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However, most of the identified gene mutations are not clinically utilized. It remains uncertain whether the presence of a certain genetic alteration will affect treatment response. Conventional statistics have limitations for causal inferences and are hard to gain sufficient power in genomic datasets. Here, we developed and evaluated a C-search algorithm for searching the causal genes that maximize the effect of the treatment.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>The algorithm was developed based on the potential outcome framework and Bayesian posterior update. The precision of the algorithm was validated using a simulation dataset. The algorithm was implemented to a cBioPortal dataset. The genes discovered by the algorithm were externally validated within CancerSCAN screening data from Samsung Medical Center.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Simulation data analysis showed that the C-search algorithm was able to identify nine causal genes out of ten. The C-search algorithm shows the discovery rate rapidly increasing until the 1500 data instances. Meanwhile, the log-rank test shows a slower increase in performance. The C-search algorithm was able to suggest nine causal genes from the cBioPortal Metabric dataset. Treating the patients with the causal genes is associated with better survival outcome in both the cBioPortal dataset and the CancerSCAN dataset which is used for external validation.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Our C-search algorithm demonstrated better performance to identify causal effects of the genes than multiple log-rank test analysis especially within a limited number of data. The result suggests that the C-search can discover the causal genes from various genetic datasets, where the number of samples is limited compared to the number of variables.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1186\/s12911-022-01852-3","type":"journal-article","created":{"date-parts":[[2022,4,27]],"date-time":"2022-04-27T13:03:01Z","timestamp":1651064581000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Discovery of genes positively modulating treatment effect using potential outcome framework and Bayesian update"],"prefix":"10.1186","volume":"22","author":[{"given":"Youngkeun","family":"Lee","sequence":"first","affiliation":[]},{"given":"Jisoo","family":"Kim","sequence":"additional","affiliation":[]},{"given":"Sung Wook","family":"Seo","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,4,27]]},"reference":[{"issue":"9","key":"1852_CR1","doi-asserted-by":"publisher","first-page":"551","DOI":"10.1038\/gt.2017.77","volume":"24","author":"F Carrasco-Ramiro","year":"2017","unstructured":"Carrasco-Ramiro F, Peir\u00f3-Pastor R, Aguado B. 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It's impossible to re-identify the data because it's completely anonymized. As a result, the Samsung Medical Center Institutional Review Boards (IRB) ethical approval was waived, and informed consent to participate was not expected in this study. The usage of the CancerSCAN dataset was approved by the institutional review boards of the participating institutions (Samsung Medical Center 2019-11-127).","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"113"}}