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knowing01 resources

Your hub for insights into our solution. Discover our white papers and Multiomics webinar series and read our scientific publications. Learn and grow with us.

White papers

  • Missing the insights for the Omics data

    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.

    Download White Paper

  • Context is key to success

    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.

    Download White Paper

Multiomics webinar series

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.

Upcoming webinar

  • Multiomics data integration with knowledge graphs

    In natural sciences, methods such as mass spectrometry and next-generation sequencing generate huge amounts of data. Integrating them through Multiomics analyses can provide deeper insights into genetic information. In biology, networks and graphs can represent linked data whose information is organized into nodes and edges. The flexible structure of a knowledge graph can be quickly adapted to complex data and enables efficient network analysis to uncover hidden biological patterns.


    Register here

Past seminars

  • Missing the insights for the Omics data

    Data plays a crucial role in disease research. Its complexity and quality are increasingly improved by automation, cost reduction and new technologies. However, simply expanding the volume of data is not enough to meet today's unmet medical needs. The challenge is to link Multiomics data in a way that preserves important contextual information and prevents valuable insights from being overlooked.

  • Multiomics context-driven target prioritization

    Target prioritization is a crucial process in drug development, often performed using a list of potential therapeutic targets. However, it is a major challenge to manually find out what is already known about these targets in order to prioritize them. A context-driven prioritization approach of Multiomics data could simplify this task enormously. At knowing01, we specialize in context-driven Multiomics analyses for prioritizing targets.

  • Covid-19 Multiomics data integration

    We are surrounded by data, even in COVID-19 disease research and world-wide efforts generated heaps of data and analysis results: information – on molecular regulation – that is scatterered across hundreds of publications. While much scientific output is produced, fetching the millions of datapoints and making them available is crucial. At knowing01, we specialize in exactly that, leveraging multi-omics data for early research and development.

  • Multiomics context-driven target identification

    In drug development, identifying therapeutic targets – genes significantly influencing a disease – is crucial. Often using discovery datasets, we sift through potential targets and eliminate irrelevant genes. However, understanding these genes can be challenging. At knowing01, we streamline this process, employing Multiomics context-driven analysis to adeptly identify drug targets.

  • Linking Multiomics layers for early discoveries

    High-throughput profiling illuminates human diseases but identifying disease markers from high-dimensional data poses challenges like excessive hits, uncertainty over dataset usage, and the need for Data Science expertise. Systematic multiomics data layering helps filter interesting candidates and remove “noise” genes. At knowing01, we’re experts in multiomics analysis and data layering.

  • Leverage the value of Multiomics data

    Data heterogeneity poses integration challenges, making Multiomics – the comparison of varied datasets – crucial in contemporary research. Despite its pros and cons, a unified biodata model shows promise. At knowing01, we’re experts in crafting a unified data model for optimal Multiomics use in early research and development.


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Scientific publications

  • Computational Mapping of the Human-SARS-CoV-2 Protein-RNA Interactome

    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”.

    Marc Horlacher, Svitlana Oleshko, Yue Hu, Mahsa Ghanbari, Giulia Cantini, Patrick Schinke, Ernesto Elorduy Vergara, Florian Bittner, Nikola S. Mueller, Uwe Ohler, Lambert Moyon, Annalisa Marsico. bioRxiv 2021.12.22.472458. Read on bioRxiv. NAR Genomics and Bioinformatics 2023; 5(1):lqad010. Read on NAR.

  • Network Embedding Elucidates Host Factors Important for COVID-19 Infection

    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.

    Yue Hu, Ghalia Rehawi, Lambert Moyon, Nathalie Gerstner, Christoph Ogris, Janine Knauer-Arloth, Florian Bittner, Annalisa Marsico and Nikola S. Mueller. Frontiers in Genetics 2022; 13:909714. Read on Front Genet.


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