MedGraph EDGE: AI-enabled knowledge discovery module of MedGraph<sup>TM</sup> for high-throughput biomedical insight generation and right decision-making with biocuration at scale
Creators: name: Vipra, Ritvik
name: Dwivedi, Rishabh
name: Vipra, Madhura
Publisher: name: F1000 Research Limited
Year: 2025

description: <b>MedGraph EDGE: AI-Enabled Knowledge Discovery Platform for Biocuration at Scale</b><br>
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<b>Abstract</b><br>
The explosion of biomedical data from multi-omics, literature, clinical trials, and patents has led to an unprecedented demand for scalable and accurate knowledge curation systems. Traditional manual approaches, while reliable, are inherently limited by human capacity, introducing inefficiencies and potential errors when applied at scale. MedGraph EDGE, developed by Medvolt.ai, is an AI-powered knowledge discovery module designed to address these limitations. Integrating expert-curated datasets with advanced AI and natural language processing (NLP) pipelines, MedGraph EDGE enables scalable, semi-automated biocuration across unstructured and semi-structured biomedical domains. This manuscript details the conceptual foundation, technological architecture, use cases, and benchmarking outcomes of MedGraph EDGE, demonstrating its transformative potential in accelerating biomedical research, enabling translational applications, and supporting open science through FAIR-aligned methodologies.<br>
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<b>1. Introduction</b><br>
Biomedical research has entered the era of data abundance. Advances in high-throughput sequencing, electronic health records, multi-omics technologies, and global publication repositories have created an expansive, complex landscape of biomedical knowledge. However, the utility of this data is constrained by the lack of scalable, accurate curation methods. Manual curation, although gold-standard in precision, is laborious, time-consuming, and increasingly unsustainable.<br>
Biocuration plays a central role in organizing this knowledge, enabling tasks such as target identification, drug repurposing, biomarker discovery, and clinical translation. The growing heterogeneity and volume of biomedical data necessitate innovative approaches that combine automation with expert validation. MedGraph EDGE emerges as a response to this need, offering a hybrid platform that leverages artificial intelligence, machine learning, and domain-specific NLP to enhance the scope, speed, and fidelity of biomedical knowledge extraction and representation.<br>
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<b>2. Challenges in Scalable Biocuration</b><br>
Despite the richness of biomedical data, several core challenges impede the extraction of meaningful insights:<br>
<b>2.1 Data Volume and Velocity</b><br>
The influx of scientific publications, omics data, and clinical information exceeds the processing capacity of traditional biocuration workflows.<br>
<b>2.2 Fragmentation and Inconsistency</b><br>
Biomedical information is dispersed across formats (structured, semi-structured, unstructured) and repositories (e.g., GEO, PubMed, ClinicalTrials.gov), often lacking standardized metadata or ontological alignment.<br>
<b>2.3 Manual Bottlenecks</b><br>
Manual curation introduces subjectivity, requires extensive expert hours, and cannot scale to support real-time decision-making in R&amp;D pipelines.<br>
<b>2.4 Sustainability and FAIR Compliance</b><br>
Data reuse and reproducibility are critical for open science. Yet, many existing systems fall short of FAIR principles, limiting the broader impact and interoperability of curated datasets.<br>
<b>2.5 Semantic Ambiguity and Lack of Contextualization</b><br>
Entities such as genes, drugs, and pathways are frequently referenced with overlapping or inconsistent nomenclature, requiring domain-aware disambiguation and contextual linking to derive accurate biological meaning.<br>
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<b>3. System Overview: MedGraph EDGE</b><br>
MedGraph EDGE is a modular, AI-enabled knowledge discovery platform engineered for high-throughput biomedical biocuration. It synergistically combines expert-curated data, automated NLP pipelines, ontological mappings, and dynamic knowledge graph construction.<br>
<b>3.1 Architecture</b><br>
MedGraph EDGE comprises two core modules:

 <b>Curated Knowledgebase</b>: Gold-standard proprietary datasets built over years of expert annotation from omics, literature, and clinical trial data.
 <b>AI-Driven Extraction Pipeline</b>: Automated ingestion and analysis engine powered by LLMs, transformer models, LSTM networks, and statistical machine learning.

Together, these components create a living knowledge graph that continuously updates as new information becomes available.<br>
<b>3.2 Expert-in-the-Loop Curation</b><br>
The platform follows a hybrid model, where AI-generated outputs are reviewed, validated, or enhanced by domain experts. This ensures both scalability and quality control across critical biomedical use cases.<br>
<b>3.3 Data Sources</b><br>
MedGraph EDGE integrates data from:

 Scientific literature (PubMed)
 Public repositories (GEO, ClinicalTrials.gov)
 Patents and regulatory submissions
 Proprietary omics datasets
 Biomedical ontologies (e.g., GO, DOID, MeSH, UMLS, ChEBI)<br>
  

<b>4. Core Technologies and Methodologies</b><br>
<b>4.1 NLP and Biomedical LLMs</b><br>
Custom-trained biomedical large language models (LLMs) underpin key tasks such as named entity recognition (NER), relationship extraction, evidence retrieval, and summarization. These models are fine-tuned on domain-specific corpora to enhance precision.<br>
<b>4.2 Retrieval-Augmented Generation (RAG)</b><br>
RAG pipelines combine structured knowledge retrieval with generative capabilities, allowing for dynamic, context-aware question answering and knowledge synthesis.<br>
<b>4.3 OCR and Table Extraction</b><br>
Layout-aware document processing enables ingestion of PDFs, scanned documents, and tables. Tools inspired by PubTable and layout parsers ensure accurate interpretation of scientific figures and tabular data.<br>
<b>4.4 Ontology-Based Normalization</b><br>
Named entities are normalized and mapped to reference ontologies using semantic matching algorithms. This enhances the interpretability, reusability, and interoperability of extracted knowledge.<br>
<b>4.5 Scalable Microservices Architecture</b><br>
The platform's backend is designed to handle large-scale parallel processing, enabling real-time ingestion and annotation across thousands of documents.<br>
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<b>5. Ontology-Driven Knowledge Representation</b><br>
At the heart of MedGraph EDGE lies a dynamic, functionally enriched <b>knowledge graph (KG)</b>. The KG represents:


 Entities (e.g., genes, variants, proteins, diseases, drugs, pathways)
 Relationships (e.g., activates, inhibits, regulates, associated_with)
 Context (e.g., cell line, species, condition, study type)

Each node and edge is enriched with ontology-backed metadata, provenance, and semantic links, enabling structured reasoning and advanced analytics.<br>
The KG is dynamically updated through both automated ingestion and expert annotation. Visualization interfaces support intuitive exploration and hypothesis generation.<br>
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<b>6. Applications in Biomedical Discovery</b><br>
MedGraph EDGE supports diverse use cases across the drug discovery and translational medicine spectrum:<br>
<b>6.1 Target and Biomarker Discovery</b><br>
Combines omics datasets with curated literature evidence to prioritize high-confidence gene/protein targets.<br>
<b>6.2 Mechanism of Action Elucidation</b><br>
Traces multi-step molecular interactions across pathways and entities to uncover disease mechanisms.<br>
<b>6.3 Drug Repurposing and Indication Expansion</b><br>
Maps compound-disease associations and literature-based signals to identify repurposing opportunities.<br>
<b>6.4 Multi-Omics Integration</b><br>
Enables cross-layered insights by aligning genomics, transcriptomics, proteomics, and metabolomics data.<br>
<b>6.5 Variant Interpretation</b><br>
Annotates genomic variants with clinical relevance, statistical significance, and supporting evidence.<br>
<b>6.6 Clinical and Translational Support</b><br>
Provides real-time, evidence-backed answers to clinicians and translational researchers through curated dashboards and search interfaces.<br>
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<b>7. Benchmarking and Performance Evaluation</b><br>
MedGraph EDGE has been validated across internal and external datasets to assess precision, scalability, and usability.<br>
<b>7.1 Curation Speed</b>


 Reduces manual effort by <b>60–70%</b>
 Ingests and processes <b>1000+ documents per week</b>

<b>7.2 Accuracy Metrics</b>


 Achieves <b>85–90% F1 scores</b> on NER and relationship extraction tasks
 High validation accuracy on gene-disease and drug-target associations

<b>7.3 Usability Feedback</b>


 Rated highly by biomedical experts on interpretability and integration ease
 Visual KG modules facilitate intuitive navigation and annotation

<b>7.4 Interoperability</b>


 Outputs aligned with FAIR principles
 Compatible with RDF, JSON-LD, and other open data exchange standards

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<b>8. Case Studies and Real-World Deployment</b><br>
<b>8.1 Consumer Genomics</b><br>
Used to annotate and prioritize gene variants from large-scale genomic panels, supporting consumer reporting modules.<br>
<b>8.2 Microbiome Analysis</b><br>
Enabled mapping of microbial taxa to metabolic functions and disease relevance through literature-curated evidence.<br>
<b>8.3 Drug Discovery Support</b><br>
Assisted in target deconvolution and validation for oncology and metabolic disorders using structured multi-omics-lens KG exploration.<br>
<b>8.4 Decentralized Clinical Trials (DCTs)</b><br>
Supported real-time literature surveillance and evidence summarization for trial investigators across therapeutic domains.<br>
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<b>9. FAIR Alignment and Sustainability</b><br>
MedGraph EDGE is purpose-built for long-term, sustainable impact:

 <b>Findability</b>: Ontology-tagged, indexed entities
 <b>Accessibility</b>: Secure APIs and user-friendly dashboards
 <b>Interoperability</b>: Ontology-driven normalization and RDF outputs
 <b>Reusability</b>: Modular knowledgebases supporting continuous updates and reuse across projects

By minimizing manual dependency and enabling collaborative validation, the platform reduces curation costs, shortens timelines, and promotes open science.<br>
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<b>10. Conclusion and Future Directions</b><br>
MedGraph EDGE represents a paradigm shift in biomedical biocuration. By integrating scalable automation with expert oversight, it addresses the most pressing challenges in knowledge extraction, interpretation, and reuse. Its modularity, precision, and domain-specific intelligence position it as a central infrastructure for next-generation biomedical discovery.<br>
Ongoing development efforts are focused on:

 Expanding cross-omics coverage
 Enhancing multilingual literature mining
 Federated learning pipelines for distributed data contexts
 Seamless plug-ins for electronic lab notebooks (ELNs) and clinical platforms

As biomedical data grows more complex, MedGraph EDGE provides a sustainable, intelligent, and open-aligned path forward for researchers worldwide.<br>
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<b>Keywords</b>: Open Data, Open Science, Ontology, Knowledge Graph, Repositories, Annotation, Genomics, Multi-Omics, LLM, RAG, NLP, ML, AI, FAIR, Curation, Biocuration at Scale