AI Reads the Literature to Learn What Genes Do

For decades, biologists have written down what genes do — in papers, reviews, and databases — yet most machine learning models that try to represent genes computationally have ignored this vast textual record entirely. A team at the University of New Mexico Comprehensive Cancer Center, working with collaborators in Jordan and Virginia, has now built a tool that changes that. Called LitGene, the model is described in Nature Communications as an interpretable transformer that learns gene representations by fusing unstructured scientific text with Gene Ontology annotations through contrastive learning. The result is a system that can predict what a gene does, which pathways it belongs to, and even which diseases it might be linked to — sometimes without ever having seen a labeled example.

The core problem LitGene tackles is representation learning: the task of converting a gene into a numerical vector, or embedding, that captures its biological meaning in a form algorithms can manipulate. Traditional approaches rely on gene sequences, expression profiles, or protein interaction networks. These are powerful but incomplete. Sequence data says nothing about function; expression data depends on the tissue and condition measured; network data is biased toward well-studied genes. The scientific literature, by contrast, is a dense, human-curated record of functional knowledge — but it is unstructured, noisy, and hard for machines to use directly. LitGene’s insight is that text and ontology together can fill gaps that sequence and network data leave behind.

Architecturally, LitGene is built on a transformer, the same class of neural network that powers modern language models. Transformers process input as sequences of tokens and use attention mechanisms to weigh how different parts of the input relate to one another. In LitGene’s case, the input includes textual descriptions of genes drawn from the literature alongside Gene Ontology annotations — a structured vocabulary that classifies gene function into molecular activity, biological process, and cellular component. The model refines gene embeddings by aligning these two views through contrastive learning, a technique that trains the network to pull together representations of the same gene from different sources while pushing apart representations of different genes. This forces the model to learn a shared space where textual meaning and ontological structure reinforce one another.

What makes LitGene unusual among biological foundation models is its emphasis on interpretability. The researchers built a SHAP-based tool — SHAP being a game-theoretic method for attributing a model’s output to its individual input features — that highlights which specific words and phrases in the literature most influenced a given prediction. On top of that, they implemented an automated literature-grounding framework that searches for corroborating textual support where it exists. So when LitGene predicts that a gene is involved in a particular process, it can show the user the evidence: the terms, sentences, and annotations that drove the call. For a field increasingly wary of black-box AI, this transparency is a significant selling point.

The team evaluated LitGene across eight benchmark tasks covering protein properties, where it achieved competitive accuracy against existing methods. More strikingly, the model exhibited zero-shot prediction — the ability to assign Gene Ontology terms, KEGG pathways, and gene-disease associations to genes it had never seen labeled examples for. Zero-shot capability matters because biology is full of under-studied genes: proteins whose functions have never been experimentally characterized, whose names appear in few papers, and whose annotations in databases are sparse. A model that can generalize from textual and ontological context to make reasonable predictions about these neglected genes could help reduce the well-documented bias in genomics toward the famous few.

The researchers demonstrated this hypothesis-generating power in a real disease context. LitGene flagged ANGPTL6 as a potential candidate gene for idiopathic pulmonary fibrosis, a progressive and often fatal lung disease of unknown cause. The prediction was not just a computational artifact: the team found that ANGPTL6 was differentially expressed in independent IPF samples, providing experimental corroboration from data the model had not been trained on. Domain experts reviewed the candidate prediction for clinical insight, and the authors acknowledge those reviewers in the paper. This kind of workflow — model predicts, independent data validates, experts assess — is exactly the pattern the researchers hope will make computational gene discovery more trustworthy.

Under-studied genes are a chronic problem in biomedical research. Studies have repeatedly shown that research funding, publication attention, and database annotations cluster around a small fraction of genes, leaving thousands essentially unexplored. This creates a feedback loop: genes with little prior annotation attract little research, which produces little new annotation. LitGene’s authors suggest that integrating textual and genetic information may help break this cycle by allowing models to make informed predictions about genes whose experimental record is thin. Because the literature itself is a form of evidence — even if indirect — a model that reads it can leverage knowledge that sequence-based or network-based approaches cannot access.

LitGene is released as open-source code, and the team has deployed a publicly accessible web interface that allows anyone to submit a gene name, disease name, drug name, or custom text — such as a publication abstract — and receive evidence-based predictions of related biomedical entities. This kind of accessibility matters for a tool aimed at hypothesis generation: a researcher studying a neglected gene, a clinician investigating a disease mechanism, or a pharmacologist exploring drug targets can all query the model directly without needing to run the underlying infrastructure. The interface is designed to ground its predictions in the literature, so users can trace the reasoning behind each call rather than accepting an opaque score.

Technically, the combination of contrastive learning and ontology integration is what gives LitGene its generalization ability. Contrastive learning is well suited to aligning heterogeneous data sources because it does not require the model to reconstruct inputs; instead, it learns which representations should be similar and which should be dissimilar. Gene Ontology, meanwhile, provides a hierarchical structure that constrains predictions to biologically coherent categories. By training the model to respect both the free-text record and the structured vocabulary, LitGene learns embeddings that are simultaneously linguistically informed and ontologically grounded — a combination that appears to be more robust than either source alone.

The work was supported by the Ovarian Cancer Research Alliance, the National Cancer Institute, and the National Institute of General Medical Sciences, reflecting the translational ambitions behind the project. The authors, including Oladimeji Macaulay, Ala Jararweh, David Arredondo, and corresponding author Avinash Sahu, among others, position LitGene not as a replacement for experimental biology but as a bridge between the written record and the computational models increasingly used to navigate it. As genomics enters an era where AI systems routinely read, reason, and predict, tools like LitGene suggest that the scientific literature itself — the accumulated written knowledge of generations of biologists — may be one of the most valuable untapped data sources for training the next wave of biological foundation models.

Subject of Research: Health sciences

Article Title: LitGene: an interpretable transformer model integrating text and ontology for gene representation learning

Article References: Macaulay, O., Jararweh, A., Arredondo, D., Tafoya, L. E., Poshiya, S., Hu, Y., Oyebamiji, O. M., Zhang, Y., Amos, C. I., Virupakshappa, K., & Sahu, A. (2026). LitGene: an interpretable transformer model integrating text and ontology for gene representation learning. Nature Communications. https://doi.org/10.1038/s41467-026-78454-4

Image Credits: AI Generated

DOI: 10.1038/s41467-026-78454-4

Keywords: machine learning, genomics, transformer models, Gene Ontology, representation learning, interpretability, idiopathic pulmonary fibrosis, zero-shot prediction, LitGene, interpretable, transformer, model