{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,11]],"date-time":"2025-12-11T07:34:38Z","timestamp":1765438478289,"version":"3.37.3"},"reference-count":55,"publisher":"Oxford University Press (OUP)","issue":"22","license":[{"start":{"date-parts":[[2017,7,29]],"date-time":"2017-07-29T00:00:00Z","timestamp":1501286400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/about_us\/legal\/notices"}],"funder":[{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["NRF-2016R1D1A1B03934135"],"award-info":[{"award-number":["NRF-2016R1D1A1B03934135"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2017,11,15]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>Identification of genes that can be used to predict prognosis in patients with cancer is important in that it can lead to improved therapy, and can also promote our understanding of tumor progression on the molecular level. One of the common but fundamental problems that render identification of prognostic genes and prediction of cancer outcomes difficult is the heterogeneity of patient samples.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>To reduce the effect of sample heterogeneity, we clustered data samples using K-means algorithm and applied modified PageRank to functional interaction (FI) networks weighted using gene expression values of samples in each cluster. Hub genes among resulting prioritized genes were selected as biomarkers to predict the prognosis of samples. This process outperformed traditional feature selection methods as well as several network-based prognostic gene selection methods when applied to Random Forest. We were able to find many cluster-specific prognostic genes for each dataset. Functional study showed that distinct biological processes were enriched in each cluster, which seems to reflect different aspect of tumor progression or oncogenesis among distinct patient groups. Taken together, these results provide support for the hypothesis that our approach can effectively identify heterogeneous prognostic genes, and these are complementary to each other, improving prediction accuracy.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>https:\/\/github.com\/mathcom\/CPR<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Supplementary information<\/jats:title>\n                  <jats:p>Supplementary data are available at Bioinformatics online.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btx487","type":"journal-article","created":{"date-parts":[[2017,7,28]],"date-time":"2017-07-28T03:10:50Z","timestamp":1501211450000},"page":"3619-3626","source":"Crossref","is-referenced-by-count":34,"title":["Improved prediction of breast cancer outcome by identifying heterogeneous biomarkers"],"prefix":"10.1093","volume":"33","author":[{"given":"Jonghwan","family":"Choi","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, Incheon National University, Incheon, The Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sanghyun","family":"Park","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Yonsei University, Seoul, The Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Youngmi","family":"Yoon","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Gachon University, Seongnam-si, Gyeonggi-do, The Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jaegyoon","family":"Ahn","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Incheon National University, Incheon, The Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2017,7,29]]},"reference":[{"key":"2023051308275633800_btx487-B1","doi-asserted-by":"crossref","first-page":"103","DOI":"10.4137\/BMI.S5740","article-title":"Breast cancer biomarker discovery in the functional genomic age: a systematic review of 42 gene expression signatures","volume":"5","author":"Abba","year":"2010","journal-title":"Biomark. 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