Sbir phase i: knowledge graph-powered information retrieval and causal inference

The National Science Foundation (NSF) awarded Insilicom LLC a $275,000 SBIR Phase I Project Grant under the NSF Technology, Innovation, and Partnerships (CFDA 47.084) program. The grant supports the development of AI-powered, commercially viable applications enabled by Insilicom's large-scale biomedical knowledge graph. Key objectives include increasing the coverage of the knowledge graph, building a novel literature alert system called iPulse, and enhancing Insilicom's versatile query interface to enable highly accurate information retrieval and causal inference. This project aims to revolutionize how biomedical knowledge is accessed, interpreted, and utilized, benefiting the pharmaceutical industry and academic research. The award reflects NSF's mission to support science and engineering research leading to breakthrough technologies and solutions for national and societal challenges.

Abstract

The broader/commercial impact of this small business innovation research (sbir) phase i project is as follows. The exponential growth of scientific literature poses two critical challenges: (1) missing important prior studies during research design can lead to resource and time wastage, incorrect conclusions, and missed discoveries, and (2) effectively utilizing the vast volume of scientific knowledge in raw text form has become increasingly difficult. This project aims to build disruptive, commercially valuable products that address these challenges, benefiting the pharmaceutical industry and academic research. In addition, the success of the project in developing advanced AI technologies will have a significant impact on the growth and development of the AI industry in tallahassee, FL, and the broader southeast region of the united states. This small business innovation research (sbir) phase i project aims to develop AI-powered, commercially viable applications enabled by a large-scale biomedical knowledge graph (KG) constructed recently using an award-winning natural language processing (NLP) pipeline. The KG has been further transformed into a causal KG by integrating causal relations and enhanced by incorporating data from 40 public databases and analysis results of some commonly used genomics datasets. To facilitate seamless access to the KG, the project team has developed a versatile query interface named iexplore. This interface enables highly accurate information retrieval and supports causal inference, providing users with valuable insights. In the current project, insilicom LLC will further increase the coverage of the KG and build a novel literature alert system called ipulse. By combining the advancements in AI, the richness of the knowledge graph, and the utility of the query interface and literature alert system, this project will result in practical and commercially viable applications that will revolutionize the way biomedical knowledge is accessed, interpreted, and utilized. 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.- subawards are not planned for this award.