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

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 minimizing literature bias. We first compared the impact of biased (Gene Ontology) versus unbiased training datasets (LINCS, TCGA, STRING). While literature-biased sources yielded higher ML metrics, they also amplified bias by prioritizing well-characterized genes. In contrast, models trained on unbiased datasets were 2-3× more effective at identifying recently discovered gene annotations. Notably, one of the unbiased channels (TCGA), combined minimal amounts of literature bias with robust performance, at a test ROC-AUC of 0.8-0.95. We demonstrate that GhostBuster can be applied to predict novel gene functions, refine pathway memberships, and prioritize intergenic GWAS hits. As the first ML framework explicitly designed to counteract literature bias, GhostBuster offers a powerful tool for uncovering the roles of understudied genes in cellular function, disease, and molecular networks.