clinical annotations and domain knowledge to ensure interpretable classification; and (3) implementing an open-source R package for reproducible analysis. Applied to the Parkinson’s Progression Markers Initiative (PPMI) cohort, our method detected 15 neurologically healthy controls exhibiting Parkinson’s-like transcriptional signatures and 22 Parkinson’s disease patients displaying transcriptionally normal profiles. Subsequent immunophenotyping revealed significantly altered peripheral immune cell proportions in these discordant individuals (p < 0.001). By decoupling molecular profiling from symptomatic presentation, our approach overcomes symptom-dependency limitations and establishes a novel paradigm for pre-symptomatic risk stratification and data-driven molecular subtyping.

Technology Category

• Machine Learning: Neuro-Symbolic Learning
• Data Mining & Knowledge Management: Anomaly/Outlier Detection
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Web Mining and Content Analysis: Machine learning and data science for the Web
• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs

Abstract

Inconsistencies between clinical and omics data may arise within medical cohorts. The identification, annotation and explanation of anomalous omics-based patients or individuals may become crucial to better reshape the disease, e.g., by detecting early onsets signaled by the omics and undetectable from observable symptoms. Here, we developed MLASDO (Machine Learning based Anomalous Sample Detection on Omics), a new method and software tool to identify, characterize and automatically describe anomalous samples based on omics data. Its workflow is based on three steps: (1) classification of healthy and cases individuals using a support vector machine algorithm; (2) detection of anomalous samples within groups; (3) explanation of anomalous individuals based on clinical data and expert knowledge. We showcase MLASDO using transcriptomics data of 317 healthy controls (HC) and 465 Parkinson's disease (PD) cases from the Parkinson's Progression Markers Initiative. In this cohort, MLASDO detected 15 anomalous HC with a PD-like transcriptomic signature and PD-like clinical features, including a lower proportion of CD4/CD8 naive T-cells and CD4 memory T-cells compared to HC (P<3.5*10^-3). MLASDO also identified 22 anomalous PD cases with a transcriptomic signature more similar to that of HC and some clinical features more similar to HC, including a lower proportion of mature neutrophils compared to PD cases (P<6*10^-3). In summary, MLASDO is a powerful tool that can help the clinician to detect and explain anomalous HC and cases of interest to be followed up. MLASDO is an open-source R package available at: https://github.com/JoseAdrian3/MLASDO.

Problem

• Detects inconsistencies between clinical and omics data in medical cohorts
• Identifies and explains anomalous samples using omics data
• Helps clinicians detect and follow up anomalous cases in diseases like Parkinson's

Innovation

• SVM-based classification of healthy and PD cases
• Detection of anomalous samples within groups
• Explanation using clinical data and expert knowledge