Scispot's knowledge graphs are ontologies that contextually connect all of your data within the platform using nodes and edges. Scispot uses AI to collate the information from your ELN (labspaces) and LIMS (labsheets), and bring it together in one graphical format.
As a result, you can track the complete journey of your data and metadata along with their connections graphically.
Scispot also helps customers fine-tune the knowledge graph based on specific use cases per customer.
What are ontologies?
Ontologies in biotech serve as comprehensive frameworks to organize information, enabling entities within the domain to be interconnected uniquely. Think of a knowledge graph as a vast network where each node represents an entity (e.g., a gene, protein, or disease), and edges symbolize the relationships between them. This structure not only facilitates sophisticated searches across diverse datasets but also ensures the integrity of data through a complete chain of custody. For instance, in Scispot's platform, if you're investigating a specific protein's role in a disease, nodes would represent the protein and the disease, and edges would depict their relationship, such as "targets" or "is involved in".
Tracking Chain of Custody
A critical application of these ontologies is tracking the chain of custody in biotech research. For every sample or data point, the knowledge graph can meticulously record its origin, handling, and analysis history. This traceability ensures that researchers can verify the authenticity and integrity of their data, crucial for the reproducibility and validation of findings.
Example:
Imagine tracing the development of a new drug. Each step, from its initial discovery in a specific organism, through various stages of testing and modification, to its final approval for use, can be mapped. Nodes represent the drug at each stage, while edges document processes, results, and transfers between labs or facilities, ensuring transparency and accountability.
Utilizing Data for Machine Learning and Data Science
The rich, structured data from knowledge graphs is invaluable for training machine learning (ML) models, particularly in biotech AI companies aiming to create their own intellectual property (IP). Here's how:
Predictive Modeling: By analyzing the relationships and patterns within the graph, ML algorithms can predict outcomes like drug efficacy or side effects, expediting the discovery process.
Data Integration: Knowledge graphs enable the integration of heterogeneous data sources, offering a holistic view that enhances the quality of ML training datasets.
Personalized Medicine: Algorithms can use these graphs to recommend personalized treatment plans by correlating genetic information, disease markers, and drug interactions stored within the graph.
Example:
A biotech company might use a knowledge graph to train an algorithm that predicts the success rate of a novel therapy based on genetic markers. By feeding the model with data on how similar therapies have interacted with those markers in the past (extracted from the graph), the company can refine its research direction and potentially reduce development time and costs.
Conclusion
Scispot's knowledge graphs provide a foundational tool for organizing, tracking, and leveraging biotech data. By enabling precise tracking of the chain of custody and offering structured, interconnected datasets, these graphs are pivotal for advancements in research and the development of AI-driven solutions in biotech. The potential to accelerate discovery, optimize research methodologies, and foster innovation in creating proprietary technologies underscores the transformative impact of knowledge graphs in the field.