GhostBuster: A Deep-Learning-based, Literature-Unbiased Gene Prioritization Tool for Gene Annotation Prediction.

ABSTRACT: All genes are not equal before literature. Despite the explosion of genomic data, a significant proportion of human protein-coding genes remain poorly characterized ("ghost genes"). Due to sociological dynamics in research, scientific literature disproportionately focuses on already well-annotated genes, reinforcing existing biases (bandwagon effect). This literature bias often permeates machine learning (ML) models trained on gene annotation tasks, leading to predictions that favor well-studied genes. Consequently, standard ML performance metrics may overestimate biological relevance by overfitting literature-derived patterns. To address this challenge, we developed GhostBuster, an encoder-decoder ML platform designed to predict gene functions, disease associations and interactions while

SUBMITTER: Deangeli G 

PROVIDER: S-EPMC12262676 | biostudies-literature | 2025 Jun

REPOSITORIES: biostudies-literature