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.
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.
Identifying key disease marker is crucial but challenging in drug development. Discovering biomarkers for Alzheimer's disease, for example, not only improves diagnostic accuracy, but also therapeutic interventions that can slow or reverse disease progression. To effectively and efficiently find targets, various Omics data (Multiomics) must be integrated and contextualized, a difficult and time-consuming task. At knowing01, we have developed a model decoding the complex interplay of various Omics to identify key disease markers.
When?
In drug development, it is often noticed that a target may apply to diseases beyond its initial identification. For instance, methotrexate was developed as a chemotherapy agent for cancer, but its anti-inflammatory properties later helped in treating autoimmune diseases such as rheumatoid arthritis. Leveraging data from Multiomics enables early identification of suitable indications for a target. At knowing01, we have developed a flexible Multiomics data model that supports the selection of the right indications from the start.
In today's Big Data era with its growing amount of high-quality data, it is a huge challenge to analyze it without overlooking important hidden patterns. Especially in biotech and pharma, where multiple Omic layers (Multiomics) need to be analyzed, the right tools and expertise are required. However, the available tools are often experimental or can only be used in a narrow range of applications. At knowing01 we have developed an elastic biodata model that links Multiomics data in a flexible way without transforming it.
In the rapidly evolving field of single-cell genomics, the detailed analysis of individual cells offers groundbreaking potential for understanding complex diseases. As we delve deeper into the nuances of cellular behavior, the challenge is to effectively contextualize this wealth of data to identify cell types and states that are critical to disease mechanisms and response to therapy. At knowing01, we linked single-cell data with our model to identify novel disease-relevant cell types and mechanisms for target validation.
The wealth of Omics data and disease-related information available is a goldmine for understanding complex health conditions and treatments. However, sifting through vast amounts of data and extracting meaningful information can be a daunting task. Knowledge graphs can intelligently link these datasets together, while subsequent graph-based analytics can provide valuable insights into potential indications. At knowing01, we incorporated graph-based analytics into our model to rapidly evaluate new datasets.
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. The flexible structure of a knowledge graph can be quickly adapted to complex data and enables efficient network analysis to uncover hidden biological patterns. At knowing01, we developed an elastic biodata model linking Multiomics data that is based on a knowledge graph.
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. At knowing01, we developed just that, an elastic biodata model that doesn't miss valuable insights.
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.
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.
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.
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.
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.
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).