Retrieving gene functional networks from knowledge databases presents a challenge due to the mismatch between disease networks and subtype-specific variations. Current solutions, including statistical and deep learning methods, often fail to effectively integrate gene interaction knowledge from databases or explicitly learn subtype-specific interactions. To address this mismatch, we propose GeSubNet, which learns a unified representation capable of predicting gene interactions while distinguishing between different disease subtypes. Graphs generated by such representations can be considered subtype-specific networks. GeSubNet is a multi-step representation learning framework with three modules: First, a deep generative model learns distinct disease subtypes from patient gene expression profiles. Second, a graph neural network captures representations of prior gene networks from knowledge databases, ensuring accurate physical gene interactions. Finally, we integrate these two representations using an inference loss that leverages graph generation capabilities, conditioned on the patient separation loss, to refine subtype-specific information in the learned representation. GeSubNet consistently outperforms traditional methods, with average improvements of 30.6%, 21.0%, 20.1%, and 56.6% across four graph evaluation metrics, averaged over four cancer datasets. Particularly, we conduct a biological simulation experiment to assess how the behavior of selected genes from over 11,000 candidates affects subtypes or patient distributions. The results show that the generated network has the potential to identify subtype-specific genes with an 83% likelihood of impacting patient distribution shifts. The GeSubNet resource is available: https://anonymous.4open.science/r/GeSubNet/

A new tool to predict the chances of successfully inserting a gene-edited sequence of DNA into the genome of a cell, using a technique known as prime editing, has been developed by researchers at the Wellcome Sanger Institute. An evolution of CRISPR-Cas9 gene editing technology, prime editing has huge potential to treat genetic disease in humans, from cancer to cystic fibrosis. But thus far, the factors determining the success of edits are not well understood. The study, published in Nature Biotechnology, assessed thousands of different DNA sequences introduced into the genome using prime editors. These data were then used to train a machine learning algorithm to help researchers design the best fix for a given genetic flaw, which promises to speed up efforts to bring prime editing into the clinic.

DNA damage is constantly occurring in cells, either due to external sources or as a result of internal cellular metabolic reactions and physiological activities. Accurate repair of such DNA damages is critical to avoid mutations and chromosomal rearrangements linked to diseases including cancer, immunodeficiencies, neurodegeneration, and premature aging. A team of researchers at Massachusetts General Hospital and the National Cancer Research Centre have identified a way to repair genetic damage and prevent DNA alterations using machine learning techniques. The researchers state that it is possible to learn more about how cancer develops and how to fight it if we understand how DNA lesions originate and repair. Therefore, they hope that their discovery will help create better cancer treatments while also protecting our healthy cells. To combat challenges to DNA integrity, cells have evolved systems that detect DNA lesions and initiate a signaling cascade that promotes DNA repair, referred to as the DNA damage response (DDR).