Access Type

Open Access Thesis

Date of Award

January 2020

Degree Type


Degree Name



Computer Science

First Advisor

Sorin Draghici


In spite of the efforts in developing and maintaining accurate variant databases, a large number of disease-associated variants are still hidden in the biomedical literature. Curation of the biomedical literature in an effort to extract this information is a challenging task due to i) the complexity of natural language processing, ii) inconsistent use of standard recommendations for variant description, and iii) the lack of clarity and consistency in describing the variant-genotype-phenotype associations in the biomedical literature. In this article, we employ text mining and word cloud analysis techniques to address these challenges. The proposed framework extracts the variant-gene-disease associations from the full-length biomedical literature and designs an evidence-based variant-driven gene panel for a given condition. We validate the identified genes by showing their diagnostic abilities to predict the patients’ clinical outcomes on several independent validation cohorts. As representative examples, we present our results for acute myeloid leukemia (AML), breast cancer, and prostate cancer. We compare these panels with other variant-driven gene panels obtained from Clinvar, Mastermind, and others from literature, as well as with a panel identified with a classical differentially expressed genes (DEGs) approach. The results show that the panels obtained by the proposed framework yield better results than the other gene panels currently available in the literature.