Abstract 5365: Extracting novel knowledge from scientific literature to build a web portal for cancer researchers to keep up with the latest scientific discoveries

Abstract

Abstract Millions of new papers are published in biomedical sciences every year. In many disciplines, it has become impossible to read all the published new papers to learn what is happening in the frontier of a particular area. This gap is widening as the publishing speed has been accelerating in recent years. To address this challenge, one can convert unstructured text data into a structured form, which can then support highly accurate information retrieval, information integration, and automated knowledge discovery. A plausible approach for such a task is to use named entity recognition (NER) and relation extraction (RE) methods to identify important biological entities and extract their relations to construct knowledge graphs (KGs). The LitCoin Natural Language Processing (NLP) Challenge was recently organized by NCATS of NIH and NASA to spur innovation by rewarding the most creative and high-impact uses of biomedical text to create KGs. In addition to entities and relations, the manually annotated LitCoin dataset also contains the annotations of relations being new discoveries or background knowledge. Our team participated in the challenge and ranked first place. The novelty prediction model of our pipeline has achieved an F1 score of 0.90. We have applied our model to all the PubMed abstracts published previously and the newly published ones to extract the novel discoveries in each article. A web portal has been created to allow scientists to view the latest discoveries in cancer research. The web portal is updated daily with versatile visualization tools for cancer researchers to quickly grasp the latest discoveries in a particular area. It also offers powerful functions to explore the existing literature and make sophisticated inferences about causal and indirect relationships. Citation Format: Feng Pan, Yuan Zhang, Xin Sui, Donghu Sun, Menghan Chung, Jinfeng Zhang. Extracting novel knowledge from scientific literature to build a web portal for cancer researchers to keep up with the latest scientific discoveries. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5365.