Richard Meyer

Cambridge-based Congenica is being acquired for an undisclosed sum by French company SeqOne but the new entity will retain a base at the Wellcome Sanger Institute from where it was spun out. The buyer, based in Montpelier, is a pioneer in AI-driven genomic analysis and the acquisition will create the largest global ‘software pure player’ in the genomics space, serving over 160 labs in more than 30 countries. The deal terms will remain undisclosed. Founded in 2012, Congenica is said to bring a legacy of deep clinical expertise and experience servicing more than 25 private and public labs, including multiple NHS Genomics Laboratory Hubs and key national initiatives such as Genomics England and the Hong Kong Genomic Program. SeqOne provides clinical decision-support tools that enable molecular laboratories to deliver fast, accurate, and scalable analysis of genetic data for applications in oncology, rare and inherited conditions and infectious diseases. The acquisition unites SeqOne’s AI-powered platform for Next-Generation Sequencing analysis with Congenica’s trusted clinical decision support technology and clinical interpretation services. The integrated offering will transform complex genomic data into rapid, actionable insights, ultimately accelerating diagnosis for families affected by rare disease and enabling more precise, personalised cancer therapies. The combined entity will have a significantly expanded global footprint and will comprise a dedicated team of more than 125 employees – enabling over 200,000 patient genomic analyses in 2025. That's a threefold increase over 2024. SeqOne confirms that it will maintain what it calls a meaningful UK presence at Sanger. Martin Dubuc, CEO of SeqOne, said: “The rapid pace of personalised medicine demands continuous investment in software innovation and deep specialisation. By integrating Congenica’s world-class team, we are further enhancing our strong growth trajectory and ability to provide market-leading software to customers, expert interpretation services, and deepening our presence in the UK – a global leader in clinical genomics since the landmark 100,000 Genomes Project.” Dr Andy Richards, Chair of Congenica, added: “The Board of Congenica is proud of the immense impact our technology has had on healthcare since our inception. We support this combination with SeqOne, believing their vision and resources are best positioned to carry that legacy forward and ensure our innovative platform continues to thrive and serve patients globally.” Full article – https://lnkd.in/eEhXhq2J

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Have you heard of openproblems.bio? 🧬 It's kind of like Kaggle for single-cell biology. And it's a brilliant model of how the ML and bioinformatics communities collaborate. Single-cell analysis sits at a fascinating intersection: massive, tabular datasets meet cutting-edge ML meets the messy reality of biology. But here's the challenge: ML researchers love Jupyter notebooks, while biology demands robust, reproducible pipelines for processing data. Different tools, different cultures. OpenProblems.bio bridges this gap brilliantly. It transforms core single-cell challenges into living, community-run benchmarks where anyone can: ✅ Submit methods in Python or R (no pipeline expertise needed) ✅ Compare results fairly with standardized datasets and metrics ✅ Access everything openly, including published results and methods The secret sauce? A powerful three-part ecosystem: * Viash converts scripts into reproducible pipeline components * Nextflow orchestrates complex workflows * Seqera provides the platform for elastic cloud execution on AWS What excites me most: this is **FAIR open science done right** Complete transparency, version control, quality checks built in, and a framework that welcomes contributions from both communities without forcing either to abandon their tools. Tasks range from dimensionality reduction to perturbation prediction, with new benchmarks continually added by the community. If you're working in computational biology or ML for life sciences, this is infrastructure worth knowing about. The future of biomedical AI needs exactly this kind of collaborative, rigorous foundation. Thanks to Robrecht Cannoodt and the openproblems.bio community for collaborating on the blog: https://lnkd.in/gpNJNd2r #Bioinformatics #MachineLearning #SingleCell #OpenScience #Benchmarking

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🧠 Cortex Conversations: Episode 3 – Digital Endpoints in Neuroscience Today Digital biomarkers are no longer a future concept—they’re shaping neuroscience trials right now. In this episode, Amber Burg is joined by Leone Atkinson, Executive Medical Director of Neuroscience and Ophthalmology at Fortrea, and Alicia Baker McDowell, DRSc, MS, EMBA, Vice President and Head of Global Regulatory Strategy at Fortrea, to explore how these tools are being used, what regulators are saying, and how biotech leaders can confidently integrate them into development strategies. 💡 Real-world wins, AI, and hybrid models—this episode lays the groundwork for smarter, more precise trials. 🎧Listen in: https://lnkd.in/eb2ArTjP #Fortrea #Neuroscience #DigitalEndpoints #ClinicalTrials

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AI isn’t the hard part — data is. In life sciences R&D, fragmented, inaccessible data is still the biggest barrier to innovation. This blog explores a data transformation framework built to enable real AI impact, from semantic layers to agentic systems that accelerate discovery. If you’re building smarter science, start here ➡️ https://ow.ly/qXGA50WLhSY #LifeSciences #AI #Pharma #DataPlatforms

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Alert: Zifo Launches AI Antibody Engineering App! Zifo's new AI Antibody Engineering application is now available on the Snowflake Marketplace. This no-code GenAI platform empowers scientists to fine-tune protein models and handle the entire discovery pipeline, from sequence generation to 3D structure prediction. Read the Full story here: https://lnkd.in/gQrd9iSN The development of new antibody-based therapies is traditionally a complex, time-consuming, and expensive process. Zifo's Antibody Engineering app directly confronts this challenge by integrating state-of-the-art AI into a seamless, end-to-end workflow that promises to improve the hit rate for high-affinity therapeutic antibodies. "We are entering a new era of drug discovery, one where AI and data converge to solve biology's most complex puzzles," said Chris McClure, Head of Solution Services for Zifo in North America. "Zifo Antibody Engineering is more than just a tool; it's a strategic partner for scientists. By building it as a Snowflake Native App, we're giving researchers the power to innovate faster, reduce R&D costs, and ultimately bring life-saving therapies to patients sooner. We've effectively democratized access to complex AI, removing the need for deep machine learning expertise." #LifeSciences #Innovation #AIinHealthcare #AntibodyEngineering

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⚡ Cambridge-based Cyted Health has closed over €37 million in Series B funding to advance its oesophageal cancer detection technology. The round was led by EQT Life Sciences and Advent Life Sciences. Founded in 2020 by University of Cambridge experts including CEO Marcel Gehrung, Professor Rebecca Fitzgerald, and pathologist Maria O’Donovan, Cyted aims to challenge the $1 billion upper gastrointestinal diagnostics market and scale its commercial operations in the US. 📖 Full story → https://lnkd.in/eCXmvGF4 British Business Bank Carmine Circelli Bruno Holthof #CytedHealth #CancerDetection #AI #MedTech #Healthcare

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Quantum was recently named as one of six “frontier technologies” for securing long-term economic growth across the UK. From tackling climate change to developing new medicines, quantum technologies can transform what’s possible across science, industry, and society.   Realising these opportunities requires the development of secure, trusted quantum digital infrastructure.   Check out this great piece by Gabriela Styf Sjöman, Managing Director of Research and Network Strategy at BT, in Infosecurity Magazine on securing the UK's quantum economy. We're proud to continue to work with BT Group to bring quantum-secure technologies into the real world via our London Quantum-Secure Metro Network, using quantum key distribution to protect highly sensitive data travelling across the city.   Read more about the technology and how we’re working towards a quantum-ready future: https://lnkd.in/eDXJxx43   #QuantumCommunications #Quantum #CyberSecurity

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New Webinar: "Accelerating drug development with causal AI and digital twins: Ansys and Nova In Silico partner to offer an ISCT solution" Register online: https://lnkd.in/eY9Mbtee Bringing a new drug to market typically takes more than ten years and costs between $1.5 billion and $2.6 billion, but only about 5% of drug candidates entering clinical trials ultimately gain regulatory approval. This high failure rate poses significant financial challenges and raises ethical questions, as many drugs that will never be approved, are tested on animals and humans.   Causal AI, which uses causal models to better understand the mechanisms of a system, has emerged as a promising solution, using mechanistic modelling and AI to inform human trials. To develop this capability, Ansys, part of Synopsys, is partnering with Nova In Silico, an innovative healthtech company based in Lyon, France. This collaboration involves Nova In Silico's purpose-built Jinko trial simulation platform with Ansys Granta Data Management, providing a unified solution for generating digital twins for in silico clinical trials (ISCT). The integrated approach has the potential to significantly reduce R&D costs, accelerate time to market for pharmaceutical innovations, and reduce uncertainty related to clinical success rates in the biopharmaceutical industry.   On 27 August, I will have the privilege of welcoming two prestigious subject-matter experts, François-Henri Boissel (Nova In Silico) and Prasanth BOKAM (Synopsys), who will present and discuss this important milestone for pharmaceutical innovation.

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Excited to share that the Broad Institute of MIT and Harvard Machine Learning in Drug Discovery Symposium is coming up. Join for a gathering of leaders pushing the boundaries at the intersection of AI in drug discovery. The full agenda is now live: https://lnkd.in/eKZmPwnZ I am happy to be presenting work from Psivant Therapeutics that leverages AI/ML, physics, and wet lab experiments to drug challenging I&I targets. While the AI/ML field continues to gain momentum, we are beginning to learn more about the true value and limitations of AI/ML. In short, AI/ML approaches are great for interpolation but quite poor for extrapolation. When you have the appropriate training data, use AI/ML and you will generally get good predictions, fast. However, much of drug discovery involves finding novel drugs for undrugged targets (i.e. not great data). For those scenarios, proceed with caution when using AI/ML methods. It is easy to fool yourself into thinking your method can extrapolate when in reality you have data in your training set that looks a lot like your test set (which does not look like the novel drugs you want to design). For example, see recent publications on protein co-folding from Eva Nittinger et al. at AZ https://lnkd.in/eWmw8-w3, Pat Walters https://lnkd.in/ekEjhhnA and the SIB Swiss Institute of Bioinformatics https://lnkd.in/eG5puMUd At the same time, we are also familiar with the limitations of physics-based approaches (complex setup; computationally intensive; sampling local/global minima; force fields; tautomers; stereochemistry; waters; expert knowledge; and more). Yet physics-based methods can truly extrapolate and in fact do not need any training data when embarking on a new target or class of ligands. Our philosophy: * Build a computational ecosystem that fully leverages state-of-the-art methods in both AI/ML and physics. * Run at scale. * Automate as appropriate. * Keep humans in the loop. * Design an intuitive data-centric user interface for experienced drug hunters. * Hire expert developers who understand the strengths and limits along with a firm grasp of drug discovery such that the focus is on the highest value problems within the project. This might sound obvious, but for all the people at the interface of in silico and wet lab approaches, you know how hard it can be. The magic happens when you combine the speed of AI/ML, the extrapolative power of physics, the right experiments, and the judgment of experienced drug hunters. Registration is open, but this one tends to sell out, so sign up now: https://lnkd.in/epuJfyNT Looking forward to joining great speakers, such as Thomas Miller, Marinka Zitnik, and more.

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🎙️ New Thought Leaders Podcast Episode 🎙️ In this episode, Andrew Beck, Co-Founder and CEO of PathAI, shares his vision for how artificial intelligence is reshaping the future of pathology — from digital diagnostics to accelerating drug development. Hosted by Jonathon Tunstall, CEO and Founder of Pathology News, this exclusive conversation explores: 🔹 How PathAI is driving adoption of digital pathology and AI across pharma and clinical settings 🔹 Andy’s journey from academia to building one of the most influential companies in the field 🔹 The role of trusted partnerships and collaborations with Roche, Moffitt Cancer Center, and Northwestern Medicine 🔹 The balance between innovation and regulation in advancing AI 🔹 The rise of digital biomarkers and the future of AI-supported pathology With insights from one of the industry’s leading voices, this episode offers a compelling look at why digitisation is inevitable for the modern pathology lab and why AI will be central to its evolution. 🔗 Listen to the full episode now: https://lnkd.in/eGT2JmF5 #DigitalPathology #AI #ThoughtLeaders #PathAI #PathologyNews #HealthcareInnovation

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A few days ago, I shared thoughts on how scientists approach controversial health claims with data and proper methodology. I’ve now published the full tutorial on YouTube. In this session, we dive into correlation analysis with Python, using NHANES data on blood lead levels and cognitive function in older adults. What you’ll learn: - How to measure correlation strength, direction, and significance - Why “correlation ≠ causation” (and how to test for confounding variables) - How statistical thinking helps evaluate health claims more critically Watch here: https://lnkd.in/eEQewpHz #datascience #statistics #cdc #healthscience #python #dataanalysis

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