Master's Programme in Biopharmaceuticals’ Post

🎉 We are excited to launch our AI-derived biomedical knowledge base, QIAGEN Biomedical KB-AI, designed to accelerate data-driven drug discovery in the pharma and biotech industries. Learn more 👉 https://lnkd.in/e9WaMDtV ✅ QIAGEN Biomedical KB-AI contains over 640 million biomedical relationships, including gene, disease and drug causal relationships, to aid in data-driven drug discovery. ✅ AI-driven insights complement human-curated QIAGEN Biomedical KB-HD and help identify novel relationships between diseases, biological pathways and molecular interactions that may be missed by traditional methods. ✅ Workflow cuts down on hypothesis testing time and speeds up the identification of promising therapeutic avenues with clear, structured data.

Reusable datasets and code, paired with open benchmarks, is the driver of compounding progress in AI

𝐓𝐡𝐞 𝐏𝐫𝐞𝐝𝐢𝐜𝐭𝐢𝐯𝐞 𝐌𝐨𝐝𝐞𝐥𝐢𝐧𝐠 𝐑𝐞𝐯𝐨𝐥𝐮𝐭𝐢𝐨𝐧!🧬⚙️ A recent lecture on Model systems by one of our professors got me thinking about how much research tools have advanced. In the rapidly evolving world of biotechnology, 𝗽𝗿𝗲𝗱𝗶𝗰𝘁𝗶𝘃𝗲 𝗺𝗼𝗱𝗲𝗹𝗶𝗻𝗴 is transforming the way we conduct research and development. But what exactly does this mean, and why should you care? Imagine this: Instead of lengthy, costly lab experiments, scientists can now use advanced algorithms to simulate biological processes and predict outcomes before even setting foot in the lab. Welcome to the world of 𝐢𝐧 𝐬𝐢𝐥𝐢𝐜𝐨 𝐚𝐧𝐚𝐥𝐲𝐬𝐢𝐬!📈📊 Predictive modeling leverages complex statistical techniques and ML to analyze vast datasets. By utilizing existing data on biological systems, researchers can create models that forecast how different variables will interact. 𝐃𝐫𝐮𝐠 𝐃𝐢𝐬𝐜𝐨𝐯𝐞𝐫𝐲: Predictive models can identify potential drug candidates by simulating their interactions with target proteins, significantly speeding up the process. For instance, a study published in Nature Biotechnology demonstrated that machine learning algorithms could predict the efficacy of drug compounds with remarkable accuracy (https://lnkd.in/dxEnnw9E) 𝐆𝐞𝐧𝐞𝐭𝐢𝐜 𝐑𝐞𝐬𝐞𝐚𝐫𝐜𝐡: In silico analysis allows scientists to predict the behavior of genes and their interactions within cellular environments. This has enormous implications for understanding diseases and developing gene therapies. A paper in Bioinformatics highlighted how predictive models successfully identified novel genetic variants associated with specific conditions. (Larrea-Sebal, Asier et al. “Predictive Modeling and Structure Analysis of Genetic Variants in Familial Hypercholesterolemia: Implications for Diagnosis and Protein Interaction Studies.” Current atherosclerosis reports vol. 25,11 (2023): 839-859. doi:10.1007/s11883-023-01154-7) 𝐄𝐧𝐯𝐢𝐫𝐨𝐧𝐦𝐞𝐧𝐭𝐚𝐥 𝐈𝐦𝐩𝐚𝐜𝐭 𝐀𝐬𝐬𝐞𝐬𝐬𝐦𝐞𝐧𝐭𝐬: Predictive modeling can assess the impact of biotechnological interventions on ecosystems, helping to ensure that innovations are sustainable and eco-friendly.🍃🌍 By reducing the time and cost associated with traditional methods, we can bring new therapies and solutions to market faster than ever. #Predictivemodelling #Bioinformatics #Modelsystems #Biotechnology #Drugdiscovery #AI #Machinelearning

Join DDW for an exclusive webinar event: "CRISPR screening at scale: Achieving 1M cell data sets with an instrument-free, cost-effective approach." 🎤 Presenters: Dylan Mooijman, Head of R&D, Single Cell Discoveries Darina Sikrova, Researcher, Molecular Biology, Single Cell Discoveries Introduction by Melanie Masuda, Director of Market Development, Scale Biosciences The event will take place on 25 April 5 PM BST / 6PM CEST / 9AM PST. 🧬 Pooled CRISPR screens are revolutionising drug discovery by allowing the simultaneous manipulation of multiple genes. But until now, scalability and cost have been significant barriers. 💡 ScaleBio's groundbreaking technology addresses these challenges, enabling high-throughput, instrument-free, cost-effective approaches to single-cell omics. Their CRISPR Guide Enrichment Kit facilitates sensitive capture of guide RNAs matched with gene expression. 🔍 In this webinar, Single Cell Discoveries will showcase how they harnessed ScaleBio’s technology to conduct single-cell RNA sequencing on a massive scale, analysing 1M cells from a genome-wide perturbation screen. 📝 What you will learn: The importance of large cell numbers in CRISPR screens Strategies for meeting these demands with ScaleBio’s CRISPR Guide Enrichment Kit Techniques for achieving high-throughput single-cell sequencing for demanding projects. Don't miss this invaluable opportunity to stay at the forefront of CRISPR technology and drug discovery! Register now. #CRISPR #DrugDiscovery #SingleCellSequencing #CRISPRScreening #Webinar #Genetics #Genomics #Singlecellrnasequencing Drug Discovery World

The global RNA Transcriptomics market was valued at USD 5.76 Billion in 2023 and is expected to grow at a CAGR of 11.8% from 2024 to 2031. The market is set for rapid expansion, driven by is a growing demand for personalized or precision medicines, increased investment in pharmaceutical and biotechnology research, increased use of RNA sequencing, high prevalence of gene mutations in cancer and genetic variations, and ongoing developments in medical infrastructure. 🚀 Based on Technology, the Microarray segment is expected to grow at a CAGR of 13.0%.  🌏 The market in Asia-Pacific region is expected to register a fast revenue growth rate with a CAGR of 13.9%. 🎯 Key players includes Thermo Fisher Scientific, Illumina, Roche, Danaher Corporation, Agilent Technologies, Affymetrix, Eurofins, CD Genomics, Bio-Rad Laboratories, Pacific Bio, Promega Corporation , and Others. Latest Development: ⭐ On 08 July 2024, Biostate AI, a scalable biodata foundry firm, has emerged from stealth with the release of two service products: Total RNA sequencing and Copilot for RNAseq data interpretation. Biostate AI intends to engage and collaborate with academic researchers, hospital biorepositories, and pharmaceutical and biotech companies, leveraging its breakthrough technologies for scalable multiomic data collecting, scientific discovery, and AI training. 📧 Feel free to reach out for a complimentary sample report. For more details, please DM or email us at navistrat@outlook.com. #RNAAnalysis #Sequencing #Reversetranscription #Geneexpression #precisionmedicine #drugdiscovery #qPCR #microarray #Proteinexpression #peptides #healthcare #NGS #proteinanalysis #transcriptionprofilling #introns #exons #cancer #genemutations #biomarkers #vaccine #

Big questions need big (amounts of) data! Single Cell Discoveries will be presenting the ability to do genome-wide CRISPR screens more cost- and time-effectively.

I just read this fascinating article by Liran Belenzon (he/him), the CEO of BenchSci, about how they are building the Disease Biology Generative AI Platform for Pharma R&D. ASCEND: the GenAI assistant for preclinical R&D is a science-first and tech-deep platform that uses multimodal ML models, LLMs, and knowledge graphs to build a comprehensive and evidence-based biological understanding of diseases, and to support scientists in various workflows such as target and drug due diligence, experimental design and validation, and translation workflow. #innovation #genai #ai #science

The greatest opportunity for AI and GenAI in drug discovery lies in unraveling the complexities of disease biology. At BenchSci, we are excited to accelerate our efforts as we work towards our mission to improve the speed and quality of R&D, helping deliver life-changing medicines to patients faster. Check out our latest blog post from BenchSci CEO & Co-Founder Liran Belenzon (he/him), where he explores: ✔ The pivotal role of GenAI in pharma R&D ✔ How BenchSci’s ASCEND is shaping the future of drug discovery—the disease biology GenAI platform for pharma R&D ✔ The promising future of GenAI in revolutionizing drug discovery https://lnkd.in/gNBihdDV #GenAI #artificialintelligencetechnology

In this publication by the #BoehringerIngelheim team (N Biotechnol (2023) Jul 25:75:31-39), Clappier et al. showcase a nice, CHO manufacturing cell line example, of: 1. An application of Long Read #NGS  sequencing to a complex challenge 2. The ongoing work of my colleagues #GenedataSelector to support the significant advances being made by our #biopharma partners across multiple use cases and modalities. #DigitalizingBiopharma  #DrugDevelopment #Bioinformatics

Caris Life Sciences: Forging Powerful Collaborations in the World of Genomic Data and AI #AbbVie #AI #artificialintelligence #bigdata #BigPharma #biomarkerspecificcancertherapeutics #Biotechnology #cancerdrugs #cancerresearch #CarisLifeSciences #clinicalnetworks #clinicogenomicdatabase #collaborations #ConcertAI #JPMorganHealthcareConference #llm #machinelearning #MorganHealthcareConference #oncologyresearchdata #oncologyresearchplatform #precisiontherapeutics #realworldoncologydata #RocheownedFlatironHealth #Technology