Versatile and open analytics for the Life Sciences
Accelerate drug discovery and development, optimize manufacturing and improve marketing precision by turning data into insight.
KNIME powers solutions for Life Science teams
Accelerate the time of taking a drug to market with streamlined data analysis.
Standardize lab automation and improve experimental reproducibility.
Ensure regulatory compliance with transparent, automatic documentation of processes.
Success stories
KNIME was a big enabler for us as a business function, allowing us to build data pipelines in a low-code way while still offering the flexibility to go deeper when needed.
Marcel Meyer, Head of Data Governance and Management, SIEMENS Healthineers
Use Cases
Real-world applications of data analytics in Life Sciences.Drug Development & Research
- Drug Repurposing
- Genomics and Bioinformatics
Clinical Trials & Research Optimization
- Clinical Trial Data Analysis
- Data Harmonization
- Healthcare Data Analytics
Patient Analytics & Personalization
- Healthcare Data Integration
- Patient Engagement Analytics
- Patient Phenotyping
- Patient Risk Prediction
- Medical Imaging Analysis
Compliance
- Regulatory Compliance Analytics
Why KNIME for Life Sciences
Sophisticated analytics, intuitive interface
- Allow scientists to build analytic models and automate tedious spreadsheet work without any coding and with a genAI assistant to lean on
- Lower the barrier to data science with a low-code/no-code environment, by providing access to novel and highly advanced algorithms including LLMs.
- Enable teams to script custom algorithms in their preferred language with built-in integrations with R, Python, Java, and more and the choice to auto-generate scripts with genAI.
A single environment for the entire pipeline
- Minimize the impact of siloed, disorganized data sitting in internal archives at clinical institutions, with research organizations, or even external partners.
- Use a single platform to support projects from start to finish with end-to-end coverage: from easy data access and prep, to visual data exploration and modeling, to enterprise-wide deployment, monitoring, and management.
- Access advanced analytics within one consistent environment to streamline the entire pipeline from drug discovery through to production, sales, and marketing.
Extensible and flexible
- Access multiple data types - electronic medical records, x-ray images, diagnosis records, personal patient information, clinical trial data, audit trails, inventory management data, supply chain documents, insurance records, and financial information.
- Give scientists access to over 300 data sources to enable them to work with their specific life science data types easily and within a single, consistent environment.
Future-proof with open source
- Keep abreast of the rapidly evolving data science ecosystem, and enable data analysts to keep pace with new technologies and techniques so they can forecast the future of diseases, medicines, gene expressions, or patient health.
- Stay on the bleeding edge of modern-day data science with KNIME’s open source approach. No vendor-developed tool can keep pace with the innovation driven by an active open source user community.
- Access many native functionalities dedicated to the life science field in addition to community extensions, such as RDKit, ImageJ2, SeqAn, and more.
Scale as you grow
- Continue to collect data from the ever-growing number of sources today and in the future, and as the technology environment evolves - disease diagnosis instruments, patient records, lab test results, and more.
- Process and analyze petabytes of data quickly, either in the cloud or internal data centers.
- Let multi-disciplinary teams work and collaborate in their preferred environment and language to create models and custom functionality.
- Share and reuse analytics easily and across the organization.
LLMs, RAGs and KNIME for biopharmaceutical applications
Wave Life Sciences, a clinical-stage biotech in Cambridge, MA, uses GenAI and KNIME to automate biomedical research, leveraging LLMs, vector stores, and PubMed retrieval to enhance drug discovery and support clinical trials in Huntington’s, Duchenne muscular dystrophy, and alpha-1 antitrypsin deficiency.
Streamlining the Design of DNA-Encoded Libraries at GSK using KNIME
GSK uses KNIME Analytics to automate and optimize the selection of building blocks for DNA-encoded libraries (DELs), enabling chemists to efficiently analyze, visualize, and streamline drug discovery workflows without needing coding expertise.
Implementation of automated GxP data pipelines using KNIME
The talk explains how Sumitomo Pharma used KNIME to automate clinical research data ingestion, standardization, and validation, reducing the traditional 16-20 day delay to near real-time analytics, enabling proactive decision-making, and ensuring compliance with GxP standards while integrating with existing SAS-based workflows.
