HTANALYSTS’ Post

🔬Quantum Computing in Medicine: Revolutionizing Healthcare🏥 Quantum computing is not just a technological marvel; it's poised to transform the medical field in profound ways. From drug discovery to personalized medicine, the potential applications are vast and revolutionary. Here’s how quantum computing is set to make an impact: 1. Accelerating Drug Discovery: Traditional drug discovery processes are time-consuming and expensive, often taking years to develop new treatments. Quantum computers can simulate molecular interactions at an unprecedented scale and speed, drastically reducing the time needed to identify and develop new drugs. 2. Personalized Medicine: Every individual’s genetic makeup is unique, and quantum computing can analyze vast amounts of genetic data quickly and accurately. This enables the development of personalized treatment plans tailored to an individual’s specific genetic profile, improving the effectiveness of therapies. 3. Optimizing Clinical Trials: Quantum algorithms can optimize the design and analysis of clinical trials by efficiently managing variables and analyzing complex datasets. This leads to more effective trial designs, quicker patient recruitment, and faster results, accelerating the overall drug development process. 4. Enhanced Medical Imaging: Quantum computing can improve the processing and analysis of medical images, such as MRIs and CT scans. By enhancing image resolution and reducing noise, quantum algorithms can provide clearer and more accurate diagnostics, leading to better patient outcomes. 5. Genomic Research: Quantum computers can handle the enormous datasets involved in genomic research, identifying patterns and correlations that are difficult for classical computers to detect. This can lead to breakthroughs in understanding genetic diseases and developing targeted gene therapies. 6. Protein Folding: Understanding how proteins fold is crucial for many biological processes and disease mechanisms. Quantum computing can model protein folding more accurately than classical methods, aiding in the development of new treatments for diseases like Alzheimer’s and Parkinson’s. 7. Epidemiology and Public Health: Quantum computing can analyze complex models of disease spread and interactions, helping public health officials make more accurate predictions and better prepare for and respond to epidemics and pandemics. 8. Complex System Simulations: Quantum computers can simulate biological systems in ways that classical computers cannot, providing deeper insights into complex biological processes and systems. This can lead to new discoveries in cell biology, neuroscience, and other fields. The integration of quantum computing in medicine holds the promise. As we continue to explore and harness the power of quantum technology, the future of medicine looks brighter than ever. 🌟🔗 #QuantumComputing #MedicalInnovation #HealthcareRevolution #FutureOfMedicine #TechInHealthcare #QuantumLeap

A new machine learning model has uncovered an important checkpoint in the process of muscle cell development. The model showed that when an enzyme's activity was inhibited, the muscle cells got "stuck" in a ready-to-fuse stage, unable to complete the fusion process that forms new muscle fibers. This discovery not only advances our understanding of muscle biology, but also demonstrates the power of AI and computational models to uncover critical insights in complex biological systems. The researchers believe this approach could be applied to study other dynamic biological processes. Interestingly, this discovery required collaboration between researchers at the Weizmann Institute of Science, and other scientific teams. Working together across disciplines was essential to fully understand this important biological checkpoint. https://lnkd.in/ePZgp5UQ #AI #Biology #MuscleResearch #TeamScience

Why do we use imaging data to understand biology? In a new video, Imran Haque, SVP of AI and Digital Sciences, walks us through the treasure trove of data found in images, explaining how we use algorithms to structure pixels, how we are leading discovery in the field, and the massive advances that have been made in training foundation models to make new predictions of promising therapeutic targets. 🔹 Some highlights: ◾ Imaging and algorithms can provide as much or more information than RNA sequencing and cost far less money, so you can run more experiments – and you don’t have to kill the cells to do it.  ◾ With ML foundation models acting as a lever, we only need a small amount of data to perform as well as a much larger set. With areas as vast as chemistry and biology, you want to build as much as you can to break these scale barriers.  ◾ The models – called masked autoencoders – can reconstruct some 75-90% of the images of cells that have been masked. ◾ Once trained, we use these models to turn an unmasked image into a list of numbers and then match these to the database of images of all the cells we’ve taken in the past to see how similar or different the biology is that is created by different conditions (or perturbations) for those cells..  ◾ This technique – called similarity comparison – is central to our digital maps of biology. It’s how we discovered RBM39 provided a new means to inhibit CDK12, a promising therapeutic target for cancer. Full video here: https://lnkd.in/ebdHGfZG #ai #ml #tech #techbio #chemistry #biology #models

"Noting that recent advances in artificial intelligence and the existence of large-scale experimental data about human biology have reached a critical mass, a team of researchers from Stanford University, Genentech, and the Chan-Zuckerberg Initiative says that science has an "unprecedented opportunity" to use artificial intelligence (AI) to create the world's first virtual human cell. Such a cell would be able to represent and simulate the precise behavior of human biomolecules, cells, and, eventually, tissues and organs. "Modeling human cells can be considered the holy grail of biology," said Emma Lundberg, associate professor of bioengineering and of pathology in the schools of Engineering and Medicine at Stanford and a senior author of a new article in the journal Cell proposing a concerted, global effort to create the world's first AI virtual cell. "AI offers the ability to learn directly from data and to move beyond assumptions and hunches to discover the emergent properties of complex biological systems."" #ai #digitaltwin #virtualcell

This year’s Nobel prizes in physiology/medicine, physics, and chemistry were awarded for research in fields that shape the future of healthcare. Congratulations to: - The Nobel Prize winners in physiology/medicine: Victor Ambros and Gary Ruvkun for the discovery of microRNA which opened up a new dimension in gene regulation. The essential function of microRNA in protein production influences all processes in the human body and helps explain the development of cancer and diabetes. - the Nobel Prize winners in physics: John J. Hopfield and Geoffrey E. Hinton for their discoveries that enable machine learning with artificial neural networks. In the 1980s, they laid the foundation for Large Language Models (LLM) and AI-assisted medical image analysis. - the Nobel Prize winners in chemistry: David Baker for computational protein design that led to new drugs and vaccines; Demis Hassabis and John Jumper for developing the AI model called “AlphaFold2” to solve a 50-year-old problem and predict the structure of virtually all the 200 million proteins that researchers have identified. Their groundbreaking research could help explain antibiotics resistance and use enzymes to break down plastic.   Such discoveries enable quantum leaps in healthcare, like it was the case with mRNA vaccines during the Covid19 pandemic. Remarkably, AI was an integral part of this research. And I am convinced that AI will lead to further quantum leaps in healthcare, massively speeding up the development of vital medicines – and make them more accessible.   Just another reason why we will host a panel at this year’s World Health Summit to discuss how we can increase trust into new technologies and AI among patients and healthcare professionals. These three Nobel Prizes are leading perfectly into our discussion on Monday next week. Looking forward to it! #NobelPrize #CommittedToLife Photo: Clément Morin © Nobel Prize Outreach.

How good are LLMs at science? Our Research team developed a benchmarking dataset to evaluate LLM capabilities on complex, nuanced questions in STEM subjects. Then we tested 5 LLMs: Qwen2-7B-Instruct, Llama-3-8B-Instruct, Mixtral-8x7B, Gemini-1.0-pro, and GPT-4o. How the benchmark helps reveal strengths and weaknesses of models: 🔹 Dataset of 180 challenging, open-ended questions (no multiple choice!) in 10 subdomains 🔹 Collaboration with researchers and domain experts in fields like high-energy physics, immunology, and cell biology 🔹 Results vary across subdomains — surprisingly, Llama pulled ahead of GPT-4o in Bioinformatics What did we find? All the models fell short of evaluators’ expectations. Notably, the model responses appear correct, but they can be misleading to non-experts. Consumers should be extra cautious about using LLMs for complex questions. Check out our blog for insights into our research, the dataset, and the implications for the future of AI in science. 👉 https://bit.ly/40iQMVs Working on LLM eval in a specific domain? Let’s chat https://bit.ly/3YUM67F #AI #STEM #LLM #Benchmarking #LLAMA #GPT4o

🔬 From Fundamental Discoveries to AI-Driven Innovation: How Nobel-Winning Research Transformed Biophysics and Biochemistry 🌟 The evolution of biophysics and biochemistry has been remarkable, moving from groundbreaking discoveries to the cutting-edge application of Artificial Intelligence (AI)! This journey is exemplified by several Nobel Prizes that highlight the fusion of foundational concepts with AI-enhanced research. 1953: Hermann Staudinger won the Nobel Prize for macromolecular chemistry, laying the groundwork for understanding biomolecules like proteins and DNA. 🧬 1962: Francis Crick, James Watson, and Maurice Wilkins unveiled the structure of DNA, opening the door for AI in biomolecular studies. 🔍 1997: Paul D. Boyer, John E. Walker, and Jens C. Skou were honored for their work on ATP synthesis, paving the way for AI-enhanced modeling of molecular machines. ⚙️ AI's impact has accelerated further: 2017: Jacques Dubochet, Joachim Frank, and Richard Henderson revolutionized structural biology with cryo-electron microscopy, with AI improving data processing. ❄️ 2020: Emmanuelle Charpentier and Jennifer Doudna's CRISPR-Cas9 breakthrough has been optimized by AI for gene editing. ✂️ 2023: Katalin Karikó and Drew Weissman advanced mRNA technology, which AI now enhances for stability and efficacy. 💉 The 2024 Nobel Prizes further showcase AI's influence, with John J. Hopfield and Geoffrey E. Hinton recognized for their contributions to artificial neural networks—foundational to modern AI systems! 🧠 As we embrace an AI-driven era, scientists must focus on continuous learning, interdisciplinary collaboration, data literacy, and ethical AI use to lead the next wave of scientific breakthroughs! 🌊 #AI #Biophysics #Biochemistry #Innovation #Research #Science 🚀

Organoid Intelligence (OI) - biological computers grown from stem cells to mimic organ functionality. The aim of OI is to create biological processors. The justification is that biologic computers are superior in efficiency, performance and robustness. Using stem cells to grow organoids which then act on some input automatically raises ethical concerns. In healthcare, this is useful for studying organs and their functionality outside of a human subject. Outside of healthcare, something like a brain organoid could potentially be used to augment AI and BI. Should we be doing this? https://lnkd.in/gkQDRRSy

"These parallel revolutions [AI and omics] provide an unprecedented opportunity for an ambitious vision of an AI virtual cell (AIVC), a multi-scale, multi-modal, large-neural-network-based model that can represent and simulate the behavior of molecules, cells and tissues across diverse states." #ai #algorithm #health #medicine #biology

Scientists call for all-out, global effort to create an AI virtual cell A team of leading scientists says that advances in artificial intelligence and masses of experimental data have put a virtual cell within reach. But getting there will take a global collaboration like never before. Noting that recent advances in artificial intelligence and the existence of large-scale experimental data about human biology have reached a critical mass, a team of researchers from Stanford University, Genentech, and the Chan Zuckerberg Initiative says that science has an “unprecedented opportunity” to use artificial intelligence (AI) to create the world’s first virtual human cell. Such a cell would be able to represent and simulate the precise behavior of human biomolecules, cells, and, eventually, tissues and organs. “Modeling human cells can be considered the holy grail of biology,” said Emma Lundberg, associate professor of bioengineering and of pathology in the schools of Engineering and Medicine at Stanford and a senior author of a new article in the journal Cell proposing a concerted, global effort to create the world’s first AI virtual cell. “AI offers the ability to learn directly from data and to move beyond assumptions and hunches to discover the emergent properties of complex biological systems.” Remarkable promise Such a synthetic cell model would both allow a deeper understanding of the complex interplay of chemical, electrical, mechanical, and other forces and processes that make healthy human cells work, and also reveal the root causes of disease that lead to cell dysfunction or death. Perhaps more intriguingly, an AI virtual cell would also allow scientists to experiment in silico instead of in vivo – on a computer rather than on living cells and organisms. This ability would expand human understanding of human biology and speed the search for new therapies, pharmaceuticals, and perhaps cures to disease. Cancer biologists might model how certain mutations turn healthy cells malignant. #AIforScience #VirtualCell #HumanBiology #AIinHealthcare #CellModeling #ArtificialIntelligence #Bioengineering #DiseaseResearch #DeepSpaceBiology #InnovationInScience #GlobalCollaboration #HealthcareAdvances #FutureOfMedicine #CancerResearch #InSilicoExperiments