José T. Palma | Searcharxiv

arXiv2026

Detecting and explaining clinical-omics inconsistencies to improve patient cohort stratification: an application to Parkinson's disease

Discrepancies between clinical diagnoses and omics profiles within a characterized cohort may reflect misdiagnosis, hidden subgroups or prodromal disease states. We propose MLASDO, a tool to detect and characterize such discrepancies before downstream analyses. MLASDO (1) detects outliers using two unsupervised methods, thereby flagging potential poor-quality samples; and (2) identifies and characterizes anomalous samples (ASs), i.e., individuals whose molecular profile resembles the opposite clinical class. We applied MLASDO to the Parkinson's Progression Markers Initiative (PPMI) and Parkinson's Disease Biomarkers Program (PDBP) Parkinson's disease (PD) cohorts. In PPMI, it detected 26 outliers and 12 ASs: 5 anomalous healthy controls (AHCs) and 7 anomalous PD cases (APDs). AHCs exhibited higher cerebrospinal fluid (CSF) A\b{eta}1-42 levels than controls (P < 0.0396), suggesting resistance to cognitive decline. AHC-specific genes were enriched for the MAPK pathway (P<0.0145), implicated in PD pathogenesis. One AHC later received a different neurological disorder, three months after enrollment. In PDBP, it identified 10 outliers and 8 ASs: 2 AHCs and 6 APDs. One AHC exhibited 24 clinical features consistent with a PD-like phenotype, including severe motor impairment. Results are compiled in an interactive report for inspection and querying, highlighting clinically meaningful individuals otherwise overlooked in conventional analyses.

cs.LG↗

arXiv2025

Enhancing Omics Cohort Discovery for Research on Neurodegeneration through Ontology-Augmented Embedding Models

The growing volume of omics and clinical data generated for neurodegenerative diseases (NDs) requires new approaches for their curation so they can be ready-to-use in bioinformatics. NeuroEmbed is an approach for the engineering of semantically accurate embedding spaces to represent cohorts and samples. The NeuroEmbed method comprises four stages: (1) extraction of ND cohorts from public repositories; (2) semi-automated normalization and augmentation of metadata of cohorts and samples using biomedical ontologies and clustering on the embedding space; (3) automated generation of a natural language question-answering (QA) dataset for cohorts and samples based on randomized combinations of standardized metadata dimensions and (4) fine-tuning of a domain-specific embedder to optimize queries. We illustrate the approach using the GEO repository and the PubMedBERT pretrained embedder. Applying NeuroEmbed, we semantically indexed 2,801 repositories and 150,924 samples. Amongst many biology-relevant categories, we normalized more than 1,700 heterogeneous tissue labels from GEO into 326 unique ontology-aligned concepts and enriched annotations with new ontology-aligned terms, leading to a fold increase in size for the metadata terms between 2.7 and 20 fold. After fine-tuning PubMedBERT with the QA training data augmented with the enlarged metadata, the model increased its mean Retrieval Precision from 0.277 to 0.866 and its mean Percentile Rank from 0.355 to 0.896. The NeuroEmbed methodology for the creation of electronic catalogues of omics cohorts and samples will foster automated bioinformatic pipelines construction. The NeuroEmbed catalogue of cohorts and samples is available at https://github.com/JoseAdrian3/NeuroEmbed.

cs.CL↗