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.

FILM: Framework for Imbalanced Learning Machines based on a new unbiased performance measure and a new ensemble-based technique

This research addresses the challenges of handling unbalanced datasets for binary classification tasks. In such scenarios, standard evaluation metrics are often biased by the disproportionate representation of the minority class. Conducting experiments across seven datasets, we uncovered inconsistencies in evaluation metrics when determining the model that outperforms others for each binary classification problem. This justifies the need for a metric that provides a more consistent and unbiased evaluation across unbalanced datasets, thereby supporting robust model selection. To mitigate this problem, we propose a novel metric, the Unbiased Integration Coefficients (UIC), which exhibits significantly reduced bias ($p < 10^{-4}$) towards the minority class compared to conventional metrics. The UIC is constructed by aggregating existing metrics while penalising those more prone to imbalance. In addition, we introduce the Identical Partitions for Imbalance Problems (IPIP) algorithm for imbalanced ML problems, an ensemble-based approach. Our experimental results show that IPIP outperforms other baseline imbalance-aware approaches using Random Forest and Logistic Regression models in three out of seven datasets as assessed by the UIC metric, demonstrating its effectiveness in addressing imbalanced data challenges in binary classification tasks. This new framework for dealing with imbalanced datasets is materialized in the FILM (Framework for Imbalanced Learning Machines) R Package, accessible at https://github.com/antoniogt/FILM.

PhenoLinker: Phenotype-Gene Link Prediction and Explanation using Heterogeneous Graph Neural Networks

The association of a given human phenotype to a genetic variant remains a critical challenge for biology. We present a novel system called PhenoLinker capable of associating a score to a phenotype-gene relationship by using heterogeneous information networks and a convolutional neural network-based model for graphs, which can provide an explanation for the predictions. This system can aid in the discovery of new associations and in the understanding of the consequences of human genetic variation.