A recent study developed a highly accurate risk prediction framework for preterm birth (PTB) that could broaden the potential of AI-driven multi-omics applications in precision obstetrics and biomedical research.

The model, deeply integrating genomics, transcriptomics, and large language models (LLMs) for the first time for PTB risk prediction, has shown its effectiveness and clinical application prospects.

The research was conducted by a collaborative team led by BGI Genomics, together with Professor Huang Hefeng's team, Shenzhen Longgang Maternal and Child Health Hospital, Fujian Maternity and Child Health Hospital, and OxTium Technology. The research was published in npj Digital Medicine on August 20th.

The non-coding genome, once dismissed as "junk DNA", is now recognized as a fundamental regulator of gene expression and a key player in understanding complex diseases. Following the landmark achievements of the Human Genome Project (HGP), scientists have increasingly focused on deciphering the non-coding regions of the human genome, which comprise approximately 98% of the genetic material. These regions, long overlooked due to their non-protein-coding nature, are now known to harbor regulatory elements crucial for cell function and disease progression.