Augmenting the intelligence of bench scientists to discover new ways to treat disease and develop next-generation therapeutics.
Augmenting the intelligence of bench scientists to discover new ways to treat disease and develop next generation therapeutics.
We build Sapiens to be the OS for the modern biotech and pharma. We abstract away all the complexities of the command-line interface, statistical modeling, or querying languages with a clean and intuitive GUI (Graphical User Interface) so that scientists and decision makers can explore their data faster, discover the anomalies and trends of interest, generate hypothesis and actionable insights, and make those critical decisions for their business. We call this the Explainable GUI (xGUI).
NLP Knowledge Extraction
Extracting underlying structure and connections from deep sources of disjointed knowledge i.e., literature, sequencing, expression type data
Graph Algorithms & Ontology
Relevancy & Context
NExTNet’s proprietary NLP, Graph algorithms and Ontology semantically connecting extracted knowledge
Ask and Answer complex questions, perform conceptual search for what you do not know via Sapiens’ xGUI
Insights & Decisions
Upload experimental data, contextualize, manipulate graph, analyze relationships and generate hypothesis
Our Breakthrough Innovation
Our Breakthrough Innovation
Our proprietary Natural Language Processing (NLP) and other multi-modal knowledge extraction modules automate pipelines of disjointed data sources (from unstructured full-text scientific literature to complex molecular data such as whole genome sequencing and expression atlases); connecting these extracted knowledge semantically using our cutting-edge Graph algorithms and Ontology, building a massive and rapidly expanding "Knowledge Graph" of biological objects (cells, genes, proteins, disease, pathways etc.). On top of that, we have built our proprietary search and discovery engine that surfaces sub-networks of hidden relationships between these biological objects.
We are also building an end-to-end no-code workflow that will allow scientists and decision makers to upload their "behind the firewall" experimental data onto the platform securely, contextualize that data and understand how that is embedded in the broader knowledge space, and directly manipulate these relationships in their Enterprise Knowledge Graph generate new hypothesis and actionable insights.
Most modern "black-box" AI systems rely on statistical confidence and probabilistic scores, often lacking explainability, with opaque logic and making predictions that are hard to trust. Whereas Sapiens surfaces transparent search results (i.e., relationships between biological objects), the underlying data and evidence for each result in a visual network format, with full provenance and lineage of the data and user-generated insights preserved. Bench scientists can explore interconnected data visually as maps that track to how humans think and determine for themselves the strength of a result.