scBench: AI Accuracy Varies by Sequencing Platform

𝗨𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱𝗶𝗻𝗴 𝗯𝗶𝗼𝗹𝗼𝗴𝘆 𝘄𝗶𝘁𝗵 𝗠𝗟: 𝗻𝗼𝘁 𝗷𝘂𝘀𝘁 “𝗲𝘅𝗽𝗹𝗮𝗻𝗮𝘁𝗶𝗼𝗻𝘀,” 𝗯𝘂𝘁 𝗰𝗼𝗺𝗽𝗿𝗲𝘀𝘀𝗶𝗼𝗻, 𝗶𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗶𝗯𝗶𝗹𝗶𝘁𝘆, 𝗮𝗻𝗱 𝗱𝗲𝗽𝗲𝗻𝗱𝗲𝗻𝗰𝘆. Law-like explanations are the exception rather than the rule in biology. This perspective explains how ML can meaningfully contribute to scientific understanding when its claims are properly scoped. 𝗧𝗵𝗿𝗲𝗲 𝗹𝗲𝗻𝘀𝗲𝘀 𝘁𝗼 𝗸𝗲𝗲𝗽 𝘂𝘀 𝗵𝗼𝗻𝗲𝘀𝘁: - 𝗖𝗼𝗺𝗽𝗿𝗲𝘀𝘀𝗶𝗼𝗻: inductive biases encode biological structure and shrink the hypothesis space. - 𝗤𝘂𝗮𝗹𝗶𝘁𝗮𝘁𝗶𝘃𝗲 𝗶𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗶𝗯𝗶𝗹𝗶𝘁𝘆: embeddings/clusters/trajectories help humans reason qualitatively (without pretending distances are always meaningful). - 𝗗𝗲𝗽𝗲𝗻𝗱𝗲𝗻𝗰𝘆 𝗺𝗼𝗱𝗲𝗹𝗹𝗶𝗻𝗴: models that make relationships explicit can better support prediction and intervention—especially when grounded with perturbations and assumption audits. 𝗪𝗵𝘆 𝗶𝘁 𝗺𝗮𝘁𝘁𝗲𝗿𝘀: In low-data regimes, “pretty interpretation” is cheap and seductive, What scales is (i) the right compression, (ii) intelligible representations that don’t overpromise, and (iii) dependencies that survive experimental pressure. Congratulations to the authors and co-authors Srijit Seal, Elsa Lawrence, Adham El-Shazly, Srijit Seal, Chaitanya Joshi, Pietro Lio', Andreas Bender, Shantanu Singh, Pietro Sormanni, Ola Spjuth, and Matt Greenig for this viewpoint. And congratulations to the supporting institutions University of Cambridge, Accelerate Programme for Scientific Discovery. Preprint: https://lnkd.in/eAaiHjAN 𝗣𝗮𝘂𝗹𝗶𝗻𝗴.𝗮𝗶 𝗿𝗲𝗳𝗹𝗲𝗰𝘁𝗶𝗼𝗻: This framing matches how we evaluate models in practice: not “is it interpretable?” but “what kind of understanding does it buy us, and what decisions does it support?” The goal is representations that compress real structure, stay qualitatively usable, and surface dependencies we can actually test.If you had to choose only one lens to optimize for in your work compression, intelligibility, or dependency modelling which one would it be, and why? 𝗙𝗶𝗴. 𝟭(From the prepeint): Why ML can enhance understanding without deductive-nomological-style laws?

11:47 PM, January 15th. Our computational biology team discovers a critical flaw in our protein folding algorithm. Three weeks before presenting to our Series B investors. Here's what happened next... The algorithm had been predicting drug-target interactions with 94% accuracy for months. Our entire pitch deck was built around it. Then Dr. Sarah Chen, our lead computational biologist, noticed something odd during routine validation. The model was memorizing training data patterns instead of learning true biological relationships. 𝗧𝗵𝗲 𝗯𝗿𝘂𝘁𝗮𝗹 𝗿𝗲𝗮𝗹𝗶𝘁𝘆: 18 months of work, potentially worthless. Most teams would panic. We did something different. We stripped the algorithm down to its core assumptions. Rebuilt the feature engineering from scratch. Instead of using traditional sequence-based inputs, we integrated 3D structural data with evolutionary conservation scores. The breakthrough came at 3:17 AM on January 22nd. Our new approach didn't just fix the overfitting problem. It revealed something profound: drug resistance mechanisms were predictable 6-12 months before they emerged clinically. We pivoted our entire value proposition. Instead of just finding new drug targets, we were now predicting resistance patterns before they happened. 𝗧𝗵𝗲 𝗼𝘂𝘁𝗰𝗼𝗺𝗲: Series B closed at $47M in Q1 2026. 40% higher than our original ask. The lesson? In computational biology, your biggest setbacks often hide your biggest breakthroughs. The key is having the courage to question your foundational assumptions when the data doesn't align. Sometimes the algorithm isn't broken. Your understanding of the biology is. Taking on select biotech AI strategy engagements for Q2 2026. DM me if you're tackling complex computational challenges. #ComputationalBiology #BiotechAI #DrugDiscovery #MachineLearning #BiotechInnovation

Aging kills more brilliant scientists than failure ever will. What if we treated it like an engineering problem instead? In the latest episode of Galaxy Balance, I sit down with Kejun (Albert) Ying, a computational biologist bridging Stanford's Wyss-Coray Lab and UW's Baker Lab. Albert shares cutting-edge work on AI-driven protein design, large-scale biological data mining, novel enzymes, and autonomous agents that could fundamentally reshape how we approach aging. One standout moment: Albert describes the emerging scientist workflow, once AI masters coding and analysis, you shift from hands-on execution to managerial oversight. Supervise 10 AI tools at once: assign tasks, evaluate outputs, refine the best ones. The result? A dramatic leap to 10x or even 100x efficiency, freeing you to focus on judgment, creativity, and the big questions. The full conversation is live now; Deep, honest, and forward-looking. Links in the comments. What would YOU prioritize if you had 10 AI assistants accelerating your research? Drop your thoughts below, I'd love to hear from fellow scientists, biotech builders, and AI explorers. #Longevity #AIAging #SyntheticBiology #BioTech #AIinScience #DecodingAging #GalaxyBalance

I’m happy to share that I have co-authored a peer-reviewed book chapter titled “Multi-Objective Optimization of Biodiesel Production Using Neural Networks, Machine Learning, and Genetic Algorithm”, published by CRC Press, Taylor & Francis Group. I had the opportunity to collaborate on this work with Jonna Jaidhitya, where we examined how advanced optimization techniques can enhance biodiesel production efficiency. The chapter reviews and compares machine learning models, artificial neural networks, response surface methodology, and genetic algorithms for optimizing key process parameters. Our analysis highlights the potential of data-driven and evolutionary approaches in improving prediction accuracy, maximizing fuel yield, and supporting more sustainable alternatives to conventional fossil fuels. This publication marks an important milestone in my academic journey and reflects my growing interest in intelligent systems, process optimization, and sustainable energy solutions. 🔗 Link: https://lnkd.in/gGaBqDN4 📘 DOI: 10.1201/9781003736820-2 #Publication #Research #RenewableEnergy #MachineLearning #GeneticAlgorithm #ProcessOptimization #SustainableEnergy #Engineering

On Structure Before Sophistication in Bioinformatics One thing that becomes clear very quickly in bioinformatics is that powerful analyses don’t start with complex algorithms, they start with structure. How data is organized. How files are handled. How workflows are executed and reproduced. Sequencing data, for example, is not just biological information, it is structured text designed to be interpreted line by line, tool by tool, step by step. Understanding why command-line environments dominate this field highlights something deeper: scalability and reproducibility matter just as much as biological insight. The more I explore this space, the more I appreciate that strong computational habits form the backbone of reliable genomics research. Before advanced modeling or variant interpretation, there must be clarity, organization, and disciplined data handling. Foundations shape outcomes.

I’ve been revisiting a few favorite authors of mine to make sense of the moment and tune out the noise. Rereading them now, some passages feel almost prophetic. (2006) : ‘To sum up, the boundary between the biotech and pharmaceutical sector will eventually be smeared during bi-directional fusion.’ (Science Business, Gary Pisano) During that decade, the chasm between biotech and pharma was defined by agility versus scale - biotech propelled by high-risk innovation, pharma anchored in capital strength and commercialization muscle. Give or take another decade, that divide gave way to massive consolidation, as pipelines, platforms, and balance sheets converged in pursuit of growth. (2017) : ‘Here, I argue that computational thinking and techniques are so central to the quest of understanding life that today all biology is computational biology…The next modern synthesis in biology will be driven by mathematical, statistical, and computational methods being absorbed into mainstream biological training, turning biology into a quantitative science.’ (All Biology is Computational Biology, Florian Markowetz) (2026) : We are witnessing a proliferation of high‑velocity, computation‑driven biotech variants - TechBio, SynBio, CodexBio, NeuroBio, LoopBio, MetaBio, and BioFoundry ; each leveraging data, AI, and platform capabilities as a competitive edge. As these players mature, the coming decade is poised for a new wave of strategic consolidation. Paving way for Pharma Commercial Engine to integrate complementary capabilities, capture scale efficiencies. This decade will see an M & A wave again.

𝐈𝐭𝐞𝐫𝐚𝐭𝐢𝐯𝐞 𝐈𝐧𝐬𝐢𝐠𝐡𝐭 𝐢𝐧 𝐒𝐢𝐧𝐠𝐥𝐞-𝐂𝐞𝐥𝐥 𝐚𝐧𝐝 𝐒𝐩𝐚𝐭𝐢𝐚𝐥 𝐓𝐫𝐚𝐧𝐬𝐜𝐫𝐢𝐩𝐭𝐨𝐦𝐢𝐜𝐬: 𝐖𝐡𝐲 𝐅𝐢𝐫𝐬𝐭-𝐏𝐚𝐬𝐬 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬 𝐅𝐚𝐥𝐥𝐬 𝐒𝐡𝐨𝐫𝐭 Single-cell, spatial, and multi-omics datasets rarely yield clear answers in a single pass. In practice, scientists move repeatedly between preprocessing, clustering, annotation, visualization, and interpretation before arriving at conclusions. Insight doesn’t emerge from a linear pipeline; it emerges through iteration. Today’s biological datasets are richer than ever: 🔬 Single-cell sequencing reveals transcriptional heterogeneity across thousands of cells. 🧭 Spatial assays map cellular organization within tissue microenvironments. 🧬 Multi-omics layers add pathway-level and molecular-level context. What they don’t provide is a straight path to decisions. In our latest blog, we explore: • Why first-pass analysis often falls short • How fragmented tools slow scientific reasoning • Why iteration is not a limitation - but the core of discovery • What modern analytical environments must enable to support real scientific decision-making As datasets grow in complexity, the cost of iteration becomes the cost of discovery. Platforms like DISTILL™ are built around this reality, supporting continuous exploration, contextual interpretation, and collaborative reasoning rather than static outputs. ✍️  Read the full blog here: https://lnkd.in/gdZSpKDD In the next part of the series, we will explore how DISTILL™ is reshaping scientific workflows, making iteration faster, collaboration easier, and insight more accessible at scale. #SingleCellAnalysis #SpatialTranscriptomics #MultiOmics

🧬 The Biological Origami: How Machine Learning is Solving Life’s Most Complex Puzzle For decades, the "Protein Folding Problem" was the Everest of structural biology. Scientists knew that the function of a protein—the workhorse of every living cell—was dictated by its complex 3D shape. But predicting that shape from a simple string of amino acids was a task so daunting it was estimated to take longer than the age of the universe using brute-force calculation. Fast forward to 2026: The "AlphaFold moment" has evolved into a full-scale revolution. Read Full blog on: https://lnkd.in/gvZQRTan #ProteinFolding #AlphaFold #Bioinformatics #ML #DL #SRU #DrugDiscovery

Anthropic partners with leading research institutes to tackle biology's data bottleneck Anthropic's focus on building AI agents for biology signals a major industry trend: the shift to deeply specialized, scientific LLMs aimed at solving complex data bottlenecks in R&D. This convergence will radically accelerate drug discovery, automate experimental design, and fundamentally change scientific workflow, requiring businesses to urgently prioritize data infrastructure and agentic skill acquisition to remain competitive. Is biology on the verge of its AI-fueled revolution? How will Anthropic's partnership reshape drug discovery and scientific automation? OR Data bottlenecks in biology holding back breakthroughs? Can AI agents be the key to unlocking scientific potential? Let's discuss! @AnthropicAI Read the full article: https://lnkd.in/guQEayJ9