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NLM Intramural Research Program

NLM is leveraging its extensive literature, sequence-related and clinical resources to address a broad range of biomedical information challenges. Our team is developing solutions to topics ranging from fold-switching proteins to visualization of personal health data. It reaches from linear DNA sequences to the complex nature of human behavior. Regardless of where our Investigators land on this spectrum, their motivations are the same—to improve human health. We invite you to learn about the people striving to make a difference through their research.

Research in Action

image of cervical cancer cells AI Tools Provide Picture of Cervical Health

Even though cervical cancer is considered one of the most preventable forms of cancer, it remains a serious and deadly scourge for many across the world. A computer algorithm designed to quickly and easily identify pre-cancerous changes using a regular smartphone may change that.

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screenshot of the Open I search platform A Multimodal Biomedical Information Retrieval System

NLM researchers have developed ways to move beyond conventional text-based searching of biomedical resources, by combining text and visual features in search queries and document representation. A combination of techniques and tools from the fields of natural language processing, information retrieval, and content-based image retrieval allows the development of building blocks for advanced information services. Such services enable searching by textual as well as visual queries, and retrieving documents enriched by relevant images, charts, and other illustrations from the journal literature, patient records and image databases.

Read the paper in the Journal of Computing Science and Engineering
diagram of the scPopCorn algorithm scPopCorn Gets to the Kernel of Single-Cell Experiments

Researchers at the National Library of Medicine’s National Center for Biotechnology Information have created a new algorithm called scPopCorn (single-cell subpopulations comparison) to capture the differences among populations of cells from single-cell experiments.

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