TL;DR: Integrating multiple biological data types gives oncologists a much richer picture of a patient’s cancer. However, effectively training AI on massive amounts of highly complex genomic data, without hitting computational bottlenecks, remains a major challenge. Enter MOJO: A multimodal AI model presented at ICML 2026, which jointly learns from bulk RNA-seq and DNA methylation… Read more »
Pioneering companies sign cooperation agreement at Wilopark Dortmund. By signing a partnership agreement, water technology group Wilo and AI technology company InstaDeep have sealed the deal to kick off a long-term cooperation. The two pioneering companies are now working on systematically integrating water technology and AI across several defined applications. The long-term goal of the… Read more »
The genome is not a linear string of information, but a long-range, three-dimensional system in which regulatory signals act across large genomic distances to control gene expression. Interpreting this requires models that can connect nucleotide-level sequence to long-range regulation, cell-state–specific expression programmes, and the phenotypic consequences of genetic variation. Meeting this challenge demands single-nucleotide resolution,… Read more »
The 39th Annual Conference on Neural Information Processing Systems (NeurIPS) was capped off by a series of firsts for the InstaDeep team, with researchers leading the way in exchanging ideas and showcasing innovation within the AI community during our visit to the San Diego Convention Center from the 02-07 December. With five workshops, three accepted… Read more »
Combinatorial optimisation (CO) problems are among the hardest challenges in computer science, with real-world importance in logistics, transport, and energy systems. These problems involve choosing the “best” option from an astronomically large set of possibilities and are often NP-hard, meaning they become computationally intractable as the problem size grows. Neural solvers, advanced AI approaches designed… Read more »
Reinforcement learning (RL) has delivered some of AI’s most striking successes, from human-level Atari 1 play to world-class performance in Go2. Yet when applied to messy, real-world combinatorial optimisation (CO) problems such as energy grid management or autonomous logistics, even state-of-the-art RL systems can stall. Despite being trained to convergence, policies often hit a performance… Read more »
Multi-agent reinforcement learning (MARL) holds significant promise across domains such as autonomous driving, warehouse logistics, intelligent rail networks, and satellite alignment. Yet deploying MARL in the real world remains difficult. Training typically requires vast amounts of interactive data, which is both costly and potentially risky, particularly in safety-critical settings where trial and error is not… Read more »
Nucleotides are the fundamental units of DNA, and when linked together by a sugar-phosphate backbone, they form the strands that define our genome. Analysing the precise role of each nucleotide within these sequences is essential to understanding their influence on gene regulation and disease. However, the human genome contains around 3 billion nucleotides in a… Read more »
BioNTech hosted its annual AI Day at the Science Museum in London, the second event in their Innovation Series. The day brought together investors, analysts, media representatives and guests to explore how artificial intelligence could transform science and medicine. As part of BioNTech’s AI ecosystem, InstaDeep showcased the progress made across multiple fronts, from advanced… Read more »
Reinforcement learning (RL) is increasingly being applied to complex processes across science and engineering, with promising results in manufacturing, biology, and energy systems. By learning through trial and error, RL agents can optimise behaviour without explicit supervision1. Many of these processes are governed by differential-algebraic equations (DAEs). These combine time-dependent dynamics with algebraic constraints, making… Read more »