Throughout the appendix, we shall adopt an orthogonal parametrization for mean vectors in the modelM.

Fine-grained zero-shot learning task requires some form of side-information to transfer discriminative information from seen to unseen classes.

AI model from Google's DeepMind reads recipe for life in DNA An AI model developed by Google's DeepMind could transform our understanding of DNA - the complete recipe for building and running the human body - and its impact on disease and medicine discovery, according to researchers. Called AlphaGenome, the model could help scientists discover why subtle differences in our DNA put us at risk of conditions such as high blood pressure, dementia and obesity. It could also dramatically accelerate our understanding of genetic diseases and cancer. The developers of the model acknowledge it's not perfect, but experts have described it as an incredible feat and a major milestone. We see AlphaGenome as a tool for understanding what the functional elements in the genome do, which we hope will accelerate our fundamental understanding of the code of life, says Natasha Latysheva, research engineer at DeepMind.

Science sleuths think they found Leonardo da Vinci's DNA Advances in genetics might help us see what set the Renaissance man apart. The painting hangs in the Uffizi Gallery in Florence, Italy. Breakthroughs, discoveries, and DIY tips sent every weekday. Scientists are one step closer to pinpointing fragments of Leonardo da Vinci's elusive DNA . A team of researchers from the Leonardo da Vinci DNA Project analyzed samples swabbed from a red chalk drawing possibly attributed to the famed polymath, as well as letters written by one of his known cousins.

Modeling these sequences is key to understand disease mechanisms and is an active research area in computational biology. Recently, Large Language Models have shown great promise in solving certain biological tasks but current approaches are limited to a single sequence modality (DNA, RNA, or protein). Key problems in genomics intrinsically involve multiple modalities, but it remains unclear how to adapt general-purpose sequence models to those cases. In this work we propose a multi-modal model that connects DNA, RNA, and proteins by leveraging information from different pre-trained modality-specific encoders. We demonstrate its capabilities by applying it to the largely unsolved problem of predicting how multiple \rna transcript isoforms originate from the same gene (i.e.

Fine-grained zero-shot learning task requires some form of side-information to transfer discriminative information from seen to unseen classes. As manually annotated visual attributes are extremely costly and often impractical to obtain for a large number of classes, in this study we use DNA as a side information for the first time for fine-grained zero-shot classification of species. Mitochondrial DNA plays an important role as a genetic marker in evolutionary biology and has been used to achieve near perfect accuracy in species classification of living organisms. We implement a simple hierarchical Bayesian model that uses DNA information to establish the hierarchy in the image space and employs local priors to define surrogate classes for unseen ones. On the benchmark CUB dataset we show that DNA can be equally promising, yet in general a more accessible alternative than word vectors as a side information. This is especially important as obtaining robust word representations for fine-grained species names is not a practicable goal when information about these species in free-form text is limited. On a newly compiled fine-grained insect dataset that uses DNA information from over a thousand species we show that the Bayesian approach outperforms state-of-the-art by a wide margin.

Centuries-old genetic material can solve historical mysteries, from lost species to what killed Napoleon's army. A glowing, digital double helix represents the billions of base pairs scientists analyze when sequencing ancient DNA. Breakthroughs, discoveries, and DIY tips sent every weekday. In 1976, workers excavating a tunnel for the Toronto subway system came across some very old bones. Using radiocarbon dating, researchers determined the partial cranium and fragments of antlers were roughly 12,000 years old.

In February 1953, two men walked into a pub in Cambridge and announced they had found the secret of life. It was not an idle boast. One was James Watson, an American biologist from the Cavendish laboratory; the other was his British research partner, Francis Crick. The full Promethean power of their achievement would slowly emerge over decades of research by fellow geneticists. It also opened a Pandora's Box of controversial scientific and ethical issues - including human cloning, designer babies and Frankenstein foods.