Discover our white papers and Multiomics webinar series and read our scientific publications. Learn and grow with us.
Data plays a pivotal role in disease research, its complexity and quality increasingly enhanced by automation, cost reduction, and emerging technologies. However, simply expanding data volume is insufficient to address today’s unmet medical needs. The key lies in posing the right questions.
This white paper explores data processing challenges within the biotech and pharmaceutical sectors, comparing current solutions with knowing01’s innovative approach designed to retain critical context information, thereby ensuring no valuable insight is overlooked.
Biotechs differ in their approach, solution and underlying data. However, incomplete data increases the risk of clinical-stage failures. Leveraging Big Data from multiple sources can improve success rates and address unmet medical needs.
This white paper delves into putting data into context to find hidden patterns and introduces knowing01’s flexible approach as a solution that connects disparate Multiomics data without requiring data transformations.
Munich-based Biotech SaaS startup knowing01 GmbH has successfully closed an oversubscribed six-figure pre-seed funding round. Led by Backbone Ventures and supported by notable DACH business angels, including Dr. Julia Ju, Veronika Chrestin, Björn W. Schäfer and Johannes Stiber, this funding will enable knowing01 to accelerate the development of its AI-powered software for contextualizing molecular data, reducing risks in early-phase drug discovery.
Welcome to our Webinar section, the perfect place to enrich your after-work or lunchtime with insights into the exciting world of multiomics integration. So take a moment to join us – a bite-sized learning experience awaits you.
In a world demanding faster, more precise drug discovery, traditional methods are reaching their limits. Multiomics AI transforms early-phase drug development by integrating complex molecular data in a unified framework to deliver actionable insights at unprecedented speed. This approach deepens our understanding of biological systems and accelerates therapeutic research. At knowing01, our MultiomicsAI platform enables an in-silico-first approach and complex data analysis for lean drug discovery.
When?
In the rapidly evolving field of R&D, staying ahead with traditional approaches is not enough. Multiomics AI is reshaping the landscape by integrating diverse biological data - genomics, proteomics, metabolomics, etc. - into a unified framework that provides deeper insights into complex biological systems, transforming our understanding of disease mechanisms and accelerating therapeutic innovation. At knowing01, our software solution uses Multiomics AI to optimize research processes and enable complex data analyses.
In the fast-paced biotech and pharma industry, critical decisions are often made on the business side, but target-indication fit at the molecular level is equally important. By leveraging Multiomics AI, scientists can integrate multiple biological data types to gain a comprehensive view of molecular interactions and disease mechanisms, uncovering relationships and patterns that are often overlooked. At knowing01, our easy-to-use software uses this integrated data analysis to improve target identification and clinical success rates.
Integrating various biological data types is an innovative approach for early-phase discoveries. By contextualizing Multiomics data, scientists gain a holistic view of biological processes and can uncover relationships and patterns missed by single-Omics methods. Knowledge graphs provide a robust framework for such comprehensive analyses. At knowing01, we have developed easy-to-use software on a knowledge graph that decodes the complexity of Multiomics to identify clinically relevant disease markers.
In collaboration with Marsico lab at Helmholtz Munich and Ohler lab at Max Delbrück Center Berlin, we ranked RNA-binding proteins by their evidences in 25+ public COVID-19 research studies comparing against approx. 100 datasets with our core software feature “Annotate”.
In collaboration with Knauer-Arloth lab and Marsico lab at Helmholtz Munich, we used our core software feature “Explore” to overlap COVID-19 GWAS variants with known human genes to identify genes affected by COVID-19, which could then be linked to various pre-pandemic datasets.
Let us collaborate on your Multiomics project(s).