Genie TechBio Inc.

Imagine a tool that understands life's genetic code—Evo, a newly unveiled #AI model, is doing just that. Trained on millions of microbial genomes, #Evo is a genomic #foundationmodel that: 🧬 Predicts fitness impacts of single-nucleotide changes. 🧪 Designs synthetic biological systems, such as #CRISPR-Cas and transposable elements, with experimental validation. 📏 Handles sequences at genome scale (up to 1 megabase) while maintaining single-nucleotide resolution. Built with cutting-edge #deeplearning techniques, Evo excels at #multimodal analysis (simultaneous interpretation of DNA, RNA, and protein data) and generation across these biological layers. This model integrates the complexities of the central dogma with the multiscale nature of #evolution, paving the way for innovations in #synbio and #genome #engineering. At Genie TechBio Inc., we are super excited about this vision of leveraging large language models to accelerate biological research and engineering. Evo exemplifies the transformative potential of AI in understanding life at its most fundamental levels—a mission that drives us every day. Dive into the science behind this transformative advancement here: https://lnkd.in/gXNd6pT9 And congratulations on the amazing work Arc Institute #AI #Genomics #SyntheticBiology #DeepLearning #CRISPR #LLM

It’s tough to see another wave of #layoffs sweeping through the #biotech industry, with 23andMe’s recent announcement of a 40% staff reduction, following similar moves earlier this year by Freenome, Intellia Therapeutics, Inc., Black Diamond Therapeutics, Singular Genomics, and Bionano Genomics. To those impacted, especially during the holiday season, we understand it’s a difficult time – but it could also be the start of something new and meaningful. At Genie TechBio Inc., we’re creating the world’s first AI bioinformatician to make bioinformatics faster, more affordable, and—most importantly—accessible to all in biotech research. This is an incredible opportunity to join a super early-stage startup where you can help build a new approach to science from the ground up. If you’re looking to apply your expertise in #AI, #bioinformatics, or #computationalbiology, let’s connect! Be part of a team that’s reimagining what’s possible in research and creating innovative solutions for scientists worldwide. 🚀 Reach out if you’re ready to explore something groundbreaking. #biotech #layoffs #innovation #bioinformatics #AI https://lnkd.in/ddXEr547

𝗧𝗵𝗲 𝗙𝘂𝘁𝘂𝗿𝗲 𝗼𝗳 𝗦𝗰𝗶𝗲𝗻𝗰𝗲: 𝗦𝗵𝗮𝗽𝗶𝗻𝗴 𝗔𝗜, 𝗼𝗿 𝗟𝗲𝘁𝘁𝗶𝗻𝗴 𝗔𝗜 𝗦𝗵𝗮𝗽𝗲 𝗨𝘀? 🧬🤖 The recent article 𝘌𝘮𝘱𝘰𝘸𝘦𝘳𝘪𝘯𝘨 𝘉𝘪𝘰𝘮𝘦𝘥𝘪𝘤𝘢𝘭 𝘋𝘪𝘴𝘤𝘰𝘷𝘦𝘳𝘺 𝘸𝘪𝘵𝘩 𝘈𝘐 𝘈𝘨𝘦𝘯𝘵𝘴 offers a thought-provoking glimpse into our scientific future and the role that AI in transforming the scientific discovery process. The authors envision "AI scientists"—agents that integrate AI models, biomedical tools, and experimental platforms, with the power to learn, reason, and collaborate with human researchers. Rather than replacing humans, these AI agents will work alongside us, combining human creativity with AI's strength in analyzing large datasets, designing experiments, and navigating complex hypothesis spaces. These AI systems can automate repetitive tasks, improve efficiency, and even predict biological behaviors before experimental data is available. The potential to simulate virtual cells, design cellular circuits, and develop therapies offers a glimpse into the future of personalized medicine and faster biomedical discovery. The role of human scientists is far from diminished. Instead, AI agents are designed to amplify our capabilities, allowing us to focus on creative problem-solving and hypothesis generation, while leaving the heavy lifting of data processing to the agents. The integration of LLMs and machine learning tools enables these agents to learn continually, refine their knowledge, and adapt to new insights. It’s an exciting prospect—AI agents that can formulate hypotheses, evaluate them critically, and refine their knowledge with human guidance. At Genie TechBio Inc., we are deeply inspired by this vision of AI-enhanced research. Our AI bioinformatician combines cutting-edge natural language processing with the flexibility needed to empower biologists to conduct omics analysis in a more intuitive, collaborative way—enabling researchers to ask questions and get answers in natural language, without the need for coding expertise. We highly recommend reading the full original article to learn more about the potential of AI agents in biomedical research: https://lnkd.in/d7JktR3T What do you think—could AI be the partner that propels us to the next major scientific breakthrough? #AIinScience #BiomedicalAI #ResearchInnovation #FutureOfMedicine #GenieTechBio

See you soon Life Sciences Tech Network - Berlin!

If you’re working in or on omics and happen to be in #Berlin on November 21, join us for an evening of drinks and biotech networking! 🍻 Whether you’re in genomics, proteomics, transcriptomics—or anything in between—come by to share insights, explore potential collaborations, or just enjoy a beer with the local biotech community. We look forward to meeting fellow researchers, founders, and industry pros making strides in omics! 👉 Sign up here to let us know you’re coming: https://lu.ma/lt4sy32w

𝗨𝗠𝗔𝗣 𝘃𝘀 𝗣𝗖𝗔 𝗶𝗻 𝘀𝗰𝗥𝗡𝗔-𝘀𝗲𝗾: 𝗞𝗲𝘆 𝗗𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝗰𝗲𝘀 𝗮𝗻𝗱 𝗖𝗼𝗺𝗺𝗼𝗻 𝗠𝗶𝘀𝘂𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱𝗶𝗻𝗴𝘀 When analyzing #scRNAseq data, #PCA (Principal Component Analysis) and #UMAP (Uniform Manifold Approximation and Projection) are essential tools for dimensionality reduction, but they serve different purposes. 🔍 𝗣𝗖𝗔: Linear method that finds the directions (principal components) with the most variance. 𝗕𝗲𝘀𝘁 𝗳𝗼𝗿: Understanding overall data variance and trends. 𝗖𝗼𝗺𝗺𝗼𝗻 𝗺𝗶𝘀𝘂𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱𝗶𝗻𝗴: “PCA should always separate cell types clearly.” PCA captures large-scale variance, which might be technical noise, not necessarily biological differences. 🔍 𝗨𝗠𝗔𝗣: Non-linear method that preserves local relationships between cells. 𝗕𝗲𝘀𝘁 𝗳𝗼𝗿: Visualizing cell clusters and complex data structures. 𝗖𝗼𝗺𝗺𝗼𝗻 𝗺𝗶𝘀𝘂𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱𝗶𝗻𝗴: “UMAP distance equals biological distance.” UMAP focuses on local structures, so distances between clusters may not directly reflect biological differences. 𝗞𝗲𝘆 𝗗𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝗰𝗲𝘀: 𝗟𝗶𝗻𝗲𝗮𝗿 𝘃𝘀 𝗡𝗼𝗻-𝗹𝗶𝗻𝗲𝗮𝗿: PCA finds linear relationships, while UMAP captures complex, non-linear structures. 𝗩𝗮𝗿𝗶𝗮𝗻𝗰𝗲 𝘃𝘀 𝗡𝗲𝗶𝗴𝗵𝗯𝗼𝗿𝗵𝗼𝗼𝗱 𝗣𝗿𝗲𝘀𝗲𝗿𝘃𝗮𝘁𝗶𝗼𝗻: PCA maximizes variance, UMAP preserves local relationships, making it better for clustering. 𝗚𝗹𝗼𝗯𝗮𝗹 𝘃𝘀 𝗟𝗼𝗰𝗮𝗹: PCA captures global trends, UMAP excels at revealing small, closely related cell populations. 𝗧𝗮𝗸𝗲𝗮𝘄𝗮𝘆: Use #PCA for an initial overview and #UMAP for detailed exploration of cell types and relationships. Remember: these tools guide you, but always validate your findings with biological markers. 🔗 At Genie TechBio Inc., we help streamline #scRNAseq analysis so you can focus on the #biology. Interested in learning more? Let's connect!

🔍 𝗖𝗮𝗻 𝗚𝗣𝗧-𝟰 𝗥𝗲𝗹𝗶𝗮𝗯𝗹𝘆 𝗜𝗺𝗽𝗿𝗼𝘃𝗲 𝗖𝗲𝗹𝗹 𝗧𝘆𝗽𝗲 𝗔𝗻𝗻𝗼𝘁𝗮𝘁𝗶𝗼𝗻 𝗶𝗻 𝗦𝗶𝗻𝗴𝗹𝗲-𝗖𝗲𝗹𝗹 𝗥𝗡𝗔-𝘀𝗲𝗾 𝗔𝗻𝗮𝗹𝘆𝘀𝗶𝘀? A recent Nature Methods publication by Wenpin H. (Columbia University) and zhicheng ji (Duke University School of Medicine) demonstrates that #GPT-4 can accurately and efficiently annotate cell types in #scRNAseq data, with results closely aligning with manual annotations in over 75% of cases. 𝗞𝗲𝘆 𝗵𝗶𝗴𝗵𝗹𝗶𝗴𝗵𝘁𝘀: 𝙊𝙥𝙩𝙞𝙢𝙞𝙯𝙚𝙙 𝙬𝙞𝙩𝙝 𝙩𝙝𝙚 𝙩𝙤𝙥 10 𝙙𝙞𝙛𝙛𝙚𝙧𝙚𝙣𝙩𝙞𝙖𝙡 𝙜𝙚𝙣𝙚𝙨: GPT-4 performs best using differential genes derived from the Wilcoxon test. 𝙒𝙞𝙙𝙚 𝙖𝙥𝙥𝙡𝙞𝙘𝙖𝙗𝙞𝙡𝙞𝙩𝙮:It excels in annotating immune cells and major cell types like T cells, while providing granular insights for more complex cell types. 𝙎𝙥𝙚𝙚𝙙 𝙖𝙣𝙙 𝙚𝙛𝙛𝙞𝙘𝙞𝙚𝙣𝙘𝙮: Integrating directly into analysis pipelines like #Seurat, #GPT4 is faster and more cost-effective than traditional methods. 𝙍𝙤𝙗𝙪𝙨𝙩𝙣𝙚𝙨𝙨 𝙞𝙣 𝙘𝙤𝙢𝙥𝙡𝙚𝙭 𝙨𝙘𝙚𝙣𝙖𝙧𝙞𝙤𝙨: Even with noisy or mixed data, #GPT4 maintains high reproducibility, distinguishing pure from mixed cell types with 93% accuracy. Despite its strengths, human validation remains key to avoid AI errors and ensure accuracy. Could fine-tuning GPT-4 with curated gene lists further boost its performance? How can researchers balance automation with expert oversight in large datasets? Read the full paper here: https://lnkd.in/eF2SCSkx #Bioinformatics #SingleCell #AI #scRNAseq #NatureMethods #GPT4 #GeneAnnotation

𝗘𝘅𝗽𝗹𝗼𝗿𝗶𝗻𝗴 𝘁𝗵𝗲 𝗧𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗮𝘁𝗶𝘃𝗲 𝗜𝗺𝗽𝗮𝗰𝘁 𝗼𝗳 𝗢𝗺𝗶𝗰𝘀 𝗶𝗻 𝗖𝗲𝗹𝗹 𝗧𝗵𝗲𝗿𝗮𝗽𝘆 #Celltherapy stands at the cutting edge of medical innovation, offering hope for treating complex diseases like cancer and #autoimmunedisorders. By harnessing the body’s own cells—or engineering them for enhanced functionality—#celltherapy aims to restore and maintain health in unprecedented ways. But to fully unlock its potential, omics technologies are proving to be the game-changer. 𝙒𝙝𝙮 #𝙊𝙢𝙞𝙘𝙨 𝙞𝙨 𝘾𝙧𝙪𝙘𝙞𝙖𝙡 𝙛𝙤𝙧 𝙩𝙝𝙚 𝘼𝙙𝙫𝙖𝙣𝙘𝙚𝙢𝙚𝙣𝙩 𝙤𝙛 𝘾𝙚𝙡𝙡 𝙏𝙝𝙚𝙧𝙖𝙥𝙮: #𝙂𝙚𝙣𝙤𝙢𝙞𝙘 𝙄𝙣𝙨𝙞𝙜𝙝𝙩𝙨: Deep genomic analysis enables us to assess genetic modifications in engineered cells, ensuring they function as designed. This level of understanding not only improves therapeutic precision but also helps minimize risks associated with off-target effects. #𝙏𝙧𝙖𝙣𝙨𝙘𝙧𝙞𝙥𝙩𝙞𝙤𝙣𝙖𝙡 𝙋𝙧𝙤𝙛𝙞𝙡𝙞𝙣𝙜: By tracking #geneexpression patterns, researchers can evaluate how different cell types respond to treatments. This helps tailor therapies to individual patient profiles, maximizing efficacy and minimizing adverse effects. #𝙋𝙧𝙤𝙩𝙚𝙤𝙢𝙞𝙘 𝙈𝙖𝙧𝙠𝙚𝙧𝙨: Monitoring the proteins that cells express provides critical insights into their functionality. These markers can indicate whether a cell therapy is working as intended and help clinicians adjust treatments based on real-time patient responses. #𝙈𝙚𝙩𝙖𝙗𝙤𝙡𝙞𝙘 𝙏𝙧𝙖𝙘𝙠𝙞𝙣𝙜: #Metabolism offers a window into cell health. Understanding shifts in #metabolic pathways allows researchers to fine-tune #therapies for optimal cell viability, ensuring that treatments not only work in the short term but also sustain long-term benefits. At Genie TechBio Inc., we’re helping to drive this innovation forward. By enabling #biologists to analyze #omics data without the need for coding, we’re making it easier to uncover the #genomic, #transcriptomic, #proteomic, and metabolic insights necessary to refine and #personalize #celltherapies. With our AI bioinformatician, researchers can explore complex datasets with ease, empowering them to make breakthroughs faster and more efficiently. The integration of #multiomics into cell therapy is far from a passing trend—it’s becoming the cornerstone for safer, more effective treatments. As we continue to refine these tools, we move closer to fully #personalizedtherapies, designed to meet the unique needs of each patient. 🔍 Curious to learn more about the companies at the forefront of this space? Check out these pioneers driving innovation in #CellTherapy and #Omics: CARGO Therapeutics, ADAPTIMMUNE THERAPEUTICS PLC, Atara Biotherapeutics, Aspen Neuroscience, Inc., MaxCyte, Inc., Gamida Cell Ltd., Allogene Therapeutics, Anixa Biosciences, Inc., Autolus Therapeutics, Cartesian Therapeutics, Kite Pharma, Legend Biotech

We are sure that we are in the right path with these double prizes from Physics yesterday, and now Chemistry!