Non-Small Cell Lung Carcinoma (NSCLC) is the most common form of lung cancer and the third most common cancer overall with respect to numbers, and deaths (American Cancer Society, Cancer Facts and Figures, Feb 2019). Previous studies have shown that targeting a commonly mutated NSCLC gene EGFR with Tyrosine kinase inhibitor (TKI) drugs is successful in patients who have EGFR mutations. It is now being recognized that overall NSCLC patient outcomes can be further improved by targeting other commonly mutated NSCLC genes in addition to the widely studies and targeted EGFR. However, DNA sequencing outputs show multiple mutations or variations in genes, which are too many to interpret, study and target with drugs. To overcome this limitation, the goal of the present study was to build a NSCLC specific knowledge base that can be used to prioritize mutated genes from a given patient genetic profile and drugs to target these genes. Commonly mutated NSCLC genes reported in NSCLC cases were used as a ‘bait’ to ‘capture’ and create a computable NSCLC knowledge base using BEL (Biological Expression Language) statements. This NSCLC specific knowledge base consisted of 33 pathogenic variants, 129 significant gene functional annotations (GO, gene ontology terms) and 568 drugs BEL knowledge statements. Overlaying 17 different NSCLC patient genetic profiles against our NSCLC specific knowledge base, successfully computed and prioritized distinct genes harboring mutations, and drugs to target these genes in patients. Here we developed this computational pipeline for NSCLC and this methodology can be adapted for other diseases with known common gene mutations. Taken together, we provide a paradigm to prioritize and select drugs based on patient’s genetic profile for precision medicine based clinical decision support. Code Availability: Computational code with html or pdf rendering showing input and output of code chunks is available as a git local repository at https://icedrive.net/s/Ww9Y35fjuZu6akSkYa8xaChFQTG6 with all files and as a git remote repository at https://gitlab.com/smukher2/nsclc_drugtargetsmutations_nov2019 with large files ignored or removed. Source: This electronic version of my Masters (MS) thesis is a copy of the same available in Arizona State University, U.S. Library.