Nicole’s research focuses on the development and application of semantic technologies to facilitate new knowledge discovery and promote scientific reproducibility. She develops biomedical ontologies and data standards for phenotypes, diseases, and other biomedical domains with the goal of improving disease diagnostics and health outcomes. Nicole has extensive expertise in working collaboratively on dynamic and dispersed teams and has contributed to several consortia and projects.
She has made significant contributions to science in the area of scientific reproducibility including demonstrating a lack of uniquely identifying information in methods sections in
publications as well as an evaluation of current journal data sharing policies. With regard to clinical and translational impact, she has contributed to layperson accessible phenotyping standards and clinical phenotyping and disease standards for use in diagnosis through her work on the Monarch Initiative.
Nicole Vasilevskya, Nico Matentzoglub, Sabrina Toroa, Joe Flackc, Ada Hamoshc, Peter Robinsond, Melissa Haendela, Chris Mungalle
aUniversity of Colorado Anschutz Medical Campus, Aurora, CO, USA; bSemanticly Ltd., Athens, Greece; cJohns Hopkins University, Baltimore, MD, USA; dJackson Laboratories, Farmington, CT, USA; eLawrence Berkeley National Laboratory, Berkeley, CA, USA
Biomedical ontologies provide a standard computable representation of knowledge that can be used to integrate and navigate large amounts of heterogeneous data for downstream computational analysis and knowledge discovery. The Mondo Disease Ontology (Mondo) is a semantic resource that integrates several existing disease terminologies, provides precise, curated semantic mappings between them, and unifies them into one coherent classification of diseases. Cancer concepts in Mondo are formally classified into a hierarchical representation, which can be used to annotate data at different levels of granularity. Neoplasm is the top-level class and subtypes (subclasses) are largely aligned with the NCIt neoplasm branch, with malignant neoplasms (cancers) being classified as subtypes. Classes are grouped in multiple ways, such as by anatomical entities affected (such as cardiovascular or digestive system neoplasms), by malignancy (benign, pre-malignant, or malignant, or onset (childhood neoplasm). In addition, Mondo has representations of susceptibilities to cancers and hereditary neoplastic syndromes such as Lynch Syndrome and Li-Fraumeni syndrome. Mondo provides mappings to other disease resources such as the National Cancer Institute Thesaurus (NCIt), as well as cancer terminology from Orphanet, OMIM, Disease Ontology, and others. Mondo precisely annotates each mapping using strict semantics, so that we know when two diseases are precisely equivalent or merely closely related. Mondo is iteratively developed and revised and we invite the community to contribute to Mondo; visit github.com/monarch-initiative/mondo for details.