{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,12]],"date-time":"2026-06-12T14:02:23Z","timestamp":1781272943485,"version":"3.54.1"},"reference-count":22,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,6,12]],"date-time":"2026-06-12T00:00:00Z","timestamp":1781222400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,6,12]],"date-time":"2026-06-12T00:00:00Z","timestamp":1781222400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"Advanced Computing and Key Software (Xinchuang) Haihe Laboratory","award":["XCHC-20240901"],"award-info":[{"award-number":["XCHC-20240901"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Mamm Genome"],"published-print":{"date-parts":[[2026,12]]},"DOI":"10.1007\/s00335-026-10249-w","type":"journal-article","created":{"date-parts":[[2026,6,12]],"date-time":"2026-06-12T13:43:58Z","timestamp":1781271838000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A novel biological function-based method for mining core genes in rare disease with limited cases"],"prefix":"10.1007","volume":"37","author":[{"given":"Yongqiang","family":"Gong","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kailong","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xicheng","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xi","family":"Peng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yin","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,6,12]]},"reference":[{"key":"10249_CR1","doi-asserted-by":"publisher","first-page":"803","DOI":"10.1038\/s41592-023-01886-z","volume":"20","author":"J Banerjee","year":"2023","unstructured":"Banerjee J, Taroni JN, Allaway RJ, Prasad DV, Guinney J, Greene C (2023) Machine learning in rare disease. Nat Methods 20:803\u2013814. https:\/\/doi.org\/10.1038\/s41592-023-01886-z","journal-title":"Nat Methods"},{"key":"10249_CR2","doi-asserted-by":"publisher","first-page":"1028545","DOI":"10.3389\/fpubh.2022.1028545","volume":"10","author":"CCY Chung","year":"2022","unstructured":"Chung CCY, Project HKG, Chu ATW, Chung BHY (2022) Rare disease emerging as a global public health priority. Front Public Health 10:1028545. https:\/\/doi.org\/10.3389\/fpubh.2022.1028545","journal-title":"Front Public Health"},{"key":"10249_CR3","doi-asserted-by":"publisher","first-page":"2938","DOI":"10.1093\/bioinformatics\/btx364","volume":"33","author":"JR Conway","year":"2017","unstructured":"Conway JR, Lex A, Gehlenborg N (2017) UpSetR: an R package for the visualization of intersecting sets and their properties. Bioinformatics 33:2938\u20132940. https:\/\/doi.org\/10.1093\/bioinformatics\/btx364","journal-title":"Bioinformatics"},{"key":"10249_CR4","doi-asserted-by":"publisher","DOI":"10.3389\/fmed.2021.747612","author":"S Decherchi","year":"2021","unstructured":"Decherchi S, Pedrini E, Mordenti M, Cavalli A, Sangiorgi L (2021) Opportunities and challenges for machine learning in rare diseases. Front Med. https:\/\/doi.org\/10.3389\/fmed.2021.747612","journal-title":"Front Med"},{"key":"10249_CR5","doi-asserted-by":"publisher","DOI":"10.1007\/s10142-025-01529-w","author":"DB Dongare","year":"2025","unstructured":"Dongare DB, Nishad SS, Mastoli SY, Saraf SA, Srivastava N, Dey A (2025) High-throughput sequencing: a breakthrough in molecular diagnosis for precision medicine. Funct Integr Genom. https:\/\/doi.org\/10.1007\/s10142-025-01529-w","journal-title":"Funct Integr Genom"},{"key":"10249_CR6","doi-asserted-by":"publisher","first-page":"885","DOI":"10.1002\/ajmg.a.61124","volume":"179","author":"CR Ferreira","year":"2019","unstructured":"Ferreira CR (2019) The burden of rare diseases. Am J Med Genet A 179:885\u2013892. https:\/\/doi.org\/10.1002\/ajmg.a.61124","journal-title":"Am J Med Genet A"},{"key":"10249_CR7","doi-asserted-by":"publisher","first-page":"e43","DOI":"10.1002\/imt2.43","volume":"1","author":"Z Gu","year":"2022","unstructured":"Gu Z (2022) Complex heatmap visualization. Imeta 1:e43. https:\/\/doi.org\/10.1002\/imt2.43","journal-title":"Imeta"},{"key":"10249_CR8","doi-asserted-by":"publisher","first-page":"2847","DOI":"10.1093\/bioinformatics\/btw313","volume":"32","author":"Z Gu","year":"2016","unstructured":"Gu Z, Eils R, Schlesner M (2016) Complex heatmaps reveal patterns and correlations in multidimensional genomic data. Bioinformatics 32:2847\u20132849. https:\/\/doi.org\/10.1093\/bioinformatics\/btw313","journal-title":"Bioinformatics"},{"issue":"1","key":"10249_CR9","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1007\/s10142-022-00949-2","volume":"23","author":"C Guo","year":"2022","unstructured":"Guo C, Xie B, Liu Q (2022) Weighted gene co-expression network analysis combined with machine learning validation to identify key hub biomarkers in colorectal cancer. Funct Integr Genomics 23:24. https:\/\/doi.org\/10.1007\/s10142-022-00949-2","journal-title":"Funct Integr Genomics"},{"key":"10249_CR10","doi-asserted-by":"publisher","first-page":"28","DOI":"10.1186\/s44342-024-00032-1","volume":"22","author":"J Hong","year":"2024","unstructured":"Hong J, Lee D, Hwang A, Kim T, Ryu H-Y, Choi J (2024) Rare disease genomics and precision medicine. Genomics Inf 22:28. https:\/\/doi.org\/10.1186\/s44342-024-00032-1","journal-title":"Genomics Inf"},{"key":"10249_CR11","doi-asserted-by":"publisher","first-page":"661","DOI":"10.1055\/s-0039-1688446","volume":"45","author":"KR Kumar","year":"2019","unstructured":"Kumar KR, Cowley MJ, Davis RL (2019) Next-generation sequencing and emerging technologies. Semin Thromb Hemost 45:661\u2013673. https:\/\/doi.org\/10.1055\/s-0039-1688446","journal-title":"Semin Thromb Hemost"},{"key":"10249_CR12","doi-asserted-by":"publisher","first-page":"559","DOI":"10.1186\/1471-2105-9-559","volume":"9","author":"P Langfelder","year":"2008","unstructured":"Langfelder P, Horvath S (2008) WGCNA: an R package for weighted correlation network analysis. BMC Bioinf 9:559. https:\/\/doi.org\/10.1186\/1471-2105-9-559","journal-title":"BMC Bioinf"},{"key":"10249_CR13","doi-asserted-by":"publisher","first-page":"882","DOI":"10.1093\/bioinformatics\/bts034","volume":"28","author":"JT Leek","year":"2012","unstructured":"Leek JT, Johnson WE, Parker HS, Jaffe AE, Storey JD (2012) The sva package for removing batch effects and other unwanted variation in high-throughput experiments. Bioinformatics 28:882\u2013883. https:\/\/doi.org\/10.1093\/bioinformatics\/bts034","journal-title":"Bioinformatics"},{"key":"10249_CR14","doi-asserted-by":"publisher","first-page":"396","DOI":"10.1360\/03yc0127","volume":"47","author":"X Li","year":"2004","unstructured":"Li X, Rao S, Zhang T, Guo Z, Zhang Q, Moser KL et al (2004) An ensemble method for gene discovery based on DNA microarray data. Sci China C Life Sci 47:396\u2013405. https:\/\/doi.org\/10.1360\/03yc0127","journal-title":"Sci China C Life Sci"},{"key":"10249_CR15","doi-asserted-by":"publisher","first-page":"550","DOI":"10.1186\/s13059-014-0550-8","volume":"15","author":"MI Love","year":"2014","unstructured":"Love MI, Huber W, Anders S (2014) Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol 15:550. https:\/\/doi.org\/10.1186\/s13059-014-0550-8","journal-title":"Genome Biol"},{"key":"10249_CR16","doi-asserted-by":"publisher","first-page":"w20057","DOI":"10.4414\/smw.2019.20057","volume":"149","author":"B Mery","year":"2019","unstructured":"Mery B, Vallard A, Rowinski E, Magne N (2019) High-throughput sequencing in clinical oncology: from past to present. Swiss Med Wkly 149:w20057. https:\/\/doi.org\/10.4414\/smw.2019.20057","journal-title":"Swiss Med Wkly"},{"key":"10249_CR17","doi-asserted-by":"publisher","first-page":"640","DOI":"10.1038\/msb.2012.61","volume":"9","author":"WW Soon","year":"2013","unstructured":"Soon WW, Hariharan M, Snyder MP (2013) High-throughput sequencing for biology and medicine. Mol Syst Biol 9:640. https:\/\/doi.org\/10.1038\/msb.2012.61","journal-title":"Mol Syst Biol"},{"key":"10249_CR18","doi-asserted-by":"publisher","first-page":"bbab363","DOI":"10.1093\/bib\/bbab363","volume":"23","author":"U-KI Umlai","year":"2022","unstructured":"Umlai U-KI, Bangarusamy DK, Estivill X, Jithesh PV (2022) Genome sequencing data analysis for rare disease gene discovery. Brief Bioinf 23:bbab363. https:\/\/doi.org\/10.1093\/bib\/bbab363","journal-title":"Brief Bioinf"},{"key":"10249_CR19","doi-asserted-by":"publisher","first-page":"24786","DOI":"10.1038\/s41598-024-75438-6","volume":"14","author":"A Vaziri-Moghadam","year":"2024","unstructured":"Vaziri-Moghadam A, Foroughmand-Araabi M-H (2024) Integrating machine learning and bioinformatics approaches for identifying novel diagnostic gene biomarkers in colorectal cancer. Sci Rep 14:24786. https:\/\/doi.org\/10.1038\/s41598-024-75438-6","journal-title":"Sci Rep"},{"key":"10249_CR20","doi-asserted-by":"publisher","first-page":"100141","DOI":"10.1016\/j.xinn.2021.100141","volume":"2","author":"T Wu","year":"2021","unstructured":"Wu T, Hu E, Xu S, Chen M, Guo P, Dai Z et al (2021) clusterProfiler 4.0: a universal enrichment tool for interpreting omics data. Innov (Camb) 2:100141. https:\/\/doi.org\/10.1016\/j.xinn.2021.100141","journal-title":"Innov (Camb)"},{"key":"10249_CR21","doi-asserted-by":"publisher","first-page":"284","DOI":"10.1089\/omi.2011.0118","volume":"16","author":"G Yu","year":"2012","unstructured":"Yu G, Wang L-G, Han Y, He Q-Y (2012) clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS 16:284\u2013287. https:\/\/doi.org\/10.1089\/omi.2011.0118","journal-title":"OMICS"},{"key":"10249_CR22","doi-asserted-by":"publisher","DOI":"10.1186\/s12859-019-3078-9","author":"H Zhang","year":"2019","unstructured":"Zhang H, Huo X, Guo X, Su X, Quan X, Jin C (2019) A disease-related gene mining method based on weakly supervised learning model. BMC Bioinf. https:\/\/doi.org\/10.1186\/s12859-019-3078-9","journal-title":"BMC Bioinf"}],"container-title":["Mammalian Genome"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00335-026-10249-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00335-026-10249-w","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00335-026-10249-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,12]],"date-time":"2026-06-12T13:43:59Z","timestamp":1781271839000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00335-026-10249-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6,12]]},"references-count":22,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2026,12]]}},"alternative-id":["10249"],"URL":"https:\/\/doi.org\/10.1007\/s00335-026-10249-w","relation":{},"ISSN":["0938-8990","1432-1777"],"issn-type":[{"value":"0938-8990","type":"print"},{"value":"1432-1777","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,6,12]]},"assertion":[{"value":"23 March 2026","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 June 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 June 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"The study was approved by the Tianjin Union Medical Ethics Committee (IRB: 2023 - C04) and carried out according to the Helsinki Declaration. Participants signed an informed consent form prior to the start of the study.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}}],"article-number":"77"}}