Trends

Scispot GLUE - Enabling AI in Biotech

Post by
Scispot GLUE - Enabling AI in Biotech

Introducing Scispot GLUE, a data stitching and transformation toolkit designed for the evolving needs of data-driven and AI biotech companies. Scispot GLUE is the data infrastructure for biotech turning them into AI powerhouses.

Lab Data Extraction and Transformation

Scispot GLUE offers self-serve connectors and agents for scientific apps and instruments. It automates lab data extraction and lab data transformation, pulling data from various scientific applications and instruments, including Benchling and other ELNs and LIMS. This addresses a common challenge in labs: data silos.

Real-World Use Case:

A biotech company researching new cancer therapies often struggles with data scattered across various systems, leading to inefficiencies in data analysis. Scispot GLUE can extract and transform this disparate data, enabling a more streamlined research process.

Data Management

Scispot GLUE offers a data lake for biotech which has embedded Jupyter Notebook and R Studio with 100s of transformation script templates, addressing the challenge of analyzing multi-modal multi-omics data.

Real-World Challenge:

Biotech companies managing large-scale genomics projects need to validate and trace vast amounts of structured and unstructured data. Scispot GLUE's configurable rules and complete data provenance address this need, enhancing lab data integrity.


Scientific Instrument Integration

Scispot's GLUE is adept at both push and pull integration with key laboratory instruments, including the top 10 like the Thermo Fisher Scientific Vi-CELL XR and the Eppendorf BioFlo 320. This versatility means it can actively retrieve data from these instruments (pull) or receive data sent automatically (push). Beyond mere data aggregation, Scispot GLUE excels in transforming raw data into analysis-ready formats. For instance, data from the BD Biosciences FACSCanto II cell analyzer, rich in cell counts and biomarker expressions, is not only integrated but also converted into comprehensive reports for immediate use in immunological studies. Similarly, metabolic rates data from the Agilent Technologies Seahorse XF Analyzer are processed for easy interpretation, aiding in critical drug discovery research. This transformation capability ensures that researchers spend less time on data preparation and more on insightful analysis, thus accelerating the pace of scientific discovery.

Artificial Intelligence and Machine Learning

Scispot GLUE leverages AI/ML for tasks like abstracting data elements from clinical records and developing predictive models, essential for AI in Biotech.

Use Case Example:

A pharmaceutical company conducting clinical trials needs to process and analyze clinical records efficiently. Scispot GLUE's NLP capabilities can abstract key data elements for quicker analysis and insights, showcasing the role of AI in Biotech.

Analysis and Visualization

Scispot GLUE supports advanced data visualization, a critical aspect of data analysis in biotech.

Real-World Application:

For a team working on drug discovery, visualizing molecular interactions and experiment results is crucial. Scispot GLUE's integration with tools like Python and R libraries facilitates custom, shareable visualizations, addressing lab interoperability challenges.

Lab Interoperability and Data Integrity

In addressing lab interoperability and ensuring lab data integrity, Scispot GLUE tackles the challenge of integrating diverse lab systems and maintaining data accuracy, furthering the role of AI in Biotech.

Challenge Example:

Integrating data from different laboratory instruments is a major hurdle in biotech research. Scispot GLUE’s LIMS integration capability ensures seamless data flow between systems, a key component of AI in Biotech.

Native Apps for ELN and LIMS Alternatives

Recognizing the need for intuitive data management tools, Scispot GLUE offers alt-ELN and alt-LIMS solutions, particularly beneficial for computational biologists.

Real-World Need:

Computational biologists often require flexible and powerful tools to manage data. Scispot GLUE’s API-first native apps provide this flexibility, enhancing lab workflow efficiency and supporting the advancement of AI in Biotech.

Conclusion

Scispot GLUE, with its advanced data management capabilities, addresses critical real-world challenges in the biotech industry. By integrating features of leading data management platforms and focusing on lab interoperability, lab data integrity, and LIMS integration, it represents a comprehensive solution for modern biotech data challenges, positioning biotech firms to become AI powerhouses.


Read our blog on the top 5 AI Biotech Companies Leading the Charge here

AI in biotechnology

Sign up for Scispot

What’s a Rich Text element?

The rich text element allows you to create and format headings, paragraphs, blockquotes, images, and video all in one place instead of having to add and format them individually. Just double-click and easily create content.

Static and dynamic content editing

A rich text element can be used with static or dynamic content. For static content, just drop it into any page and begin editing. For dynamic content, add a rich text field to any collection and then connect a rich text element to that field in the settings panel. Voila!

How to customize formatting for each rich text

Headings, paragraphs, blockquotes, figures, images, and figure captions can all be styled after a class is added to the rich text element using the "When inside of" nested selector system.