Biomedical Text Mining
Biomedical Text Mining - Related Videos
09:20
Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications
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Cited by 9 •
2019
We present a protocol and associated programming code as well as metadata samples to support a cloud-based automated identification of phrases-category association representing unique concepts in user selected knowledge domain in biomedical literature. The phrase-category association quantified by this protocol can facilitate in depth analysis in the selected knowledge domain.
05:47
Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
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Cited by 2 •
2025
This article describes RUGGED (Retrieval Under Graph-Guided Explainable disease Distinction), which integrates Large Language Model (LLM) inference with Retrieval-Augmented Generation (RAG). It draws evidence from expert-curated biomedical knowledge bases and peer-reviewed biomedical publications to synthesize new knowledge from up-to-date information, identify explainable and actionable predictions, and pinpoint promising directions for hypothesis-driven investigations.
07:35
A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
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Cited by 2 •
2023
A computational protocol, CaseOLAP LIFT, and a use case are presented for investigating mitochondrial proteins and their associations with cardiovascular disease as described in biomedical reports. This protocol can be easily adapted to study user-selected cellular components and diseases.
10:33
Generation of Alginate Microspheres for Biomedical Applications
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Cited by 33 •
2012
In the following sections, we outline procedures for the preparation of alginate microspheres for use in biomedical applications. We specifically illustrate a technique for creating multilayered alginate microspheres for the dual purpose of cell and protein encapsulation as a potential treatment for type 1 diabetes.
09:22
Fabricating Superhydrophobic Polymeric Materials for Biomedical Applications
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Cited by 4 •
2015
Two- and three-dimensional superhydrophobic polymeric materials are prepared by electrospinning or electrospraying biodegradable polymers blended with a lower surface energy polymer of similar composition.
FAQs
Biomedical text mining can move from unstructured prose toward analyzable evidence by combining several NLP functions. Entity recognition identifies mentions of genes, proteins, metabolites, and diseases; terminology normalization aligns those mentions with consistent concepts; relation extraction captures links among them; and information retrieval helps locate relevant documents or passages. Together, these stages support systematic study of biochemical findings across literature.
Terminology normalization is important because biomedical literature can refer to related molecular concepts through varied wording. By aligning mentions with consistent concepts, the method makes findings easier to aggregate and compare across documents. This is especially useful when tracing genes, proteins, metabolites, or diseases through biochemical research and connecting them to functions or pathways.
Relation extraction moves analysis beyond listing entities by identifying stated connections among molecular concepts and biomedical findings. It can help associate genes, proteins, metabolites, or diseases with biochemical functions, pathways, experimental findings, or disease mechanisms. These structured links make it easier to examine how separate statements in the literature fit together.
Researchers can use Biomedical Text Mining to assemble evidence scattered across publications. The resulting links among molecular entities, functions, pathways, experimental findings, and disease mechanisms can reveal connections that are difficult to detect manually. Such connections can guide new questions and support hypothesis generation within biochemical research.
The method can reduce the time required to locate relevant results, organize evidence, and support literature-based discovery. Information retrieval helps researchers find pertinent material, while extracted entities and relationships provide a structured view of findings distributed across documents. This combination is useful when surveying a large literature and identifying molecular connections for further investigation.
Biochemical research often requires connecting molecular entities with functions, pathways, experimental findings, and disease mechanisms across dispersed literature. Biomedical Text Mining provides a way to organize those connections from unstructured text, helping researchers examine biochemical relationships at a broader scale. Its relevance extends from molecular interpretation to literature-based investigation of disease-related mechanisms.