Conceptual Knowledge Discovery in Databases for Drug Combinations Predictions in Malignant Melanoma.
The worldwide incidence of melanoma is rising faster than any other cancer, and prognosis for patients with metastatic disease is poor. Current targeted therapies are limited in their durability and/or effect size in certain patient populations due to acquired mechanisms of resistance. Thus, the development of synergistic combinatorial treatment regimens holds great promise to improve patient outcomes. We have previously shown that a model for in-silico knowledge discovery, Translational Ontology-anchored Knowledge Discovery Engine (TOKEn), is able to generate valid relationships between bimolecular and clinical phenotypes. In this study, we have aggregated observational and canonical knowledge consisting of melanoma-related biomolecular entities and targeted therapeutics in a computationally tractable model. We demonstrate here that the explicit linkage of therapeutic modalities with biomolecular underpinnings of melanoma utilizing the TOKEn pipeline yield a set of informed relationships that have the potential to generate combination therapy strategies.
Department of Biomedical Informatics, The Ohio State University, Columbus, OH, USA.
Kelly
K
Regan
Department of Biomedical Informatics, The Ohio State University, Columbus, OH, USA.
Satyajeet
S
Raje
Department of Biomedical Informatics, The Ohio State University, Columbus, OH, USA.
Cartik
C
Saravanamuthu
Department of Biomedical Informatics, The Ohio State University, Columbus, OH, USA.
Philip R O
PR
Payne
D000971:Antineoplastic Combined Chemotherapy Protocols / Q000145:classification / Q000627:therapeutic use*; D016303:Clinical Pharmacy Information Systems / Q000458:organization & administration*; D057225:Data Mining / Q000379:methods; D062313:Databases, Pharmaceutical / Q000145:classification*; D020000:Decision Support Systems, Clinical / Q000458:organization & administration; D051188:Knowledge Bases; D000069550:Machine Learning; D008545:Melanoma / Q000145:classification / Q000188:drug therapy*; D009323:Natural Language Processing; D012878:Skin Neoplasms / Q000145:classification / Q000188:drug therapy*