SBIR Phase I: Development of a Semantic Search Engine Using Natural Language Processing to Generate Validated Technique-Based Recommendations for Life Science Research Methodology

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The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project is to advance the development of an artificial intelligence (AI)-supported search engine that facilitates reproducibility and efficiency in life science research. Development of the proposed technology will allow researchers to quickly access unbiased recommendations on techniques and products to advance scientific discovery. By streamlining the literature search process and optimizing research at the experimental design stage, researchers are able to avoid lengthy trial-and-error in the laboratory and accelerate productive experiments. By providing researchers with literature-supported and relevant experimental recommendations within minutes, the proposed search engine can spare researchers time and resources spent on experimental methods poorly suited to their research goals, while also enabling researchers to explore promising methods potentially outside their standard operations.This Small Business Innovation Research Phase I project seeks to address the persistent problems of experimental inefficiency and irreproducibility that slow life sciences research. Phase I efforts will advance the development and evaluation of a proof-of-concept search engine for recommendation of techniques associated with antibodies, a filter mechanism capable of refining search results, and automatically generated graphical analytics presenting key data on technique usage. Leveraging machine learning and Natural Language Processing (NLP) to scan the entire body of peer-reviewed literature and extract data relevant to technique-based search terms, search outputs will accordingly rank antibodies and protocol conditions. To filter results, constraints, such as access to equipment or target genes, will be imposed through development of NLP algorithms capable of identifying relevant contextual information indicating conformance to imposed criteria. Accuracy and relevance of the developed platform's search results will be compared to a popular research-based search engine and is expected to demonstrate highly refined search outputs and recommendations, supporting improved experimental design.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.