tv.qiagenbioinformatics.com marketingbiox@qiagen.com https://tv.qiagenbioinformatics.com Webinar series focusing on data-driven drug discovery in various therapeutic areas. en-us Visualplatform http://blogs.law.harvard.edu/tech/rss tv.qiagenbioinformatics.com CLC bio TV Watch tutorials, interviews and much more on our web based TV channel! clc bio tv, bioinformatics, genomics, research episodic no https://tv.qiagenbioinformatics.com/files/rv0.0/sitelogo.gif https://tv.qiagenbioinformatics.com http://tv.qiagenbioinformatics.com/photo/130064192/how-single-cell-atlases-and-ai-are <p><p>Large-scale single-cell datasets are creating new opportunities to understand biology, model drug response, and accelerate discovery. As these datasets grow, two needs are becoming increasingly important: generating clean, high-quality single-cell data at unprecedented scale to train AI models and applying analytical frameworks that can turn complex results into biological insight.</p><p></p><p>This webinar will feature two complementary presentations focused on different aspects of these challenges.</p><p></p><p>First,&nbsp;<strong>Johnny Yu</strong>, CSO and cofounder of Tahoe Therapeutics, will discuss the generation of Tahoe-100 Million, the largest publicly available single-cell dataset to date. Spanning 50 cancer cell lines, 379 drugs at three concentrations and more than 56,000 conditions, Tahoe-100M comprises over 100 million single-cell transcriptomes from co-cultured, drug-treated cells. It provides a training dataset capable of powering predictive models of drug action, shifting discovery from one-perturbation-at-a-time studies toward AI-driven prediction of efficacy, resistance and combination effects.</p><p></p><p>Following,&nbsp;<strong>Britt Flaherty</strong>, director at QIAGEN Digital Insights, will focus on the critical analytical challenge of extracting mechanistic insight from large and complex single-cell datasets. While single-cell workflows often rely on clustering, marker gene identification and descriptive annotation, these approaches can limit the ability to translate high-dimensional cellular states into biologically meaningful and experimentally actionable hypotheses.</p><p>The speakers will discuss a case study in neuroblastoma to demonstrate how a dataset of this scale can uncover cellular response mechanisms, prioritize therapeutic hypotheses, and generate actionable insights into disease biology.</p><p><strong>Attendees will learn:</strong></p><ul><li><p>- About the Tahoe-100 Million dataset and Tahoe’s plans to build an even larger dataset consisting of 300 million single cells</p></li><li><p>- How Parse Biosciences Evercode single-cell RNA sequencing technology enables the generation of high-resolution, single-cell data at scale</p></li><li><p>- How large-scale single-cell datasets can support AI models for predicting drug MoA, efficacy, resistance, and combination effects</p></li><li><p>- How to harmonize, curate, contextualize, and rapidly interrogate large-scale single-cell datasets to support cross-study comparisons, perturbation-driven analyses, and hypothesis generation</p></li><li><p>- How to use integrated pathway- and network-driven interpretation to move analyses beyond clustering to prioritize targets, uncover signaling programs, and accelerate translational insight</p></li></ul><p></p></p><p><a href="http://tv.qiagenbioinformatics.com/photo/130064192/how-single-cell-atlases-and-ai-are"><img src="http://tv.qiagenbioinformatics.com/64968561/130064192/750118d2a58a717361f9ffb8255ee77a/standard/download-10-thumbnail.jpg" width="600" height="338"/></a></p> http://tv.qiagenbioinformatics.com/photo/130064192 Tue, 28 Jul 2026 14:33:48 GMT How Single-Cell Atlases and AI Are Reshaping Drug Discovery Large-scale single-cell datasets are creating new opportunities to understand biology, model drug response, and accelerate discovery. As these datasets grow, two needs are becoming increasingly important: generating clean, high-quality single-cell data at unprecedented scale to train AI models and applying analytical frameworks that can turn complex results into biological insight.This webinar will feature two complementary presentations focused on different aspects of these challenges.First,Johnny Yu, CSO and cofounder of Tahoe Therapeutics, will discuss the generation of Tahoe-100 Million, the largest publicly available single-cell dataset to date. Spanning 50 cancer cell lines, 379 drugs at three concentrations and more than 56,000 conditions, Tahoe-100M comprises over 100 million single-cell transcriptomes from co-cultured, drug-treated cells. It provides a training dataset capable of powering predictive models of drug action, shifting discovery from one-perturbation-at-a-time studies toward AI-driven prediction of efficacy, resistance and combination effects.Following,Britt Flaherty, director at QIAGEN Digital Insights, will focus on the critical analytical challenge of extracting mechanistic insight from large and complex single-cell datasets. While single-cell workflows often rely on clustering, marker gene identification and descriptive annotation, these approaches can limit the ability to translate high-dimensional cellular states into biologically meaningful and experimentally actionable hypotheses.The speakers will discuss a case study in neuroblastoma to demonstrate how a dataset of this scale can uncover cellular response mechanisms, prioritize therapeutic hypotheses, and generate actionable insights into disease biology.Attendees will learn:- About the Tahoe-100 Million dataset and Tahoe’s plans to build an even larger dataset consisting of 300 million single cells- How Parse Biosciences Evercode single-cell RNA sequencing technology enables the generation of high-resolution, single-cell data at scale- How large-scale single-cell datasets can support AI models for predicting drug MoA, efficacy, resistance, and combination effects- How to harmonize, curate, contextualize, and rapidly interrogate large-scale single-cell datasets to support cross-study comparisons, perturbation-driven analyses, and hypothesis generation- How to use integrated pathway- and network-driven interpretation to move analyses beyond clustering to prioritize targets, uncover signaling programs, and accelerate translational insight Large-scale single-cell datasets are creating new opportunities to understand biology, model drug response, and accelerate discovery. As these datasets grow, two needs are becoming increasingly important: generating clean, high-quality single-cell... tv.qiagenbioinformatics.com 54:34 <p><p>Large-scale single-cell datasets are creating new opportunities to understand biology, model drug response, and accelerate discovery. As these datasets grow, two needs are becoming increasingly important: generating clean, high-quality single-cell data at unprecedented scale to train AI models and applying analytical frameworks that can turn complex results into biological insight.</p><p></p><p>This webinar will feature two complementary presentations focused on different aspects of these challenges.</p><p></p><p>First,&nbsp;<strong>Johnny Yu</strong>, CSO and cofounder of Tahoe Therapeutics, will discuss the generation of Tahoe-100 Million, the largest publicly available single-cell dataset to date. Spanning 50 cancer cell lines, 379 drugs at three concentrations and more than 56,000 conditions, Tahoe-100M comprises over 100 million single-cell transcriptomes from co-cultured, drug-treated cells. It provides a training dataset capable of powering predictive models of drug action, shifting discovery from one-perturbation-at-a-time studies toward AI-driven prediction of efficacy, resistance and combination effects.</p><p></p><p>Following,&nbsp;<strong>Britt Flaherty</strong>, director at QIAGEN Digital Insights, will focus on the critical analytical challenge of extracting mechanistic insight from large and complex single-cell datasets. While single-cell workflows often rely on clustering, marker gene identification and descriptive annotation, these approaches can limit the ability to translate high-dimensional cellular states into biologically meaningful and experimentally actionable hypotheses.</p><p>The speakers will discuss a case study in neuroblastoma to demonstrate how a dataset of this scale can uncover cellular response mechanisms, prioritize therapeutic hypotheses, and generate actionable insights into disease biology.</p><p><strong>Attendees will learn:</strong></p><ul><li><p>- About the Tahoe-100 Million dataset and Tahoe’s plans to build an even larger dataset consisting of 300 million single cells</p></li><li><p>- How Parse Biosciences Evercode single-cell RNA sequencing technology enables the generation of high-resolution, single-cell data at scale</p></li><li><p>- How large-scale single-cell datasets can support AI models for predicting drug MoA, efficacy, resistance, and combination effects</p></li><li><p>- How to harmonize, curate, contextualize, and rapidly interrogate large-scale single-cell datasets to support cross-study comparisons, perturbation-driven analyses, and hypothesis generation</p></li><li><p>- How to use integrated pathway- and network-driven interpretation to move analyses beyond clustering to prioritize targets, uncover signaling programs, and accelerate translational insight</p></li></ul><p></p></p><p><a href="http://tv.qiagenbioinformatics.com/photo/130064192/how-single-cell-atlases-and-ai-are"><img src="http://tv.qiagenbioinformatics.com/64968561/130064192/750118d2a58a717361f9ffb8255ee77a/standard/download-10-thumbnail.jpg" width="600" height="338"/></a></p> http://tv.qiagenbioinformatics.com/photo/129209938/ai-meets-expertise-a-hybrid <p><p>AI excels at identifying patterns and generating insights across large datasets, but its output quality depends heavily on its training data. Meanwhile, projects involve making critical decisions – decisions that require a nuanced understanding of biology, subject-matter expertise and experience that AI lacks. That’s where humans come in.<br><br></p><p>Our fireside chat will explore a hybrid workflow that combines the best of both worlds: AI efficiency and human intelligence. Led by experts from Sygnature Discovery, we will discuss how this modern approach benefits drug discovery, shortening the path from target lists to confident decisions.</p><p><br></p><p>You will learn:</p><p><strong><br></strong></p><p><strong>What “human-in-the-loop” and “lab-in-the-loop” means</strong></p><p>The importance of expert intervention and wet-lab validation</p><p><strong>The synergistic impact of well-trained AI and expert discretion</strong></p><p>How they reduce project risk by flagging safety/IP/druggability issues and more</p><p><strong>How cross-disciplinary input drives actionable results</strong></p><p>Uncover insights into drug metabolism and pharmacokinetics (DMPK) with causation-driven interpretation and feasibility assays</p><p><strong>Why explainability matters</strong></p><p>How transparency and human expertise lead to defensible decisions</p></p><p><a href="http://tv.qiagenbioinformatics.com/photo/129209938/ai-meets-expertise-a-hybrid"><img src="http://tv.qiagenbioinformatics.com/64968567/129209938/874a7aba08351bddcdb99593685f4496/standard/download-11-thumbnail.jpg" width="600" height="338"/></a></p> http://tv.qiagenbioinformatics.com/photo/129209938 Thu, 02 Jul 2026 22:05:03 GMT AI meets expertise: A hybrid workflow for modern target ID AI excels at identifying patterns and generating insights across large datasets, but its output quality depends heavily on its training data. Meanwhile, projects involve making critical decisions – decisions that require a nuanced understanding of biology, subject-matter expertise and experience that AI lacks. That’s where humans come in.Our fireside chat will explore a hybrid workflow that combines the best of both worlds: AI efficiency and human intelligence. Led by experts from Sygnature Discovery, we will discuss how this modern approach benefits drug discovery, shortening the path from target lists to confident decisions.You will learn:What “human-in-the-loop” and “lab-in-the-loop” meansThe importance of expert intervention and wet-lab validationThe synergistic impact of well-trained AI and expert discretionHow they reduce project risk by flagging safety/IP/druggability issues and moreHow cross-disciplinary input drives actionable resultsUncover insights into drug metabolism and pharmacokinetics (DMPK) with causation-driven interpretation and feasibility assaysWhy explainability mattersHow transparency and human expertise lead to defensible decisions AI excels at identifying patterns and generating insights across large datasets, but its output quality depends heavily on its training data. Meanwhile, projects involve making critical decisions – decisions that require a nuanced understanding of... tv.qiagenbioinformatics.com 46:08 <p><p>AI excels at identifying patterns and generating insights across large datasets, but its output quality depends heavily on its training data. Meanwhile, projects involve making critical decisions – decisions that require a nuanced understanding of biology, subject-matter expertise and experience that AI lacks. That’s where humans come in.<br><br></p><p>Our fireside chat will explore a hybrid workflow that combines the best of both worlds: AI efficiency and human intelligence. Led by experts from Sygnature Discovery, we will discuss how this modern approach benefits drug discovery, shortening the path from target lists to confident decisions.</p><p><br></p><p>You will learn:</p><p><strong><br></strong></p><p><strong>What “human-in-the-loop” and “lab-in-the-loop” means</strong></p><p>The importance of expert intervention and wet-lab validation</p><p><strong>The synergistic impact of well-trained AI and expert discretion</strong></p><p>How they reduce project risk by flagging safety/IP/druggability issues and more</p><p><strong>How cross-disciplinary input drives actionable results</strong></p><p>Uncover insights into drug metabolism and pharmacokinetics (DMPK) with causation-driven interpretation and feasibility assays</p><p><strong>Why explainability matters</strong></p><p>How transparency and human expertise lead to defensible decisions</p></p><p><a href="http://tv.qiagenbioinformatics.com/photo/129209938/ai-meets-expertise-a-hybrid"><img src="http://tv.qiagenbioinformatics.com/64968567/129209938/874a7aba08351bddcdb99593685f4496/standard/download-11-thumbnail.jpg" width="600" height="338"/></a></p> http://tv.qiagenbioinformatics.com/photo/127864700/beyond-enrichment-causal-discovery <p><p>One click from your target gene to disease drivers, drug candidates and causal evidence. The Neo4j &amp; QIAGEN solution transforms causal biomedical knowledge from QIAGEN Discovery KB+ into an interactive discovery surface. Learn how to query biomarkers, navigate causal networks and find drug repositioning candidates, &nbsp;provenance and natural language summaries included.</p></p><p><a href="http://tv.qiagenbioinformatics.com/photo/127864700/beyond-enrichment-causal-discovery"><img src="http://tv.qiagenbioinformatics.com/64968576/127864700/95ddeb9621a5616b11c867aa88268f60/standard/download-10-thumbnail.jpg" width="600" height="338"/></a></p> http://tv.qiagenbioinformatics.com/photo/127864700 Fri, 29 May 2026 17:19:15 GMT Beyond enrichment Causal discovery powered by Neo4j & QIAGEN Discovery KB+ One click from your target gene to disease drivers, drug candidates and causal evidence. The Neo4j QIAGEN solution transforms causal biomedical knowledge from QIAGEN Discovery KB+ into an interactive discovery surface. Learn how to query biomarkers, navigate causal networks and find drug repositioning candidates, provenance and natural language summaries included. One click from your target gene to disease drivers, drug candidates and causal evidence. The Neo4j QIAGEN solution transforms causal biomedical knowledge from QIAGEN Discovery KB+ into an interactive discovery surface. Learn how to query... tv.qiagenbioinformatics.com 36:45 <p><p>One click from your target gene to disease drivers, drug candidates and causal evidence. The Neo4j &amp; QIAGEN solution transforms causal biomedical knowledge from QIAGEN Discovery KB+ into an interactive discovery surface. Learn how to query biomarkers, navigate causal networks and find drug repositioning candidates, &nbsp;provenance and natural language summaries included.</p></p><p><a href="http://tv.qiagenbioinformatics.com/photo/127864700/beyond-enrichment-causal-discovery"><img src="http://tv.qiagenbioinformatics.com/64968576/127864700/95ddeb9621a5616b11c867aa88268f60/standard/download-10-thumbnail.jpg" width="600" height="338"/></a></p> http://tv.qiagenbioinformatics.com/photo/127772718/qiagen-and-nvidia-collaboration-to <p><p>NVIDIA and QIAGEN are collaborating to bring trustworthy, contextual AI to drug discovery. By integrating NVIDIA's accelerated computing and the BioNeMo platform with QIAGEN Digital Insights' manually curated biomedical knowledge graphs, cited in over 70,000 peer-reviewed publications; the collaboration aims to transform how disease mechanisms, therapeutic targets, and biomarkers are identified. Using a graph-based AI approach built on G-Retriever, the solution delivers 5–10x better performance than standard RAG pipelines, giving researchers answers they can trust, defend, and act on. In this conversation from Bio-IT World 2026, NVIDIA's Ben Busby and QIAGEN's Venkatesh Moktali discuss what each side brings, why knowledge graphs are a must-have for pharma AI, and what's coming next — including initial pilots with select pharma and biotech partners ahead of broader availability.</p></p><p><a href="http://tv.qiagenbioinformatics.com/photo/127772718/qiagen-and-nvidia-collaboration-to"><img src="http://tv.qiagenbioinformatics.com/64968571/127772718/3bece73ca9179c9a84cf1cdc1584d7bf/standard/download-29-thumbnail.jpg" width="600" height="338"/></a></p> http://tv.qiagenbioinformatics.com/photo/127772718 Fri, 29 May 2026 13:24:12 GMT QIAGEN and NVIDIA collaboration to advance AI driven drug discovery NVIDIA and QIAGEN are collaborating to bring trustworthy, contextual AI to drug discovery. By integrating NVIDIA's accelerated computing and the BioNeMo platform with QIAGEN Digital Insights' manually curated biomedical knowledge graphs, cited in over 70,000 peer-reviewed publications; the collaboration aims to transform how disease mechanisms, therapeutic targets, and biomarkers are identified. Using a graph-based AI approach built on G-Retriever, the solution delivers 5–10x better performance than standard RAG pipelines, giving researchers answers they can trust, defend, and act on. In this conversation from Bio-IT World 2026, NVIDIA's Ben Busby and QIAGEN's Venkatesh Moktali discuss what each side brings, why knowledge graphs are a must-have for pharma AI, and what's coming next — including initial pilots with select pharma and biotech partners ahead of broader availability. NVIDIA and QIAGEN are collaborating to bring trustworthy, contextual AI to drug discovery. By integrating NVIDIA's accelerated computing and the BioNeMo platform with QIAGEN Digital Insights' manually curated biomedical knowledge graphs, cited in... tv.qiagenbioinformatics.com 10:23 <p><p>NVIDIA and QIAGEN are collaborating to bring trustworthy, contextual AI to drug discovery. By integrating NVIDIA's accelerated computing and the BioNeMo platform with QIAGEN Digital Insights' manually curated biomedical knowledge graphs, cited in over 70,000 peer-reviewed publications; the collaboration aims to transform how disease mechanisms, therapeutic targets, and biomarkers are identified. Using a graph-based AI approach built on G-Retriever, the solution delivers 5–10x better performance than standard RAG pipelines, giving researchers answers they can trust, defend, and act on. In this conversation from Bio-IT World 2026, NVIDIA's Ben Busby and QIAGEN's Venkatesh Moktali discuss what each side brings, why knowledge graphs are a must-have for pharma AI, and what's coming next — including initial pilots with select pharma and biotech partners ahead of broader availability.</p></p><p><a href="http://tv.qiagenbioinformatics.com/photo/127772718/qiagen-and-nvidia-collaboration-to"><img src="http://tv.qiagenbioinformatics.com/64968571/127772718/3bece73ca9179c9a84cf1cdc1584d7bf/standard/download-29-thumbnail.jpg" width="600" height="338"/></a></p> highlighted http://tv.qiagenbioinformatics.com/photo/126600257/immunology-guide-the-immunology <p><p>The autoimmune drug market is dominated by blockbuster biologics, including Janssen’s Stelara® (ustekinumab), a dual IL-23 and IL-12 inhibitor approved for plaque psoriasis, psoriatic arthritis, Crohn’s disease and ulcerative colitis. These biologics generate billions in annual revenue, but when exclusivity ends, competition from biosimilars quickly becomes a concern.</p><p>Using the real-world example of Stelara, which faced multiple new biosimilars within a year of its patent expiry, we explore how to sustain value with smarter lifecycle strategy. We’ll combine data curation, large-scale ‘omics evidence and pathway analytics with QIAGEN Discovery Platform to uncover new opportunities for Stelara.</p><p>In this session, you’ll learn how to:</p><ul><li><p>Support discovery decisions in immunology research</p></li><li><p>Contextualize targets with curated biological knowledge and pathway analytics</p></li><li><p>Identify opportunities for indication expansion and combination strategies</p></li><li><p>Generate AI-ready insights from integrated multimodal data and knowledge graphs</p></li></ul><p>Speakers:</p><p><strong>Tim Hou, PhD</strong></p><p>Senior Field Application Scientist, QIAGEN Digital Insights</p><p>Tim Hou, PhD, is a Senior Field Application Scientist at QIAGEN Digital Insights, where he leverages extensive expertise in molecular biology, genomics and bioinformatics to support researchers with biological data analysis and interpretation platforms from QIAGEN.</p><p><strong>Ethan Strattan, PhD</strong></p><p>Immunology Consulting Scientist, QIAGEN Digital Insights</p><p>Ethan Strattan, PhD, helps clients understand the biological underpinnings of ‘omics-level findings. He works as an immunology and oncology subject matter expert in the QDI Services team, bridging the gap between data scientists and bench researchers. He works with diverse datasets to generate use cases and proof-of-concepts for findings generated from cutting-edge informatics and AI pipelines.&nbsp;</p></p><p><a href="http://tv.qiagenbioinformatics.com/photo/126600257/immunology-guide-the-immunology"><img src="http://tv.qiagenbioinformatics.com/64968578/126600257/5226b3cc3b5cf67d8062c3208b78da12/standard/download-14-thumbnail.jpg" width="600" height="338"/></a></p> http://tv.qiagenbioinformatics.com/photo/126600257 Tue, 05 May 2026 17:32:26 GMT Immunology: Guide the immunology drug discovery lifecycle with ‘omics... The autoimmune drug market is dominated by blockbuster biologics, including Janssen’s Stelara® (ustekinumab), a dual IL-23 and IL-12 inhibitor approved for plaque psoriasis, psoriatic arthritis, Crohn’s disease and ulcerative colitis. These biologics generate billions in annual revenue, but when exclusivity ends, competition from biosimilars quickly becomes a concern.Using the real-world example of Stelara, which faced multiple new biosimilars within a year of its patent expiry, we explore how to sustain value with smarter lifecycle strategy. We’ll combine data curation, large-scale ‘omics evidence and pathway analytics with QIAGEN Discovery Platform to uncover new opportunities for Stelara.In this session, you’ll learn how to:Support discovery decisions in immunology researchContextualize targets with curated biological knowledge and pathway analyticsIdentify opportunities for indication expansion and combination strategiesGenerate AI-ready insights from integrated multimodal data and knowledge graphsSpeakers:Tim Hou, PhDSenior Field Application Scientist, QIAGEN Digital InsightsTim Hou, PhD, is a Senior Field Application Scientist at QIAGEN Digital Insights, where he leverages extensive expertise in molecular biology, genomics and bioinformatics to support researchers with biological data analysis and interpretation platforms from QIAGEN.Ethan Strattan, PhDImmunology Consulting Scientist, QIAGEN Digital InsightsEthan Strattan, PhD, helps clients understand the biological underpinnings of ‘omics-level findings. He works as an immunology and oncology subject matter expert in the QDI Services team, bridging the gap between data scientists and bench researchers. He works with diverse datasets to generate use cases and proof-of-concepts for findings generated from cutting-edge informatics and AI pipelines. The autoimmune drug market is dominated by blockbuster biologics, including Janssen’s Stelara® (ustekinumab), a dual IL-23 and IL-12 inhibitor approved for plaque psoriasis, psoriatic arthritis, Crohn’s disease and ulcerative colitis. These... tv.qiagenbioinformatics.com 57:15 <p><p>The autoimmune drug market is dominated by blockbuster biologics, including Janssen’s Stelara® (ustekinumab), a dual IL-23 and IL-12 inhibitor approved for plaque psoriasis, psoriatic arthritis, Crohn’s disease and ulcerative colitis. These biologics generate billions in annual revenue, but when exclusivity ends, competition from biosimilars quickly becomes a concern.</p><p>Using the real-world example of Stelara, which faced multiple new biosimilars within a year of its patent expiry, we explore how to sustain value with smarter lifecycle strategy. We’ll combine data curation, large-scale ‘omics evidence and pathway analytics with QIAGEN Discovery Platform to uncover new opportunities for Stelara.</p><p>In this session, you’ll learn how to:</p><ul><li><p>Support discovery decisions in immunology research</p></li><li><p>Contextualize targets with curated biological knowledge and pathway analytics</p></li><li><p>Identify opportunities for indication expansion and combination strategies</p></li><li><p>Generate AI-ready insights from integrated multimodal data and knowledge graphs</p></li></ul><p>Speakers:</p><p><strong>Tim Hou, PhD</strong></p><p>Senior Field Application Scientist, QIAGEN Digital Insights</p><p>Tim Hou, PhD, is a Senior Field Application Scientist at QIAGEN Digital Insights, where he leverages extensive expertise in molecular biology, genomics and bioinformatics to support researchers with biological data analysis and interpretation platforms from QIAGEN.</p><p><strong>Ethan Strattan, PhD</strong></p><p>Immunology Consulting Scientist, QIAGEN Digital Insights</p><p>Ethan Strattan, PhD, helps clients understand the biological underpinnings of ‘omics-level findings. He works as an immunology and oncology subject matter expert in the QDI Services team, bridging the gap between data scientists and bench researchers. He works with diverse datasets to generate use cases and proof-of-concepts for findings generated from cutting-edge informatics and AI pipelines.&nbsp;</p></p><p><a href="http://tv.qiagenbioinformatics.com/photo/126600257/immunology-guide-the-immunology"><img src="http://tv.qiagenbioinformatics.com/64968578/126600257/5226b3cc3b5cf67d8062c3208b78da12/standard/download-14-thumbnail.jpg" width="600" height="338"/></a></p> drug discovery http://tv.qiagenbioinformatics.com/photo/126774951/oncology-de-risking-target <p><p>The early stages of drug development are inherently high-risk; molecule screening, target evaluation and lead refinement can take years and cost millions of dollars – often have little to show for the effort. Indication expansion and drug repurposing can open new avenues of possibility, after careful evaluation of the relationships between the drug, its targets and the new disease context. These connections can be uncovered by mining curated, causal knowledge graphs, which inform smarter target identification.</p><p>Learn how to evaluate targets and drugs in this oncology-focused webinar, which examines BRAF as a target in multiple myeloma. Also, we’ll evaluate different drugs in clinical trials with an analysis that covers the GOT-IT assessment blocks for drug evaluation. These blocks, developed by the GOT-IT (Guidelines On Target Assessment for Innovative Therapeutics) working group, are part of a target assessment framework that supports robust, reproducible data.</p><p><strong>We’ll cover how to:</strong></p><ul><li><p>Apply the GOT-IT framework to target evaluation and indication expansion</p></li><li><p>Uncover causal relationships between existing drugs and new diseases with our curated knowledge graphs</p></li><li><p>Make informed decisions based on concrete data, including toxicity, adverse events and competing drugs in clinical trials</p></li></ul><p>Speakers:</p><p><strong>Sathiya Manivannan, PhD</strong></p><p>Scientific Consulting Expert (Multiomics), QIAGEN Digital Insights</p><p>Sathiya Manivannan, PhD,&nbsp;delivers tailored solutions for pharmaceutical and biotech companies. His work includes large-scale single-cell and spatial transcriptomics analyses, multi-omics database generation, and the development of graph knowledge bases and AI/ML-driven solutions that accelerate therapeutic target discovery and optimize drug development.</p><p><strong>Ruth Stoney, PhD</strong></p><p>Senior Field Application Scientist, QIAGEN Digital Insights</p><p>Ruth Stoney, PhD, designs data science projects for customers using ‘omics datasets and insights mined from the QDI knowledge graph. She collaborates with academics and biopharma data scientists on biological discovery, AI usage and more.</p></p><p><a href="http://tv.qiagenbioinformatics.com/photo/126774951/oncology-de-risking-target"><img src="http://tv.qiagenbioinformatics.com/64968567/126774951/9f10fcd6e1dbff137d7380963d499670/standard/download-17-thumbnail.jpg" width="600" height="338"/></a></p> http://tv.qiagenbioinformatics.com/photo/126774951 Tue, 05 May 2026 17:32:22 GMT Oncology: De-risking target evaluation and indication expansion with curated... The early stages of drug development are inherently high-risk; molecule screening, target evaluation and lead refinement can take years and cost millions of dollars – often have little to show for the effort. Indication expansion and drug repurposing can open new avenues of possibility, after careful evaluation of the relationships between the drug, its targets and the new disease context. These connections can be uncovered by mining curated, causal knowledge graphs, which inform smarter target identification.Learn how to evaluate targets and drugs in this oncology-focused webinar, which examines BRAF as a target in multiple myeloma. Also, we’ll evaluate different drugs in clinical trials with an analysis that covers the GOT-IT assessment blocks for drug evaluation. These blocks, developed by the GOT-IT (Guidelines On Target Assessment for Innovative Therapeutics) working group, are part of a target assessment framework that supports robust, reproducible data.We’ll cover how to:Apply the GOT-IT framework to target evaluation and indication expansionUncover causal relationships between existing drugs and new diseases with our curated knowledge graphsMake informed decisions based on concrete data, including toxicity, adverse events and competing drugs in clinical trialsSpeakers:Sathiya Manivannan, PhDScientific Consulting Expert (Multiomics), QIAGEN Digital InsightsSathiya Manivannan, PhD,delivers tailored solutions for pharmaceutical and biotech companies. His work includes large-scale single-cell and spatial transcriptomics analyses, multi-omics database generation, and the development of graph knowledge bases and AI/ML-driven solutions that accelerate therapeutic target discovery and optimize drug development.Ruth Stoney, PhDSenior Field Application Scientist, QIAGEN Digital InsightsRuth Stoney, PhD, designs data science projects for customers using ‘omics datasets and insights mined from the QDI knowledge graph. She collaborates with academics and biopharma data scientists on biological discovery, AI usage and more. The early stages of drug development are inherently high-risk; molecule screening, target evaluation and lead refinement can take years and cost millions of dollars – often have little to show for the effort. Indication expansion and drug... tv.qiagenbioinformatics.com 58:10 <p><p>The early stages of drug development are inherently high-risk; molecule screening, target evaluation and lead refinement can take years and cost millions of dollars – often have little to show for the effort. Indication expansion and drug repurposing can open new avenues of possibility, after careful evaluation of the relationships between the drug, its targets and the new disease context. These connections can be uncovered by mining curated, causal knowledge graphs, which inform smarter target identification.</p><p>Learn how to evaluate targets and drugs in this oncology-focused webinar, which examines BRAF as a target in multiple myeloma. Also, we’ll evaluate different drugs in clinical trials with an analysis that covers the GOT-IT assessment blocks for drug evaluation. These blocks, developed by the GOT-IT (Guidelines On Target Assessment for Innovative Therapeutics) working group, are part of a target assessment framework that supports robust, reproducible data.</p><p><strong>We’ll cover how to:</strong></p><ul><li><p>Apply the GOT-IT framework to target evaluation and indication expansion</p></li><li><p>Uncover causal relationships between existing drugs and new diseases with our curated knowledge graphs</p></li><li><p>Make informed decisions based on concrete data, including toxicity, adverse events and competing drugs in clinical trials</p></li></ul><p>Speakers:</p><p><strong>Sathiya Manivannan, PhD</strong></p><p>Scientific Consulting Expert (Multiomics), QIAGEN Digital Insights</p><p>Sathiya Manivannan, PhD,&nbsp;delivers tailored solutions for pharmaceutical and biotech companies. His work includes large-scale single-cell and spatial transcriptomics analyses, multi-omics database generation, and the development of graph knowledge bases and AI/ML-driven solutions that accelerate therapeutic target discovery and optimize drug development.</p><p><strong>Ruth Stoney, PhD</strong></p><p>Senior Field Application Scientist, QIAGEN Digital Insights</p><p>Ruth Stoney, PhD, designs data science projects for customers using ‘omics datasets and insights mined from the QDI knowledge graph. She collaborates with academics and biopharma data scientists on biological discovery, AI usage and more.</p></p><p><a href="http://tv.qiagenbioinformatics.com/photo/126774951/oncology-de-risking-target"><img src="http://tv.qiagenbioinformatics.com/64968567/126774951/9f10fcd6e1dbff137d7380963d499670/standard/download-17-thumbnail.jpg" width="600" height="338"/></a></p> drug discovery