Text mining for precision medicine: automating disease-mutation relationship extraction from biomedical literature.
Identifying disease-mutation relationships is a significant challenge in the advancement of precision medicine. The aim of this work is to design a tool that automates the extraction of disease-related mutations from biomedical text to advance database curation for the support of precision medicine.



We developed a machine-learning (ML) based method to automatically identify the mutations mentioned in the biomedical literature related to a particular disease. In order to predict a relationship between the mutation and the target disease, several features, such as statistical features, distance features, and sentiment features, were constructed. Our ML model was trained with a pre-labeled dataset consisting of manually curated information about mutation-disease associations. The model was subsequently used to extract disease-related mutations from larger biomedical literature corpora.



The performance of the proposed approach was assessed using a benchmarking dataset. Results show that our proposed approach gains significant improvement over the previous state of the art and obtains F-measures of 0.880 and 0.845 for prostate and breast cancer mutations, respectively.



To demonstrate its utility, we applied our approach to all abstracts in PubMed for 3 diseases (including a non-cancer disease). The mutations extracted were then manually validated against human-curated databases. The validation results show that the proposed approach is useful in a real-world setting to extract uncurated disease mutations from the biomedical literature.



The proposed approach improves the state of the art for mutation-disease extraction from text. It is scalable and generalizable to identify mutations for any disease at a PubMed scale.
National Center for Biotechnology Information (NCBI), National Library of Medicine (NLM), National Institutes of Health, Bethesda, MD, USA.
Ayush
A
Singhal
National Center for Biotechnology Information (NCBI), National Library of Medicine (NLM), National Institutes of Health, Bethesda, MD, USA.
Michael
M
Simmons
National Center for Biotechnology Information (NCBI), National Library of Medicine (NLM), National Institutes of Health, Bethesda, MD, USA zhiyong.lu@nih.gov.
Zhiyong
Z
Lu
D001943:Breast Neoplasms / Q000235:genetics*; D019295:Computational Biology; D057225:Data Mining / Q000379:methods*; D019992:Databases as Topic; D005260:Female; D006801:Humans; D000069550:Machine Learning; D008297:Male; D009154:Mutation; D057285:Precision Medicine; D011471:Prostatic Neoplasms / Q000235:genetics*; D039781:PubMed
automated extraction; breast cancer; disease-mutation relationship; machine learning; precision medicine; prostate cancer; text mining