tv.qiagenbioinformatics.com marketingbiox@qiagen.com https://tv.qiagenbioinformatics.com Watch tutorials, interviews and much more on our web based TV channel! 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/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 http://tv.qiagenbioinformatics.com/photo/91490017/mining-curated-knowledge-graphs-and <p><div><br></div><div>In an era of near-limitless public experimental data but little standardization, meaningful insights are lost to noise. Large collections of quality experimental data are essential for big-picture discoveries that stand up to scrutiny.<br><br>In this webinar, you will learn how to feed your drug discovery programs by integrating connections mined from QIAGEN Biomedical Knowledge Base with deeply-curated disease datasets from QIAGEN OmicSoft Lands.<br><br>Combining unified 'omics datasets with contextual relationship evidence from our knowledge graph, we will address complex questions such as:<br>• Which genes aren't expressed in normal tissue, yet are expressed in diseases of interest, based on experimental evidence?<br>• Which of these proteins are cell surface proteins, with evidence for extracellular localization?<br>• How are these proteins related directly or indirectly to disease pathways, and can these be connected to known drug targets?<br>• Can we identify correlated biomarkers, mutation targets, clinical factors or other means of cohort selection?<br></div></p><p><a href="http://tv.qiagenbioinformatics.com/photo/91490017/mining-curated-knowledge-graphs-and"><img src="http://tv.qiagenbioinformatics.com/64968571/91490017/24778ce45bbf9f4489e827e8cbcb68b2/standard/download-8-thumbnail.jpg" width="75" height=""/></a></p> http://tv.qiagenbioinformatics.com/photo/91490017 Mon, 11 Dec 2023 13:14:15 GMT Mining curated knowledge graphs and validating with experimental datasets to... In an era of near-limitless public experimental data but little standardization, meaningful insights are lost to noise. Large collections of quality experimental data are essential for big-picture discoveries that stand up to scrutiny.In this webinar, you will learn how to feed your drug discovery programs by integrating connections mined from QIAGEN Biomedical Knowledge Base with deeply-curated disease datasets from QIAGEN OmicSoft Lands.Combining unified 'omics datasets with contextual relationship evidence from our knowledge graph, we will address complex questions such as:• Which genes aren't expressed in normal tissue, yet are expressed in diseases of interest, based on experimental evidence?• Which of these proteins are cell surface proteins, with evidence for extracellular localization?• How are these proteins related directly or indirectly to disease pathways, and can these be connected to known drug targets?• Can we identify correlated biomarkers, mutation targets, clinical factors or other means of cohort selection? In an era of near-limitless public experimental data but little standardization, meaningful insights are lost to noise. Large collections of quality experimental data are essential for big-picture discoveries that stand up to scrutiny.In this... tv.qiagenbioinformatics.com 59:42 <p><div><br></div><div>In an era of near-limitless public experimental data but little standardization, meaningful insights are lost to noise. Large collections of quality experimental data are essential for big-picture discoveries that stand up to scrutiny.<br><br>In this webinar, you will learn how to feed your drug discovery programs by integrating connections mined from QIAGEN Biomedical Knowledge Base with deeply-curated disease datasets from QIAGEN OmicSoft Lands.<br><br>Combining unified 'omics datasets with contextual relationship evidence from our knowledge graph, we will address complex questions such as:<br>• Which genes aren't expressed in normal tissue, yet are expressed in diseases of interest, based on experimental evidence?<br>• Which of these proteins are cell surface proteins, with evidence for extracellular localization?<br>• How are these proteins related directly or indirectly to disease pathways, and can these be connected to known drug targets?<br>• Can we identify correlated biomarkers, mutation targets, clinical factors or other means of cohort selection?<br></div></p><p><a href="http://tv.qiagenbioinformatics.com/photo/91490017/mining-curated-knowledge-graphs-and"><img src="http://tv.qiagenbioinformatics.com/64968571/91490017/24778ce45bbf9f4489e827e8cbcb68b2/standard/download-8-thumbnail.jpg" width="75" height=""/></a></p> biomarker drug discovery omicsoft webinar http://tv.qiagenbioinformatics.com/photo/89505871/qiagen-biomedical-knowledge-base-1 <p>Biomedical relationships knowledge is now required for innovative data- and analytics-driven drug discovery. It powers biomedical knowledge graph analysis, artificial intelligence (AI)-driven target identification and many more applications.<br> In this one-hour training, you’ll get an introduction to QIAGEN Biomedical Knowledge Base. You’ll learn how to tackle applications you can’t achieve with the QIAGEN Ingenuity Pathway Analysis (IPA) graphical user interface, or which can be done quicker and with more flexibility when performed programmatically. You’ll learn how to perform queries such as:<br> • Quickly find the shortest connections between genes/proteins/metabolites of interest in the context of a specific disease<p></p> <p>• Systematically build a network using a short list of genes/proteins/metabolites/chemicals<br> • Recreate a drug mechanism of action</p></p><p><a href="http://tv.qiagenbioinformatics.com/photo/89505871/qiagen-biomedical-knowledge-base-1"><img src="http://tv.qiagenbioinformatics.com/64968566/89505871/bdc7bd64d2b97bbb51d738ad4b946279/standard/download-8-thumbnail.jpg" width="75" height=""/></a></p> http://tv.qiagenbioinformatics.com/photo/89505871 Wed, 11 Oct 2023 14:27:32 GMT QIAGEN Biomedical Knowledge Base: Data- and analytics-driven drug discovery Biomedical relationships knowledge is now required for innovative data- and analytics-driven drug discovery. It powers biomedical knowledge graph analysis, artificial intelligence (AI)-driven target identification and many more applications. In this one-hour training, you’ll get an introduction to QIAGEN Biomedical Knowledge Base. You’ll learn how to tackle applications you can’t achieve with the QIAGEN Ingenuity Pathway Analysis (IPA) graphical user interface, or which can be done quicker and with more flexibility when performed programmatically. You’ll learn how to perform queries such as: • Quickly find the shortest connections between genes/proteins/metabolites of interest in the context of a specific disease • Systematically build a network using a short list of genes/proteins/metabolites/chemicals • Recreate a drug mechanism of action Biomedical relationships knowledge is now required for innovative data- and analytics-driven drug discovery. It powers biomedical knowledge graph analysis, artificial intelligence (AI)-driven target identification and many more applications. In... tv.qiagenbioinformatics.com 01:09:55 <p>Biomedical relationships knowledge is now required for innovative data- and analytics-driven drug discovery. It powers biomedical knowledge graph analysis, artificial intelligence (AI)-driven target identification and many more applications.<br> In this one-hour training, you’ll get an introduction to QIAGEN Biomedical Knowledge Base. You’ll learn how to tackle applications you can’t achieve with the QIAGEN Ingenuity Pathway Analysis (IPA) graphical user interface, or which can be done quicker and with more flexibility when performed programmatically. You’ll learn how to perform queries such as:<br> • Quickly find the shortest connections between genes/proteins/metabolites of interest in the context of a specific disease<p></p> <p>• Systematically build a network using a short list of genes/proteins/metabolites/chemicals<br> • Recreate a drug mechanism of action</p></p><p><a href="http://tv.qiagenbioinformatics.com/photo/89505871/qiagen-biomedical-knowledge-base-1"><img src="http://tv.qiagenbioinformatics.com/64968566/89505871/bdc7bd64d2b97bbb51d738ad4b946279/standard/download-8-thumbnail.jpg" width="75" height=""/></a></p> drug discovery omicsoft webinar http://tv.qiagenbioinformatics.com/photo/89505138/supercharge-your-ai-in-drug <p>If you’re working in pharma or biotech, you likely rely on artificial intelligence (AI) to help you identify new drug targets or plausible biomarkers for disease within large data sets. Yet AI alone isn’t enough. A large proportion of Biomedical data have errors and are unstructured. For AI models to provide reliable insights, the underlying data must be of ‘high quality’, meaning it’s accurate, comprehensive, up-to-date and standardized. <p>Jesper Ryge (Idorsia Pharmaceuticals), Alex Jarasch (Neo4j) and Venkatesh Moktali (QIAGEN Digital Insights) come together to showcase the practical applications of high-quality biomedical relationships data from the QIAGEN Biomedical Knowledge Base (BKB) to accelerate, improve and transform research in drug discovery and pharmaceutical development. By applying AI to a gene-disease knowledge graph, they identify promising drug targets and key mechanisms underlying diseases. A brief introduction to Neo4j shows how graph-centric analysis and visualizations facilitate the effective exploration of large knowledge graphs like BKB. This integration of high-quality curated data, AI-driven analysis and advanced visualization provides valuable insights and accelerates the progress of precision medicine.</p> <p>In this webinar, you’ll learn how you can:</p> <p>Build disease interactomes using protein-protein interactions<br> Identify high-quality drug targets using inferred causal interactions<br> Choose targets with the least likelihood of adverse outcomes by leveraging the depth of the data in BKB<br> Formulate plausible hypotheses using state-of-the-art graph visualization<br> Don’t miss this chance to learn how to supercharge your AI toolbox to transform your drug discovery.</p></p><p><a href="http://tv.qiagenbioinformatics.com/photo/89505138/supercharge-your-ai-in-drug"><img src="http://tv.qiagenbioinformatics.com/64968576/89505138/b3839e61b96a50536291f44cebf81745/standard/download-8-thumbnail.jpg" width="75" height=""/></a></p> http://tv.qiagenbioinformatics.com/photo/89505138 Tue, 03 Oct 2023 18:00:00 GMT Supercharge your AI in drug discovery with high-quality biomedical data If you’re working in pharma or biotech, you likely rely on artificial intelligence (AI) to help you identify new drug targets or plausible biomarkers for disease within large data sets. Yet AI alone isn’t enough. A large proportion of Biomedical data have errors and are unstructured. For AI models to provide reliable insights, the underlying data must be of ‘high quality’, meaning it’s accurate, comprehensive, up-to-date and standardized. Jesper Ryge (Idorsia Pharmaceuticals), Alex Jarasch (Neo4j) and Venkatesh Moktali (QIAGEN Digital Insights) come together to showcase the practical applications of high-quality biomedical relationships data from the QIAGEN Biomedical Knowledge Base (BKB) to accelerate, improve and transform research in drug discovery and pharmaceutical development. By applying AI to a gene-disease knowledge graph, they identify promising drug targets and key mechanisms underlying diseases. A brief introduction to Neo4j shows how graph-centric analysis and visualizations facilitate the effective exploration of large knowledge graphs like BKB. This integration of high-quality curated data, AI-driven analysis and advanced visualization provides valuable insights and accelerates the progress of precision medicine. In this webinar, you’ll learn how you can: Build disease interactomes using protein-protein interactions Identify high-quality drug targets using inferred causal interactions Choose targets with the least likelihood of adverse outcomes by leveraging the depth of the data in BKB Formulate plausible hypotheses using state-of-the-art graph visualization Don’t miss this chance to learn how to supercharge your AI toolbox to transform your drug discovery. If you’re working in pharma or biotech, you likely rely on artificial intelligence (AI) to help you identify new drug targets or plausible biomarkers for disease within large data sets. Yet AI alone isn’t enough. A large proportion of Biomedical... tv.qiagenbioinformatics.com 55:27 <p>If you’re working in pharma or biotech, you likely rely on artificial intelligence (AI) to help you identify new drug targets or plausible biomarkers for disease within large data sets. Yet AI alone isn’t enough. A large proportion of Biomedical data have errors and are unstructured. For AI models to provide reliable insights, the underlying data must be of ‘high quality’, meaning it’s accurate, comprehensive, up-to-date and standardized. <p>Jesper Ryge (Idorsia Pharmaceuticals), Alex Jarasch (Neo4j) and Venkatesh Moktali (QIAGEN Digital Insights) come together to showcase the practical applications of high-quality biomedical relationships data from the QIAGEN Biomedical Knowledge Base (BKB) to accelerate, improve and transform research in drug discovery and pharmaceutical development. By applying AI to a gene-disease knowledge graph, they identify promising drug targets and key mechanisms underlying diseases. A brief introduction to Neo4j shows how graph-centric analysis and visualizations facilitate the effective exploration of large knowledge graphs like BKB. This integration of high-quality curated data, AI-driven analysis and advanced visualization provides valuable insights and accelerates the progress of precision medicine.</p> <p>In this webinar, you’ll learn how you can:</p> <p>Build disease interactomes using protein-protein interactions<br> Identify high-quality drug targets using inferred causal interactions<br> Choose targets with the least likelihood of adverse outcomes by leveraging the depth of the data in BKB<br> Formulate plausible hypotheses using state-of-the-art graph visualization<br> Don’t miss this chance to learn how to supercharge your AI toolbox to transform your drug discovery.</p></p><p><a href="http://tv.qiagenbioinformatics.com/photo/89505138/supercharge-your-ai-in-drug"><img src="http://tv.qiagenbioinformatics.com/64968576/89505138/b3839e61b96a50536291f44cebf81745/standard/download-8-thumbnail.jpg" width="75" height=""/></a></p> drug discovery neo4j omicsoft webinar http://tv.qiagenbioinformatics.com/photo/84564113/biomarker-discovery-and-disease <p>In this training, attendees will learn how to harness curated ‘omics datasets in OmicSoft DiseaseLand and curated research findings in IPA to discover new potential biomarkers. Using a neurological disorder as a case study, we will:<p></p> <p>• Search public RNA-Seq datasets for tissue- and disease-specific differential expression in brain<br> • Identify genes whose expression correlates with our factor within a sample group<br> • Prioritize candidate biomarkers by disease vs. normal expression<br> • Simulate biomarker activity changes to determine potential mechanisms of action<br> Investigation of inflammatory, infectious, oncological, and other disorders can also be done using similar approach and will be highlighted during this training.</p></p><p><a href="http://tv.qiagenbioinformatics.com/photo/84564113/biomarker-discovery-and-disease"><img src="http://tv.qiagenbioinformatics.com/64968577/84564113/bb7e5c7a98da4a66cb165c56548e4c15/standard/download-8-thumbnail.jpg" width="75" height=""/></a></p> http://tv.qiagenbioinformatics.com/photo/84564113 Thu, 16 Mar 2023 16:01:02 GMT Biomarker discovery and disease pathology investigation using OmicSoft and... In this training, attendees will learn how to harness curated ‘omics datasets in OmicSoft DiseaseLand and curated research findings in IPA to discover new potential biomarkers. Using a neurological disorder as a case study, we will: • Search public RNA-Seq datasets for tissue- and disease-specific differential expression in brain • Identify genes whose expression correlates with our factor within a sample group • Prioritize candidate biomarkers by disease vs. normal expression • Simulate biomarker activity changes to determine potential mechanisms of action Investigation of inflammatory, infectious, oncological, and other disorders can also be done using similar approach and will be highlighted during this training. In this training, attendees will learn how to harness curated ‘omics datasets in OmicSoft DiseaseLand and curated research findings in IPA to discover new potential biomarkers. Using a neurological disorder as a case study, we will: • Search... tv.qiagenbioinformatics.com 01:28:10 <p>In this training, attendees will learn how to harness curated ‘omics datasets in OmicSoft DiseaseLand and curated research findings in IPA to discover new potential biomarkers. Using a neurological disorder as a case study, we will:<p></p> <p>• Search public RNA-Seq datasets for tissue- and disease-specific differential expression in brain<br> • Identify genes whose expression correlates with our factor within a sample group<br> • Prioritize candidate biomarkers by disease vs. normal expression<br> • Simulate biomarker activity changes to determine potential mechanisms of action<br> Investigation of inflammatory, infectious, oncological, and other disorders can also be done using similar approach and will be highlighted during this training.</p></p><p><a href="http://tv.qiagenbioinformatics.com/photo/84564113/biomarker-discovery-and-disease"><img src="http://tv.qiagenbioinformatics.com/64968577/84564113/bb7e5c7a98da4a66cb165c56548e4c15/standard/download-8-thumbnail.jpg" width="75" height=""/></a></p> biomarker drug discovery omicsoft webinar http://tv.qiagenbioinformatics.com/photo/84563028/drug-treatment-data-investigation <p>In this training, the trainer will go over how to utilize public drug treatment data from GEO, LINCS, and other sources to do discoveries regarding drug treatment investigation, biomarker discovery and validation. We will also use IPA to gain insight on the biological mechanisms of these treatments. <p>Participants will learn how to:<br> • Easily search for public studies relevant to drug treatment of their interest<br> • Identify a list of key genes/biomarkers relevant to a treatment or pathological condition<br> • Send differential expression data to Ingenuity Pathway Analysis to investigate biological mechanism (underlying disease pathology, drug response and more)<br> • Generate expression, correlation, heatmap and other plots to investigate key genes and biomarkers<br> • Explore other treatments that may share similar gene expression patterns</p></p><p><a href="http://tv.qiagenbioinformatics.com/photo/84563028/drug-treatment-data-investigation"><img src="http://tv.qiagenbioinformatics.com/64968569/84563028/ee1fcc8f016958df32738ac8e368914a/standard/download-8-thumbnail.jpg" width="75" height=""/></a></p> http://tv.qiagenbioinformatics.com/photo/84563028 Thu, 02 Mar 2023 15:19:00 GMT Drug treatment data investigation using Omicsoft and Ingenuity Pathway Analysis In this training, the trainer will go over how to utilize public drug treatment data from GEO, LINCS, and other sources to do discoveries regarding drug treatment investigation, biomarker discovery and validation. We will also use IPA to gain insight on the biological mechanisms of these treatments. Participants will learn how to: • Easily search for public studies relevant to drug treatment of their interest • Identify a list of key genes/biomarkers relevant to a treatment or pathological condition • Send differential expression data to Ingenuity Pathway Analysis to investigate biological mechanism (underlying disease pathology, drug response and more) • Generate expression, correlation, heatmap and other plots to investigate key genes and biomarkers • Explore other treatments that may share similar gene expression patterns In this training, the trainer will go over how to utilize public drug treatment data from GEO, LINCS, and other sources to do discoveries regarding drug treatment investigation, biomarker discovery and validation. We will also use IPA to gain... tv.qiagenbioinformatics.com 01:43:25 <p>In this training, the trainer will go over how to utilize public drug treatment data from GEO, LINCS, and other sources to do discoveries regarding drug treatment investigation, biomarker discovery and validation. We will also use IPA to gain insight on the biological mechanisms of these treatments. <p>Participants will learn how to:<br> • Easily search for public studies relevant to drug treatment of their interest<br> • Identify a list of key genes/biomarkers relevant to a treatment or pathological condition<br> • Send differential expression data to Ingenuity Pathway Analysis to investigate biological mechanism (underlying disease pathology, drug response and more)<br> • Generate expression, correlation, heatmap and other plots to investigate key genes and biomarkers<br> • Explore other treatments that may share similar gene expression patterns</p></p><p><a href="http://tv.qiagenbioinformatics.com/photo/84563028/drug-treatment-data-investigation"><img src="http://tv.qiagenbioinformatics.com/64968569/84563028/ee1fcc8f016958df32738ac8e368914a/standard/download-8-thumbnail.jpg" width="75" height=""/></a></p> biomarker drug discovery ipa webinar omicsoft webinar