In 2025, we will see AI and machine learning begin to amplify the impact of Crispr genome editing in medicine, agriculture, climate change, and the basic research that underpins these fields. It's worth saying upfront that the field of AI is awash with big promises like this. With any major new technological advance there is always a hype cycle, and we are in one now. In many cases, the benefits of AI lie some years in the future, but in genomics and life science research we are seeing real impacts right now. This story is from the WIRED World in 2025, our annual trends briefing.

The "RNA world" represents a novel frontier for the study of fundamental biological processes and human diseases and is paving the way for the development of new drugs tailored to the patient's biomolecular characteristics. Although scientific data about coding and non-coding RNA molecules are continuously produced and available from public repositories, they are scattered across different databases and a centralized, uniform, and semantically consistent representation of the "RNA world" is still lacking. We propose RNA-KG, a knowledge graph encompassing biological knowledge about RNAs gathered from more than 50 public databases, integrating functional relationships with genes, proteins, and chemicals and ontologically grounded biomedical concepts. To develop RNA-KG, we first identified, pre-processed, and characterized each data source; next, we built a meta-graph that provides an ontological description of the KG by representing all the bio-molecular entities and medical concepts of interest in this domain, as well as the types of interactions connecting them. Finally, we leveraged an instance-based semantically abstracted knowledge model to specify the ontological alignment according to which RNA-KG was generated. RNA-KG can be downloaded in different formats and also queried by a SPARQL endpoint. A thorough topological analysis of the resulting heterogeneous graph provides further insights into the characteristics of the "RNA world". RNA-KG can be both directly explored and visualized, and/or analyzed by applying computational methods to infer bio-medical knowledge from its heterogeneous nodes and edges. The resource can be easily updated with new experimental data, and specific views of the overall KG can be extracted according to the bio-medical problem to be studied.

For the function of many biomolecules, their three-dimensional structure is crucial. Researchers are therefore not only interested in the sequence of the individual building blocks of biomolecules, but also in their spatial structure. With the help of artificial intelligence (AI), bioinformaticians can already reliably predict the three-dimensional structure of a protein from its amino acid sequence. For RNA molecules, however, this technology is still in its infancy. Researchers at Ruhr-Universität Bochum (RUB) describe a way to use AI to reliably predict the structure of certain RNA molecules from their nucleotide sequence in the journal PLOS Computational Biology on July 7, 2022.

Predicting RNA (ribonucleic acid) structures may help accelerate the discovery and development of new drugs to treat diseases and disorders. A new Stanford study published in Science uses artificial intelligence (AI) machine learning to predict RNA structures with state-of-the-art performance results. "Few RNA structures are known, however, and predicting them computationally has proven challenging," wrote the Stanford scientists. "We introduce a machine learning approach that enables identification of accurate structural models without assumptions about their defining characteristics, despite being trained with only 18 known RNA structures." In molecular biology, RNA (ribonucleic acid) is involved in many important cellular functions.

We combined a machine learning algorithm with knowledge gleaned from hundreds of biological experiments to develop a technique that allows biomedical researchers to figure out the functions of the proteins that turn genes on and off in cells, called transcription factors. This knowledge could make it easier to develop drugs for a wide range of diseases. Early on during the COVID-19 pandemic, scientists who worked out the genetic code of the RNA molecules of cells in the lungs and intestines found that only a small group of cells in these organs were most vulnerable to being infected by the SARS-CoV-2 virus. That allowed researchers to focus on blocking the virus's ability to enter these cells. Our technique could make it easier for researchers to find this kind of information.

A new computational tool developed by KAUST scientists uses artificial intelligence (AI) to infer the RNA-binding properties of proteins. The software, called NucleicNet, outperforms other algorithmic models of its kind and provides additional biological insights that could aid in drug design and development. "RNA binding is a fundamental feature of many proteins," says Jordy Homing Lam, a former research associate at KAUST and co-first author of the study. "Our structure-based computational framework can reveal the detailed RNA-binding properties of these proteins, which is important for characterizing the pathology of many diseases." Proteins routinely interface with RNA molecules as a way to control the processing and transporting of gene transcripts--and when these interactions go awry, information flow inside the cell is disrupted and disorders can arise, including cancer and neurodegenerative disease.

RNA, or ribonucleic acid, is present in all living cells. It acts as a messenger, carrying instructions from DNA (deoxyribonucleic acid) that dictate how proteins in the body are synthesized. And when it doesn't work as it should, it can severely affect neurological, cardiovascular, and muscular regulatory processes, resulting in effects like tumors, insulin resistance, and motor skill impairment. That's why researchers at the University of Freiburg's Department of Computer Science developed an AI system -- LEARNA -- that can learn to design RNA molecules for study. It's described in a new paper ("Learning to Design RNA") published this week on the preprint server Arxiv.org.

Is the natural world creative? Just take a look around it. Look at the brilliant plumage of tropical birds, the diverse pattern and shape of leaves, the cunning stratagems of microbes, the dazzling profusion of climbing, crawling, flying, swimming things. Look at the "grandeur" of life, the "endless forms most beautiful and most wonderful," as Darwin put it. Isn't that enough to persuade you? Ah, but isn't all this wonder simply the product of the blind fumbling of Darwinian evolution, that mindless machine which takes random variation and sieves it by natural selection? You don't have to be a benighted creationist, nor even a believer in divine providence, to argue that Darwin's astonishing theory doesn't fully explain why nature is so marvelously, endlessly inventive.

Two EteRNA players check out a molecule designed using the online game. The display above shows output from a laser microscope that tests the new designs. Two EteRNA players check out a molecule designed using the online game. The display above shows output from a laser microscope that tests the new designs. Though it's the world's top infectious killer, tuberculosis is surprisingly tricky to diagnose.

Two EteRNA players check out a molecule designed using the online game. The display above shows output from a laser microscope that tests the new designs. Two EteRNA players check out a molecule designed using the online game. The display above shows output from a laser microscope that tests the new designs. Though it's the world's top infectious killer, tuberculosis is surprisingly tricky to diagnose.