Text Mining Genotype-Phenotype Relationships from Biomedical Literature for Database Curation and Precision Medicine

Abstract

The practice of precision medicine will ultimately require databases of genes and mutations for healthcare providers to reference in order to understand the clinical implications of each patient’s genetic makeup. Although the highest quality databases require manual curation, text mining tools can facilitate the curation process, increasing accuracy, coverage, and productivity. However, to date there are no available text mining tools that offer high-accuracy performance for extracting such triplets from biomedical literature. In this paper we propose a high-performance machine learning approach to automate the extraction of disease-gene-variant triplets from biomedical literature. Our approach is unique because we identify the genes and protein products associated with each mutation from not just the local text content, but from a global context as well (from the Internet and from all literature in PubMed). Our approach also incorporates protein sequence validation and disease association using a novel text-mining-based machine learning approach. We extract disease-gene-variant triplets from all abstracts in PubMed related to a set of ten important diseases (breast cancer, prostate cancer, pancreatic cancer, lung cancer, acute myeloid leukemia, Alzheimer’s disease, hemochromatosis, age-related macular degeneration (AMD), diabetes mellitus, and cystic fibrosis). We then evaluate our approach in two ways: (1) a direct comparison with the state of the art using benchmark datasets; (2) a validation study comparing the results of our approach with entries in a popular human-curated database (UniProt) for each of the previously mentioned diseases. In the benchmark comparison, our full approach achieves a 28% improvement in F1-measure (from 0.62 to 0.79) over the state-of-the-art results. For the validation study with UniProt Knowledgebase (KB), we present a thorough analysis of the results and errors. Across all diseases, our approach returned 272 triplets (disease-gene-variant) that overlapped with entries in UniProt and 5,384 triplets without overlap in UniProt. Analysis of the overlapping triplets and of a stratified sample of the non-overlapping triplets revealed accuracies of 93% and 80% for the respective categories (cumulative accuracy, 77%). We conclude that our process represents an important and broadly applicable improvement to the state of the art for curation of disease-gene-variant relationships.

Deep learning for extracting protein-protein interactions from biomedical literature

“…With the growing amount of biomedical information available in the textual form, there has been considerable interest in applying natural language processing (NLP) techniques and machine learning (ML) methods to the biomedical litera-ture (Huang and Lu, 2015;Singhal et al, 2016;. One of the most important tasks is to extract protein-protein interaction relations (Krallinger et al, 2008).…”

On expert curation and sustainability: UniProtKB/Swiss-Prot as a case study

“…PubTator has been widely used for text mining, data curation, and bioinformatics research (e.g. (Singhal, Simmons, et al, 2016; Wei, Leaman, et al, 2016)).…”

DeepPheno: Predicting single gene loss-of-function phenotypes using an ontology-aware hierarchical classifier

“…Several methods have been developed to automatically predict or generate genotype-phenotype associations. To predict phenotype associations, these methods use different sources such as literature (Kahanda et al, 2015;Collier et al, 2015;Singhal et al, 2016), functional annotations (Kulmanov et al, 2018;Kahanda et al, 2015;Dogan, 2018), proteinprotein interactions (Kahanda et al, 2015), expression profiles (Xu et al, 2009;Labuzzetta et al, 2016), genetic variations (Chen et al, 2014;Kahanda et al, 2015), or their combinations (Kahanda et al, 2015). The general idea behind most of these methods is to find genetic similarities, or interactions, and transfer phenotypes between genes based on the assumption that similar or interacting genes are involved in similar or related phenotypes (Gillis and Pavlidis, 2012).…”