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Introducing Tahoe-x1 (Tx1) by Tahoe. A 3-billion-parameter, single-cell foundation model that learns unified representations of genes, cells, and drugs, achieving state-of-the-art performance across cancer-relevant cell biology benchmarks, open-sourced on Hugging Face. 🧵

Nima Alidoust

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172,035 просмотров • 10 месяцев назад •via X (Twitter)

Здоровье и велнес Наука и технологии

Anya Rossi• Live Now

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Releasing METAGENE-1: In collaboration with researchers from USC, we're open-sourcing a state-of-the-art 7B parameter Metagenomic Foundation Model. Enabling planetary-scale pathogen detection and reducing the risk of pandemics in the age of exponential biology.

Releasing METAGENE-1: In collaboration with researchers from USC, we're open-sourcing a state-of-the-art 7B parameter Metagenomic Foundation Model. Enabling planetary-scale pathogen detection and reducing the risk of pandemics in the age of exponential biology.

Prime Intellect

622,592 просмотров • 1 год назад

I'm creating 3D tours of open single cell RNA-seq data in UMAP space. Here, using data from 50k brain cells by the allen institute . ( The UMAP position and class of every cell is read into Blender, realized as a stylized, class-specific model I sculpted

I'm creating 3D tours of open single cell RNA-seq data in UMAP space. Here, using data from 50k brain cells by the allen institute . ( The UMAP position and class of every cell is read into Blender, realized as a stylized, class-specific model I sculpted

Brad Krajina

48,144 просмотров • 3 лет назад

The Bitter Lesson for Biology — Adam Green on Scaling Laws for Virtual Cells In this episode, the founder of Markov Biosciences, Adam Green, explains the "bitter lesson" for biology, the idea borrowed from Richard Sutton that large unbiased datasets and the right training objective tend to outcompete models with hard-coded rules and human priors. We talk a lot about virtual cells, sources of bias in data, and his evidence that virtual cells pre-trained on plain observational data show clean scaling laws, getting monotonically better at predicting unseen perturbations as the models grow, and beating a state-of-the-art model built specifically for that task. Our interview was about 3 hours long, but cut to 1.5 hours. My main goal was to understand Adam's perspective on virtual cells and his goal to "solve biology." Search for "The New Biology" on YouTube, Spotify, and Apple Podcasts. Timestamps: 00:00 — Cold open 01:58 — Clinical predictions from a virtual cell 05:38 — What is a "virtual cell"? 08:01 — The problems with single-cell RNA-seq 11:31 — The urns analogy 19:54 — Observational vs. perturbation 23:29 — The bitter lesson for biology 29:06 — Geometric Plackett-Luce 38:27 — Ablations and loss function 47:23 — Cells as specimens 59:26 — Antibody-Drug conjugates 1:11:16 — Will we ever understand the cell fully?

The Bitter Lesson for Biology — Adam Green on Scaling Laws for Virtual Cells In this episode, the founder of Markov Biosciences, Adam Green, explains the "bitter lesson" for biology, the idea borrowed from Richard Sutton that large unbiased datasets and the right training objective tend to outcompete models with hard-coded rules and human priors. We talk a lot about virtual cells, sources of bias in data, and his evidence that virtual cells pre-trained on plain observational data show clean scaling laws, getting monotonically better at predicting unseen perturbations as the models grow, and beating a state-of-the-art model built specifically for that task. Our interview was about 3 hours long, but cut to 1.5 hours. My main goal was to understand Adam's perspective on virtual cells and his goal to "solve biology." Search for "The New Biology" on YouTube, Spotify, and Apple Podcasts. Timestamps: 00:00 — Cold open 01:58 — Clinical predictions from a virtual cell 05:38 — What is a "virtual cell"? 08:01 — The problems with single-cell RNA-seq 11:31 — The urns analogy 19:54 — Observational vs. perturbation 23:29 — The bitter lesson for biology 29:06 — Geometric Plackett-Luce 38:27 — Ablations and loss function 47:23 — Cells as specimens 59:26 — Antibody-Drug conjugates 1:11:16 — Will we ever understand the cell fully?

Niko McCarty.

28,208 просмотров • 2 месяцев назад

How does an embryo reliably "compute" its form - "cell by cell" - using only local interactions and mechanics, yet produce a precise global body plan? I’m excited to share our Nature Methods paper "MultiCell: geometric learning in multicellular development", presenting #AIxBiology research led by Haiqian Yang and the result of a great collaboration with Ming Guo, George Roy, Tomer Stern, Anh Nguyen and Dapeng Bi. A long-standing challenge in developmental biology is to predict how thousands of cells collectively self-organize as tissues fold, divide, and rearrange. In MultiCell, we represent a developing embryo as a dual graph that unifies two complementary views of tissue mechanics with single-cell resolution: cells as moving points (granular) and cells as a connected foam (junction network). This lets the model learn dynamics from both geometry and cell–cell connectivity. On whole-embryo 4D light-sheet movies of Drosophila gastrulation (~5,000 cells), our model predicts key cell behaviors and the timing of events, including junction loss, rearrangements, and divisions with high accuracy, at single-cell resolution. Beyond prediction, the same representation supports robust time alignment across embryos and offers interpretable activation maps that highlight the morphogenetic "drivers" of development. The broader goal is a foundation for cell-by-cell forecasting in more complex tissues, and eventually for detecting subtle dynamical signatures of disease. Kudos to the team for this inspiring collaboration with brilliant researchers to push the boundary of AI for biology! Citation: Yang, H., Roy, G., Nguyen, A.Q., Buehler, M.J., et al. MultiCell: geometric learning in multicellular development. Nature Methods (2025), DOI: 10.1038/s41592-025-02983-x Code/data links are in the manuscript.

How does an embryo reliably "compute" its form - "cell by cell" - using only local interactions and mechanics, yet produce a precise global body plan? I’m excited to share our Nature Methods paper "MultiCell: geometric learning in multicellular development", presenting #AIxBiology research led by Haiqian Yang and the result of a great collaboration with Ming Guo, George Roy, Tomer Stern, Anh Nguyen and Dapeng Bi. A long-standing challenge in developmental biology is to predict how thousands of cells collectively self-organize as tissues fold, divide, and rearrange. In MultiCell, we represent a developing embryo as a dual graph that unifies two complementary views of tissue mechanics with single-cell resolution: cells as moving points (granular) and cells as a connected foam (junction network). This lets the model learn dynamics from both geometry and cell–cell connectivity. On whole-embryo 4D light-sheet movies of Drosophila gastrulation (~5,000 cells), our model predicts key cell behaviors and the timing of events, including junction loss, rearrangements, and divisions with high accuracy, at single-cell resolution. Beyond prediction, the same representation supports robust time alignment across embryos and offers interpretable activation maps that highlight the morphogenetic "drivers" of development. The broader goal is a foundation for cell-by-cell forecasting in more complex tissues, and eventually for detecting subtle dynamical signatures of disease. Kudos to the team for this inspiring collaboration with brilliant researchers to push the boundary of AI for biology! Citation: Yang, H., Roy, G., Nguyen, A.Q., Buehler, M.J., et al. MultiCell: geometric learning in multicellular development. Nature Methods (2025), DOI: 10.1038/s41592-025-02983-x Code/data links are in the manuscript.

Markus J. Buehler

388,264 просмотров • 8 месяцев назад

🔬 Exciting News! Our manuscript, "scGPT: toward building a foundation model for single-cell multi-omics using generative AI" is now finally published in Nature Methods (Nature Methods) 🎉 !!! (Re-)Introducing scGPT: A transformative foundation model engineered for single-cell omics analysis. Developed through the analysis of over 33 million human cells, scGPT sets a new benchmark for application versatility, offering both fine-tuning and zero-shot capabilities. Since its preprint in May 2023, scGPT has significantly impacted the field, evidenced by 13K+ installations, 600+ GitHub stars 🌟, and 40+ citations before its official publication! scGPT has been validated by numerous benchmark studies as a leading foundation model in single-cell analysis. Its pre-trained embeddings extend its utility beyond single-cell studies, enhancing a variety of downstream tasks including protein enrichment and genetic perturbation predictions. Some key updates lately: ---Expanded zero-shot applications for efficient reference mapping and integration, now with CellXGene census integration. ---Advanced perturbation analysis capabilities, including genome-scale perturb-seq data analysis and bulk sequencing data generalization. ---Upgraded scGPT package, offering versatile model loading compatible with PyTorch and flash-attn, for both GPU and CPU. ---Cloud-based scGPT applications for reference mapping, cell annotation, and gene regulatory network inference are available on ---Integration with Hugging Face for easier model training. Limitations: scGPT is an early foray into foundation models for single-cell omics, facing challenges like limited zero-shot learning in some tasks, pretraining constraints, data quality issues, and evaluation limitations. See our Supplementary Notes for details. 🚀 Future Work? Short-Term Goals: 1. Releasing a Mouse Model for broader analysis. 2. Developing a comprehensive evaluation suite for foundation models in single-cell analysis. 3. Creating a foundation model for single-cell spatial omics. 4. Enhancing zero-shot capacity by integrating scGPT with RAG (e.g., knowledge graphs). Long-Term Goals: 1. Expanding scGPT for comprehensive single-cell multi-omics analysis. 2. Developing an in-silico perturbation model for predicting genetic perturbation effects. 3. Merging scGPT with multi-modal genomic sequence models for a deeper understanding of cell biology. 📚 Access the paper on Nature Methods: 🔬Preprint in Bioarixv: 💻 All our codes/data/weights are open source: Wholehearted congratulations to all the authors, especially the two co-first authors, Haotian (Haotian Cui ) and Chloe (ChloeXWang), who are really the emerging superstars in AI and biology! Vector Institute Peter Munk Cardiac Centre AI U of T Department of Computer Science Department of Laboratory Medicine & Pathobiology University Health Network University of Toronto #scGPT #GenerativeAI #AI4Science #Combio #opensource

🔬 Exciting News! Our manuscript, "scGPT: toward building a foundation model for single-cell multi-omics using generative AI" is now finally published in Nature Methods (Nature Methods) 🎉 !!! (Re-)Introducing scGPT: A transformative foundation model engineered for single-cell omics analysis. Developed through the analysis of over 33 million human cells, scGPT sets a new benchmark for application versatility, offering both fine-tuning and zero-shot capabilities. Since its preprint in May 2023, scGPT has significantly impacted the field, evidenced by 13K+ installations, 600+ GitHub stars 🌟, and 40+ citations before its official publication! scGPT has been validated by numerous benchmark studies as a leading foundation model in single-cell analysis. Its pre-trained embeddings extend its utility beyond single-cell studies, enhancing a variety of downstream tasks including protein enrichment and genetic perturbation predictions. Some key updates lately: ---Expanded zero-shot applications for efficient reference mapping and integration, now with CellXGene census integration. ---Advanced perturbation analysis capabilities, including genome-scale perturb-seq data analysis and bulk sequencing data generalization. ---Upgraded scGPT package, offering versatile model loading compatible with PyTorch and flash-attn, for both GPU and CPU. ---Cloud-based scGPT applications for reference mapping, cell annotation, and gene regulatory network inference are available on ---Integration with Hugging Face for easier model training. Limitations: scGPT is an early foray into foundation models for single-cell omics, facing challenges like limited zero-shot learning in some tasks, pretraining constraints, data quality issues, and evaluation limitations. See our Supplementary Notes for details. 🚀 Future Work? Short-Term Goals: 1. Releasing a Mouse Model for broader analysis. 2. Developing a comprehensive evaluation suite for foundation models in single-cell analysis. 3. Creating a foundation model for single-cell spatial omics. 4. Enhancing zero-shot capacity by integrating scGPT with RAG (e.g., knowledge graphs). Long-Term Goals: 1. Expanding scGPT for comprehensive single-cell multi-omics analysis. 2. Developing an in-silico perturbation model for predicting genetic perturbation effects. 3. Merging scGPT with multi-modal genomic sequence models for a deeper understanding of cell biology. 📚 Access the paper on Nature Methods: 🔬Preprint in Bioarixv: 💻 All our codes/data/weights are open source: Wholehearted congratulations to all the authors, especially the two co-first authors, Haotian (Haotian Cui ) and Chloe (ChloeXWang), who are really the emerging superstars in AI and biology! Vector Institute Peter Munk Cardiac Centre AI U of T Department of Computer Science Department of Laboratory Medicine & Pathobiology University Health Network University of Toronto #scGPT #GenerativeAI #AI4Science #Combio #opensource

Bo Wang

199,771 просмотров • 2 лет назад

Introducing INTELLECT-3: Scaling RL to a 100B+ MoE model on our end-to-end stack Achieving state-of-the-art performance for its size across math, code and reasoning Built using the same tools we put in your hands, from environments & evals, RL frameworks, sandboxes & more

Introducing INTELLECT-3: Scaling RL to a 100B+ MoE model on our end-to-end stack Achieving state-of-the-art performance for its size across math, code and reasoning Built using the same tools we put in your hands, from environments & evals, RL frameworks, sandboxes & more

Prime Intellect

1,149,775 просмотров • 9 месяцев назад

🚨Here's what a lot of people misunderstand about cancer treatment, says drpaulmarik: "Cancer is not homogeneous. The somatic mutation theory—which is the current theory in which treatment is based—posits that you have a mutation in a single cell, and that gives rise to a whole population of cells that look the same and have the same mutation. But the Cancer Genome Atlas has shown that that theory is completely wrong. The cancer cells are very heterogeneous, so they're made up of very different populations of cells with different mutations, and one of the populations is the cancer stem cell. It's a sub-population of the cancer. These are generally slow-growing, but they're distinct in that they have the ability to divide indefinitely and grow indefinitely, and can change their characteristics. Basically, if you get rid of the fast-dividing cells, which is the cancer, you're left with the stem cells, which then become the roots, which grow back to form the tumor" sometimes years later. Conventional chemotherapy gets rid of the fast-dividing regular cancer cells but *NOT* the stem cells. So the key question is: how do you get rid of the stem cells? “There are a number of repurposed drugs that do it, and this has been well-established in scientific medical literature. One of the most effective treatments to knock out the stem cell is the famous horse deworming medicine," says drpaulmarik. Yes, ivermectin. Independent Medical Alliance

🚨Here's what a lot of people misunderstand about cancer treatment, says drpaulmarik: "Cancer is not homogeneous. The somatic mutation theory—which is the current theory in which treatment is based—posits that you have a mutation in a single cell, and that gives rise to a whole population of cells that look the same and have the same mutation. But the Cancer Genome Atlas has shown that that theory is completely wrong. The cancer cells are very heterogeneous, so they're made up of very different populations of cells with different mutations, and one of the populations is the cancer stem cell. It's a sub-population of the cancer. These are generally slow-growing, but they're distinct in that they have the ability to divide indefinitely and grow indefinitely, and can change their characteristics. Basically, if you get rid of the fast-dividing cells, which is the cancer, you're left with the stem cells, which then become the roots, which grow back to form the tumor" sometimes years later. Conventional chemotherapy gets rid of the fast-dividing regular cancer cells but NOT the stem cells. So the key question is: how do you get rid of the stem cells? “There are a number of repurposed drugs that do it, and this has been well-established in scientific medical literature. One of the most effective treatments to knock out the stem cell is the famous horse deworming medicine," says drpaulmarik. Yes, ivermectin. Independent Medical Alliance

Jan Jekielek

96,002 просмотров • 1 год назад

David Sinclair's lab just discovered that the same technology that reverses aging also kills cancer cells. His team has been using epigenetic reprogramming to reset old cells back to a younger state. It works. Old cells become young again. But when they tried it on cancer cells, something unexpected happened. The cancer cells didn't get younger. They killed themselves. Sinclair (David Sinclair) explained why. Cancer cells survive by ignoring the DNA damage inside them. They're filled with it. But they've shut down the part of the cell that would normally detect it and trigger self-destruction. Epigenetic reprogramming wakes that system back up. Sinclair put it this way: the cancer cell wakes up from its zombie-like state, looks at its own chromosomes, realizes they're destroyed, and says "I better kill myself." And it does. A normal cell gets reprogrammed and becomes young again. A cancer cell gets reprogrammed and destroys itself. Same technology. Two opposite outcomes. Both exactly what you'd want. His team has shown this works across many types of cancer. Side note: David Sinclair is speaking on May 6th at SynBioBeta this year - discussing the science of slowing and reversing aging. If longevity is the world you're in, the investors, partners, and scientists who matter in this space will be in the room. Link for tickets below.

David Sinclair's lab just discovered that the same technology that reverses aging also kills cancer cells. His team has been using epigenetic reprogramming to reset old cells back to a younger state. It works. Old cells become young again. But when they tried it on cancer cells, something unexpected happened. The cancer cells didn't get younger. They killed themselves. Sinclair (David Sinclair) explained why. Cancer cells survive by ignoring the DNA damage inside them. They're filled with it. But they've shut down the part of the cell that would normally detect it and trigger self-destruction. Epigenetic reprogramming wakes that system back up. Sinclair put it this way: the cancer cell wakes up from its zombie-like state, looks at its own chromosomes, realizes they're destroyed, and says "I better kill myself." And it does. A normal cell gets reprogrammed and becomes young again. A cancer cell gets reprogrammed and destroys itself. Same technology. Two opposite outcomes. Both exactly what you'd want. His team has shown this works across many types of cancer. Side note: David Sinclair is speaking on May 6th at SynBioBeta this year - discussing the science of slowing and reversing aging. If longevity is the world you're in, the investors, partners, and scientists who matter in this space will be in the room. Link for tickets below.

John Cumbers

95,747 просмотров • 5 месяцев назад

1/n: 🔬We introduce NicheCompass (NC) a graph learning model to build spatial atlases across millions of cells, quantitatively characterize cell niches, infer cellular communication, and map new data into these atlases. NC can be used with various spatial technologies, ranging from cellular to subcellular resolution including even multimodal approaches. We have explored interesting biology by analyzing cell niches spanning from development to cancer. Led by Sebastian Birk in a fun colab with @cartalop: paper:

1/n: 🔬We introduce NicheCompass (NC) a graph learning model to build spatial atlases across millions of cells, quantitatively characterize cell niches, infer cellular communication, and map new data into these atlases. NC can be used with various spatial technologies, ranging from cellular to subcellular resolution including even multimodal approaches. We have explored interesting biology by analyzing cell niches spanning from development to cancer. Led by Sebastian Birk in a fun colab with @cartalop: paper:

Mo Lotfollahi

61,775 просмотров • 2 лет назад

Single-cell technologies now let us profile entire transcriptomes in individual cells. But how do we make sense of this complexity in a biologically meaningful way? Many methods summarise cells into a single embedding, but this often comes at the cost of interpretability, especially when multiple gene programs are active at once. We developed Tripso, a self-supervised transformer model that represents cells through multiple gene program-specific embeddings, while also uncovering new programs directly from the data. Instead of collapsing biology into a single vector, Tripso decomposes cell state into multiple representations, each reflecting a different gene program. We explored this across multiple systems. In human hematopoiesis, spanning development to aging, Tripso identified distinct age-associated program activity, including stronger JAK-STAT signalling in early life and dynamic IKZF1-related changes during B cell maturation. By comparing in vitro culture conditions with in vivo hematopoietic stem cell states, Tripso suggested that targeting the SEC61 translocon could enhance stem cell maintenance ex vivo, a prediction that we subsequently validated experimentally. In parallel, we identified a previously uncharacterised tissue-resident memory T-cell program associated with atopic dermatitis and mapped it to distinct spatial immune niches Together, these results show how modelling cells through gene programs can lead to interpretable and experimentally testable insights. More broadly, this work points toward a more interpretable and biologically grounded models of cell state. As single-cell datasets continue to grow, we hope approaches like Tripso will help bridge the gap between data-driven representations and biological insight. This work wouldn’t have been possible without the contributions of an amazing team. Thank you to co-first authors Marie, Tomoya Isobe, Amirhosein Vahidi, Carlo Leonardi, and everyone from roser's Lab, Haniffa Lab, Nicola Wilson and Bertie Gottgens's Lab, bringing together expertise across Cambridge Stem Cell Institute, Open Targets, Wellcome Sanger Institute and Cambridge University. Marie is one of the very best PhD students I have ever supervised. She is truly a force of nature, exceptionally resourceful, deeply innovative, and one of the most impressive scientists I have worked with. I am immensely proud of her and all that she has accomplished. As she begins her internship at Genentech , I have no doubt she will do amazing work there and continue to make her mark. paper: code:

Mo Lotfollahi

22,525 просмотров • 5 месяцев назад

Our vision is for AI that uses world models to adapt in new and dynamic environments and efficiently learn new skills. We’re sharing V-JEPA 2, a new world model with state-of-the-art performance in visual understanding and prediction. V-JEPA 2 is a 1.2 billion-parameter model, trained on video, that can enable zero-shot planning in robots—allowing them to plan and execute tasks in unfamiliar environments. Learn more about V-JEPA 2 ➡️ As we continue working toward our goal of achieving advanced machine intelligence (AMI), we’re also releasing three new benchmarks for evaluating how well existing models can reason about the physical world from video. Learn more and download the new benchmarks ➡️

Our vision is for AI that uses world models to adapt in new and dynamic environments and efficiently learn new skills. We’re sharing V-JEPA 2, a new world model with state-of-the-art performance in visual understanding and prediction. V-JEPA 2 is a 1.2 billion-parameter model, trained on video, that can enable zero-shot planning in robots—allowing them to plan and execute tasks in unfamiliar environments. Learn more about V-JEPA 2 ➡️ As we continue working toward our goal of achieving advanced machine intelligence (AMI), we’re also releasing three new benchmarks for evaluating how well existing models can reason about the physical world from video. Learn more and download the new benchmarks ➡️

AI at Meta

310,507 просмотров • 1 год назад

TRANSFORMATION: A Third Option in Human Biology In nearly every disease, from cancer to Alzheimer’s, cells face two fates: Die. Or survive. But what if we could transform them? That’s what I discovered. This isn't stem cells. This is genetics. Most therapies kill cells (apoptosis) or let them live damaged (survival). But through decoupling PI3K and MTOR among other inside the cell pathways, I found a way to guide cells to reprogram from diseased to healthy. In Jill’s case, this produced 10 molecular “impossibles.” 10 human first biomedical results. It reactivated silenced genes. It reduced tumors. And it did so without killing the cell with the healthy genes being restored. For non-cancer disorders like Severe autism, Alzheimer’s, Huntington's that are genetic degeneration or de-development - transformation is the only way. This is the future of therapeutics: Not war. Not stalling. But biological transformation.

TRANSFORMATION: A Third Option in Human Biology In nearly every disease, from cancer to Alzheimer’s, cells face two fates: Die. Or survive. But what if we could transform them? That’s what I discovered. This isn't stem cells. This is genetics. Most therapies kill cells (apoptosis) or let them live damaged (survival). But through decoupling PI3K and MTOR among other inside the cell pathways, I found a way to guide cells to reprogram from diseased to healthy. In Jill’s case, this produced 10 molecular “impossibles.” 10 human first biomedical results. It reactivated silenced genes. It reduced tumors. And it did so without killing the cell with the healthy genes being restored. For non-cancer disorders like Severe autism, Alzheimer’s, Huntington's that are genetic degeneration or de-development - transformation is the only way. This is the future of therapeutics: Not war. Not stalling. But biological transformation.

Marc Malone

22,753 просмотров • 10 месяцев назад

Dr. Patrick Soon-Shiong on the Cancer-Fighting Immune Breakthrough: Awakening the 450 Million-Year-Old Natural Killer Cell After decades of pioneering work since discovering the natural killer cell in the 1990s—a remarkable immune cell created by God and preserved through 450 million years—Dr. Patrick Soon-Shiong reveals an extraordinary breakthrough. This cell, unknown to many until the 1970s, serves as the body's first line of defense, designed to destroy cancer cells, infections like COVID, sepsis, bacteria, and fungus. Dr. Soon-Shiong emphasizes that this is not an invention, but an enlightenment: activating the body’s own natural killer cells to combat disease naturally, shifting away from toxic chemotherapy and radiation. With a single jab, they have unlocked the secret to empowering this primal cellular protector. This is evolution’s own weapon against illness, a game-changer that taps into ancient biology to revolutionize modern medicine. This insight reframes how we think about health and cancer treatment by harnessing the immune system's innate power, moving beyond harsh treatments, and embracing nature’s original disease-fighter from within. The discovery is a call to awaken the body’s own god-given defense system for a future of empowered healing.

Dr. Patrick Soon-Shiong on the Cancer-Fighting Immune Breakthrough: Awakening the 450 Million-Year-Old Natural Killer Cell After decades of pioneering work since discovering the natural killer cell in the 1990s—a remarkable immune cell created by God and preserved through 450 million years—Dr. Patrick Soon-Shiong reveals an extraordinary breakthrough. This cell, unknown to many until the 1970s, serves as the body's first line of defense, designed to destroy cancer cells, infections like COVID, sepsis, bacteria, and fungus. Dr. Soon-Shiong emphasizes that this is not an invention, but an enlightenment: activating the body’s own natural killer cells to combat disease naturally, shifting away from toxic chemotherapy and radiation. With a single jab, they have unlocked the secret to empowering this primal cellular protector. This is evolution’s own weapon against illness, a game-changer that taps into ancient biology to revolutionize modern medicine. This insight reframes how we think about health and cancer treatment by harnessing the immune system's innate power, moving beyond harsh treatments, and embracing nature’s original disease-fighter from within. The discovery is a call to awaken the body’s own god-given defense system for a future of empowered healing.

Camus

33,407 просмотров • 10 месяцев назад

Excited to share TERRA, a tissue world model 🧬 Over ~1.5 years we ran a large data-generation + modelling effort to build a world model for human tissues, pretrained on 112M cells from spatial transcriptomics (mostly Xenium 5000-plex + public data). It's built on one of the largest human spatial transcriptomics corpora assembled to date, spanning 20 tissues across development, health and 26 disease conditions, ~two-thirds newly generated in-house. Why a "world model" for tissue? Images have universal representations (ViT/DINOv3), so do proteins (ESM, Alex Rives) and pathology (UNI, Faisal Mahmood). We've worked hard to build something similar for human tissue: one model that captures its multi-scale logic, genes → cells → their native microenvironments. Like the JEPA approach Yann LeCun has championed, TERRA learns by prediction in embedding space, but for human tissue. How it works: it tokenises each cell together with its nearest neighbours into one sequence while keeping every gene's identity, then masks part of a neighbourhood and predicts the representation of the hidden part, not raw noisy counts. From one backbone it reads out three scales, gene embeddings (what a gene is doing in a cell and its niche), cell embeddings (cell type and state) and neighbourhood embeddings (the niche), and because it keeps gene-level resolution it can knock a gene out in silico and predict the response. Applied entirely zero-shot, TERRA maps and perturbs human tissue across unseen organs, diseases and technologies, outperforming existing spatial approaches. Three take-homes: 1️⃣ One model, any tissue. A single pretrained backbone provides tissue representations zero-shot, handling genes, cells and niches across organs and platforms, off the shelf. 2️⃣ New biology, development to clinic. We built a new spatial atlas of the developing human pancreas and found an islet-associated capillary state that looks like a precursor of mature islet vasculature. In kidney, TERRA's in silico knockouts predicted the tissue-injury programme from cancer immunotherapy (checkpoint blockade), confirmed in treated kidneys, detected in blood, and linked to declining kidney function. 3️⃣ A grammar of tissue architecture. By coupling each cell's state to its niche, TERRA defines recurring cross-organ "archetypes" of macrophage neighbourhoods, including a tumour-boundary niche that tracks poor survival in kidney cancer. TERRA is already in use: it powered our recent skin atlas of hidden immune-memory niches ( with more studies coming soon. This was an amazing collaboration between clinicians, machine-learning scientists and cell biologists 🙏 Led by Sebastian Birk, Vali Sanian Amirhosein Vahidi, Samuel Ogden, Daniyal Jafree, Adib Miraki, Carlo Leonardi and Arpit Merchant, with Lassi Paavolainen, Menna Clatworthy, Omer Ali Bayraktar, Muzlifah Haniffa, Tom Mitchell and Mostafa Bakhti. Huge thanks too to everyone who shared data and helped along the way. What excites me most is seeing how the community builds on this. The model, code and tutorials are all public, so anyone can run TERRA on their own tissues, extend it, or build new models on top. Huge thanks to the whole team across Wellcome Sanger Institute and our many collaborators. 📄 Paper: 💻 Code: 🤗 Model: #SpatialTranscriptomics #SpatialGenomics #FoundationModels #AI4Science #MachineLearning #ComputationalBiology #SingleCell #WorldModels

Excited to share TERRA, a tissue world model 🧬 Over ~1.5 years we ran a large data-generation + modelling effort to build a world model for human tissues, pretrained on 112M cells from spatial transcriptomics (mostly Xenium 5000-plex + public data). It's built on one of the largest human spatial transcriptomics corpora assembled to date, spanning 20 tissues across development, health and 26 disease conditions, ~two-thirds newly generated in-house. Why a "world model" for tissue? Images have universal representations (ViT/DINOv3), so do proteins (ESM, Alex Rives) and pathology (UNI, Faisal Mahmood). We've worked hard to build something similar for human tissue: one model that captures its multi-scale logic, genes → cells → their native microenvironments. Like the JEPA approach Yann LeCun has championed, TERRA learns by prediction in embedding space, but for human tissue. How it works: it tokenises each cell together with its nearest neighbours into one sequence while keeping every gene's identity, then masks part of a neighbourhood and predicts the representation of the hidden part, not raw noisy counts. From one backbone it reads out three scales, gene embeddings (what a gene is doing in a cell and its niche), cell embeddings (cell type and state) and neighbourhood embeddings (the niche), and because it keeps gene-level resolution it can knock a gene out in silico and predict the response. Applied entirely zero-shot, TERRA maps and perturbs human tissue across unseen organs, diseases and technologies, outperforming existing spatial approaches. Three take-homes: 1️⃣ One model, any tissue. A single pretrained backbone provides tissue representations zero-shot, handling genes, cells and niches across organs and platforms, off the shelf. 2️⃣ New biology, development to clinic. We built a new spatial atlas of the developing human pancreas and found an islet-associated capillary state that looks like a precursor of mature islet vasculature. In kidney, TERRA's in silico knockouts predicted the tissue-injury programme from cancer immunotherapy (checkpoint blockade), confirmed in treated kidneys, detected in blood, and linked to declining kidney function. 3️⃣ A grammar of tissue architecture. By coupling each cell's state to its niche, TERRA defines recurring cross-organ "archetypes" of macrophage neighbourhoods, including a tumour-boundary niche that tracks poor survival in kidney cancer. TERRA is already in use: it powered our recent skin atlas of hidden immune-memory niches ( with more studies coming soon. This was an amazing collaboration between clinicians, machine-learning scientists and cell biologists 🙏 Led by Sebastian Birk, Vali Sanian Amirhosein Vahidi, Samuel Ogden, Daniyal Jafree, Adib Miraki, Carlo Leonardi and Arpit Merchant, with Lassi Paavolainen, Menna Clatworthy, Omer Ali Bayraktar, Muzlifah Haniffa, Tom Mitchell and Mostafa Bakhti. Huge thanks too to everyone who shared data and helped along the way. What excites me most is seeing how the community builds on this. The model, code and tutorials are all public, so anyone can run TERRA on their own tissues, extend it, or build new models on top. Huge thanks to the whole team across Wellcome Sanger Institute and our many collaborators. 📄 Paper: 💻 Code: 🤗 Model: #SpatialTranscriptomics #SpatialGenomics #FoundationModels #AI4Science #MachineLearning #ComputationalBiology #SingleCell #WorldModels

Mo Lotfollahi

50,890 просмотров • 1 месяц назад

Big Pharma spends billions on cancer drugs that may weaken our body’s natural defenses, but Dr. Soon-Shiong reveals a $10 test could hold the key to longevity—keeping your natural killer cells above 1,000 to fight cancer, infections, and more! Dr. Patrick Soon-Shiong unveils a groundbreaking perspective on human health, describing not an invention but an evolution in understanding the body’s defense system. For 450-460 million years, a single cell has enabled mammalian survival against infection, cancer, and trauma: the natural killer (NK) cell. This cell, he argues, forms the foundation of a unifying theory of longevity—a bold claim grounded in decades of research. Soon-Shiong critiques the conventional “whack-a-mole” approach to cancer treatment. He spent years chasing genomic mutations, only to realize that targeting one mutation often spawns another. Big Pharma, he notes, invests billions in mutation-specific drugs, chemotherapy, radiation, and checkpoint inhibitors—treatments that inadvertently suppress the very NK cells critical to life. Even therapies like EPOGEN and NEUPOGEN, designed to support chemotherapy, enable more aggressive treatments that further deplete these vital cells. The solution, he proposes, is startlingly simple: monitor the absolute lymphocyte count (ALC) via a $10 CBC test. NK cells, a subset of lymphocytes, must remain above 1,000 per microliter to maintain robust defense against cancer, infections, and sepsis. Radiation, however, often crashes this count, sometimes for over a year, leaving patients vulnerable to metastasis. A recent study highlights this “badscopal” effect, where radiation, while shrinking one tumor, may increase metastatic spread by wiping out NK cells—contrasting the long-touted “abscopal” benefit of systemic tumor reduction. Aging, viral infections, sepsis, and cancer all correlate with declining NK cell levels. Yet, Soon-Shiong emphasizes, these cells possess an innate ability to recognize and destroy transforming cells before they become cancerous. This insight shifts the paradigm from attacking disease to bolstering the body’s natural defenses. The challenge now is to rethink medical approaches. “Follow the science,” Soon-Shiong urges, advocating for data-driven strategies over entrenched agendas. By preserving NK cell counts, we may unlock the secret to long, healthy lives. This isn’t just a theory—it’s a call to transform how we fight disease.

Big Pharma spends billions on cancer drugs that may weaken our body’s natural defenses, but Dr. Soon-Shiong reveals a $10 test could hold the key to longevity—keeping your natural killer cells above 1,000 to fight cancer, infections, and more! Dr. Patrick Soon-Shiong unveils a groundbreaking perspective on human health, describing not an invention but an evolution in understanding the body’s defense system. For 450-460 million years, a single cell has enabled mammalian survival against infection, cancer, and trauma: the natural killer (NK) cell. This cell, he argues, forms the foundation of a unifying theory of longevity—a bold claim grounded in decades of research. Soon-Shiong critiques the conventional “whack-a-mole” approach to cancer treatment. He spent years chasing genomic mutations, only to realize that targeting one mutation often spawns another. Big Pharma, he notes, invests billions in mutation-specific drugs, chemotherapy, radiation, and checkpoint inhibitors—treatments that inadvertently suppress the very NK cells critical to life. Even therapies like EPOGEN and NEUPOGEN, designed to support chemotherapy, enable more aggressive treatments that further deplete these vital cells. The solution, he proposes, is startlingly simple: monitor the absolute lymphocyte count (ALC) via a $10 CBC test. NK cells, a subset of lymphocytes, must remain above 1,000 per microliter to maintain robust defense against cancer, infections, and sepsis. Radiation, however, often crashes this count, sometimes for over a year, leaving patients vulnerable to metastasis. A recent study highlights this “badscopal” effect, where radiation, while shrinking one tumor, may increase metastatic spread by wiping out NK cells—contrasting the long-touted “abscopal” benefit of systemic tumor reduction. Aging, viral infections, sepsis, and cancer all correlate with declining NK cell levels. Yet, Soon-Shiong emphasizes, these cells possess an innate ability to recognize and destroy transforming cells before they become cancerous. This insight shifts the paradigm from attacking disease to bolstering the body’s natural defenses. The challenge now is to rethink medical approaches. “Follow the science,” Soon-Shiong urges, advocating for data-driven strategies over entrenched agendas. By preserving NK cell counts, we may unlock the secret to long, healthy lives. This isn’t just a theory—it’s a call to transform how we fight disease.

Camus

40,176 просмотров • 1 год назад

The Chemotherapy Paradox – A "Cure" That Preserves the Root of Cancer? You've been told chemotherapy is a cornerstone of cancer care. But what if the standard of care is fundamentally flawed, suppressing the very system designed to heal you while protecting the engine that drives the disease? In a stunning exposition, Dr. Paul Marik pulls back the curtain on oncology's biggest dilemma. Here's the shocking truth: - Chemotherapy annihilates your immune army. It wipes out your natural killer cells and T-cells—the very soldiers your body needs to fight cancer. You are immune-suppressed, allowing the tumor a clearer path to proliferate. - Chemotherapy preserves the cancer stem cell. This is the root of the tumor. While chemo kills rapidly dividing cells, it often leaves the stem cell—the queen bee—untouched. From this root, the cancer indefinitely divides, mutates, and regrows. - Some chemo drugs may even STIMULATE the stem cell. That's right. The very treatment intended to kill cancer can, in some cases, fuel its source. "So you can't cure the patient unless you get rid of the cancer stem cell," states Dr. Marik. "Interestingly, chemotherapy doesn't kill the stem cell." This explains why "remission" is not a "cure." The cancer can return 7, 8, or 10 years later because the root was never addressed. You are in remission, not cured. The conclusion? The high-dose, "burn-and-cut" approach of traditional oncology is not holistic. It weakens the host and empowers the enemy's most resilient forces. Dr. Marik confirms: The efficacy depends on the tumor type. Cancers with a low percentage of stem cells can see long-term remission. But for many, it's a ticking time bomb. This isn't an opinion; it's a biological reality. It’s time for a paradigm shift. Share this to spark a crucial conversation. The future of oncology depends on it.

The Chemotherapy Paradox – A "Cure" That Preserves the Root of Cancer? You've been told chemotherapy is a cornerstone of cancer care. But what if the standard of care is fundamentally flawed, suppressing the very system designed to heal you while protecting the engine that drives the disease? In a stunning exposition, Dr. Paul Marik pulls back the curtain on oncology's biggest dilemma. Here's the shocking truth: - Chemotherapy annihilates your immune army. It wipes out your natural killer cells and T-cells—the very soldiers your body needs to fight cancer. You are immune-suppressed, allowing the tumor a clearer path to proliferate. - Chemotherapy preserves the cancer stem cell. This is the root of the tumor. While chemo kills rapidly dividing cells, it often leaves the stem cell—the queen bee—untouched. From this root, the cancer indefinitely divides, mutates, and regrows. - Some chemo drugs may even STIMULATE the stem cell. That's right. The very treatment intended to kill cancer can, in some cases, fuel its source. "So you can't cure the patient unless you get rid of the cancer stem cell," states Dr. Marik. "Interestingly, chemotherapy doesn't kill the stem cell." This explains why "remission" is not a "cure." The cancer can return 7, 8, or 10 years later because the root was never addressed. You are in remission, not cured. The conclusion? The high-dose, "burn-and-cut" approach of traditional oncology is not holistic. It weakens the host and empowers the enemy's most resilient forces. Dr. Marik confirms: The efficacy depends on the tumor type. Cancers with a low percentage of stem cells can see long-term remission. But for many, it's a ticking time bomb. This isn't an opinion; it's a biological reality. It’s time for a paradigm shift. Share this to spark a crucial conversation. The future of oncology depends on it.

Camus

69,662 просмотров • 9 месяцев назад

Anti-Parasitic. Anti-Viral. Anti-Cancer. Two Drugs Already in Your Pharmacy. Ivermectin and mebendazole were developed to fight parasites. Then studies found they also had antiviral properties. Now a patient-reported outcomes study shows anti-cancer effects across nearly 200 patients. The same mechanisms that starve parasites — cutting off proliferation, blocking blood supply, triggering cell death — work on cancer cells too. The science was there. Nobody funded the trials. Join the Fight: Courtesy of The Amber May Show #MedicalFreedom

Anti-Parasitic. Anti-Viral. Anti-Cancer. Two Drugs Already in Your Pharmacy. Ivermectin and mebendazole were developed to fight parasites. Then studies found they also had antiviral properties. Now a patient-reported outcomes study shows anti-cancer effects across nearly 200 patients. The same mechanisms that starve parasites — cutting off proliferation, blocking blood supply, triggering cell death — work on cancer cells too. The science was there. Nobody funded the trials. Join the Fight: Courtesy of The Amber May Show #MedicalFreedom

McCullough Foundation

31,580 просмотров • 4 месяцев назад