Sam Berns was my friend. With the wisdom of a sage, he inspired me and many others about how to make the most of life. Afflicted with the rare disease called progeria, his body aged at a rapid rate, and he died of heart failure at just 17, a brave life cut much too short. My lab discovered the genetic cause of Sam's illness two decades ago: Just one DNA letter gone awry, a T that should have been a C in a critical gene called lamin A. The same misspelling is found in almost all of the 200 individuals around the world with progeria. This story is from the WIRED World in 2025, our annual trends briefing.

Now the company says it has fine-tuned that protein model to predict which misspellings found in human DNA are safe to ignore and which are likely to cause disease. The new software, called AlphaMissense, was described today in a report published by the journal Science. As part of its project, DeepMind says, it is publicly releasing tens of millions of these predictions, but the company isn't letting others directly download the model because of what it characterizes as potential biosecurity risks should the technique be applied to other species. Although not intended to directly make diagnoses, computer predictions are already used by doctors to help locate the genetic causes of mysterious syndromes. In a blog post, DeepMind said its results are part of an effort to uncover "the root cause of disease" and could lead to "faster diagnosis and developing life-saving treatments."

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.

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 today (February 16) 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.

Researchers at the University of Zurich have developed a new tool that uses artificial intelligence to predict the efficacy of various genome-editing repair options. Unintentional errors in the correction of DNA mutations of genetic diseases can thus be reduced. Genome editing technologies offer great opportunities for treating genetic diseases. Methods such as the widely used CRISPR/Cas9 gene scissors directly address the cause of the disease in the DNA. The scissors are used in the laboratory to make targeted modifications to the genetic material in cell lines and model organisms and to study biological processes.

Scientists from the University of California San Diego School of Medicine and Rady Children's Institute for Genomic Medicine have developed a method for identifying mosaic mutations using deep learning. The process involves training a model to analyze large amounts of genomic data and recognize patterns associated with mosaic mutations. The researchers hope that this approach will help increase our understanding of the genetic basis of disease and lead to the development of more effective treatments. Genetic mutations can lead to a wide range of disorders that are often difficult to treat or understand. One type of mutation, called mosaic mutations, is particularly challenging to identify because it only affects a small percentage of cells.

"We should be much more worried about population collapse….If there aren't enough people for Earth, then there definitely won't be enough for Mars," he opined. Musk's statements brought the world's falling birthrate to the forefront of social consciousness. For nearly a century, fertility rates have been decreasing globally. The result is what scientists are describing as a "worldwide infertility crisis." In 2017, scientists created a "BioBag" that functioned as an artificial womb, and they used it to grow a baby lamb.

Artificial intelligence (AI) is revolutionizing genomic medicine by providing better health outcomes. Genomic diagnostic is an area that can benefit hugely from the capabilities of AI. The involvement of AI in healthcare can potentially be beneficial in genetic diagnostics. Genomic medicine is an emerging medical discipline that involves using genomic information about an individual as part of their clinical care (e.g. for diagnostic or therapeutic decision-making) and the health outcomes and policy implications of that clinical use. Rare diseases are fairly common in the world, with nearly half a billion people suffering from some or the other kind of lesser-known ailments.

While many babies are born without issues on a daily basis, there are quite a few who are born early and with some complications. Those babies end up in the Neonatal Intensive Care Unit (NICU), which is where Dr. Sabrina Malone Jenkins works. The Neonatologist at University of Utah Health and Intermountain Primary Children's Hospital said, "A lot of the time we don't know what the underlying condition is and there is a wide variety of causes and rarely are any of them the same." Researchers say on a global scale about seven million infants are born with serious genetic disorders each year and it can be tough for doctors to treat them if they're not sure what's wrong. Dr. Mark Yandell, a Professor in the Department of Human Genetics at the University of Utah said, "It's estimated that about 20% of the newborns in a high-intensity newborn intensive care unit have some form of genetic disease."

The Exeter Genomics Laboratory implemented a diagnostic rare disease exome pipeline in 2013 and in October 2019, it launched the new NHSE nationally-commissioned rapid exome sequencing service for acutely unwell children. Children in neonatal or pediatric intensive care units or those with a likely monogenic disorder where a diagnosis was needed for immediate management decisions were eligible for this service. In its first year, 519 children were tested and a genetic diagnosis identified in 37% . With a focus on multidisciplinary team working, this service is a model of how translational medical research and close multidisciplinary working across the NHS can revolutionise patient care, improve patient outcomes and empower families by providing early diagnoses. Genetic disease is a leading contributor to infant and childhood mortality in hospital intensive care units (ICU).