{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,21]],"date-time":"2025-10-21T14:53:40Z","timestamp":1761058420720,"version":"3.32.0"},"reference-count":16,"publisher":"Oxford University Press (OUP)","issue":"12","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2006,6,15]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Motivation: There is a well-recognized potential of protein expression profiling using the surface-enhanced laser desorption and ionization technology for discovering biomarkers that can be applied in clinical diagnosis, prognosis and therapy prediction. The pre-processing of the raw data, however, is still problematic.<\/jats:p><jats:p>Methods: We focus on the peak detection step, where the standard method is marked by poor specificity. Currently, scientists need to inspect individual spectra visually and laboriously in order to verify that spectral peaks identified by the standard method are real. Motivated by this multi-spectral process, we investigate an analytical approach\u2014called RS for \u2018regions of significance\u2019\u2014that reduces the data to a single spectrum of F-statistics capturing significant variability between spectra. To account for multiple testing, we use a false discovery rate criterion for identifying potentially interesting proteins.<\/jats:p><jats:p>Results: We show that RS has better operating characteristics than several existing methods and demonstrate routine applications on a number of large datasets.<\/jats:p><jats:p>Availability: RS is implemented in an R package called ProSpect which is available at<\/jats:p><jats:p>Contact: \u00a0yudi.pawitan@ki.se<\/jats:p><jats:p>Supplementary information: Supplementary data are available at Bioinformatics online.<\/jats:p>","DOI":"10.1093\/bioinformatics\/btl106","type":"journal-article","created":{"date-parts":[[2006,3,28]],"date-time":"2006-03-28T01:24:35Z","timestamp":1143509075000},"page":"1515-1523","source":"Crossref","is-referenced-by-count":29,"title":["Finding regions of significance in SELDI measurements for identifying protein biomarkers"],"prefix":"10.1093","volume":"22","author":[{"given":"Chuen Seng","family":"Tan","sequence":"first","affiliation":[{"name":"Department of Medical Epidemiology and Biostatistics, Karolinska Institutet 1 \u00a0 1 \u00a0 \u00a0 Stockholm, Sweden"},{"name":"Center for Molecular Epidemiology, Yong Loo Lin School of Medicine, National University of Singapore and Genome Institute of Singapore 3 \u00a0 3 \u00a0 \u00a0 Singapore"}]},{"given":"Alexander","family":"Ploner","sequence":"additional","affiliation":[{"name":"Department of Medical Epidemiology and Biostatistics, Karolinska Institutet 1 \u00a0 1 \u00a0 \u00a0 Stockholm, Sweden"}]},{"given":"Andreas","family":"Quandt","sequence":"additional","affiliation":[{"name":"Department of Medical Epidemiology and Biostatistics, Karolinska Institutet 1 \u00a0 1 \u00a0 \u00a0 Stockholm, Sweden"}]},{"given":"Janne","family":"Lehti\u00f6","sequence":"additional","affiliation":[{"name":"Cancer Centrum Karolinska, Karolinska Institutet 2 \u00a0 2 \u00a0 \u00a0 Stockholm, Sweden"},{"name":"Clinical Proteomics, Karolinska Biomics Center, Karolinska University Hospital 4 \u00a0 4 \u00a0 \u00a0 Stockholm, Sweden"}]},{"given":"Yudi","family":"Pawitan","sequence":"additional","affiliation":[{"name":"Department of Medical Epidemiology and Biostatistics, Karolinska Institutet 1 \u00a0 1 \u00a0 \u00a0 Stockholm, Sweden"}]}],"member":"286","published-online":{"date-parts":[[2006,3,27]]},"reference":[{"key":"2023012408410219600_b1","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1111\/j.2517-6161.1995.tb02031.x","article-title":"Controlling the false discovery rate: a practical and powerful approach to multiple testing","volume":"57","author":"Benjamini","year":"1995","journal-title":"J. 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