{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,8]],"date-time":"2026-05-08T12:27:14Z","timestamp":1778243234880,"version":"3.51.4"},"reference-count":22,"publisher":"Oxford University Press (OUP)","issue":"16","license":[{"start":{"date-parts":[[2020,5,16]],"date-time":"2020-05-16T00:00:00Z","timestamp":1589587200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"DOI":"10.13039\/501100001809","name":"Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61871050"],"award-info":[{"award-number":["61871050"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020,8,15]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>The classification of high-throughput protein data based on mass spectrometry (MS) is of great practical significance in medical diagnosis. Generally, MS data are characterized by high dimension, which inevitably leads to prohibitive cost of computation. To solve this problem, one-bit compressed sensing (CS), which is an extreme case of quantized CS, has been employed on MS data to select important features with low dimension. Though enjoying remarkably reduction of computation complexity, the current one-bit CS method does not consider the unavoidable noise contained in MS dataset, and does not exploit the inherent structure of the underlying MS data.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>We propose two feature selection (FS) methods based on one-bit CS to deal with the noise and the underlying block-sparsity features, respectively. In the first method, the FS problem is modeled as a perturbed one-bit CS problem, where the perturbation represents the noise in MS data. By iterating between perturbation refinement and FS, this method selects the significant features from noisy data. The second method formulates the problem as a perturbed one-bit block CS problem and selects the features block by block. Such block extraction is due to the fact that the significant features in the first method usually cluster in groups. Experiments show that, the two proposed methods have better classification performance for real MS data when compared with the existing method, and the second one outperforms the first one.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>The source code of our methods is available at: https:\/\/github.com\/tianyan8023\/OBCS.<\/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\/btaa516","type":"journal-article","created":{"date-parts":[[2020,5,12]],"date-time":"2020-05-12T13:21:44Z","timestamp":1589289704000},"page":"4423-4431","source":"Crossref","is-referenced-by-count":8,"title":["Feature selection and classification of noisy proteomics mass spectrometry data based on one-bit perturbed compressed sensing"],"prefix":"10.1093","volume":"36","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0791-7690","authenticated-orcid":false,"given":"Wenbo","family":"Xu","sequence":"first","affiliation":[{"name":"Key Lab of Universal Wireless Communications, Ministry of Education, Beijing University of Posts and Telecommunications , Beijing 100876, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yan","family":"Tian","sequence":"additional","affiliation":[{"name":"Key Lab of Universal Wireless Communications, Ministry of Education, Beijing University of Posts and Telecommunications , Beijing 100876, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Siye","family":"Wang","sequence":"additional","affiliation":[{"name":"Key Lab of Universal Wireless Communications, Ministry of Education, Beijing University of Posts and Telecommunications , Beijing 100876, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yupeng","family":"Cui","sequence":"additional","affiliation":[{"name":"Key Lab of Universal Wireless Communications, Ministry of Education, Beijing University of Posts and Telecommunications , Beijing 100876, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2020,5,16]]},"reference":[{"key":"2023062213522588800_btaa516-B1","first-page":"2353","author":"Acharya","year":"2017"},{"key":"2023062213522588800_btaa516-B2","first-page":"816","author":"Afef","year":"2018"},{"key":"2023062213522588800_btaa516-B3","first-page":"0258","author":"Awedat","year":"2016"},{"key":"2023062213522588800_btaa516-B4","first-page":"080","author":"Awedat","year":"2017"},{"key":"2023062213522588800_btaa516-B5","doi-asserted-by":"crossref","first-page":"1215","DOI":"10.1093\/bioinformatics\/btx724","article-title":"Deep learning for tumor classification in imaging mass spectrometry","volume":"34","author":"Behrmann","year":"2018","journal-title":"Bioinformatics"},{"key":"2023062213522588800_btaa516-B6","doi-asserted-by":"crossref","first-page":"160","DOI":"10.1186\/s12859-017-1565-4","article-title":"Sparse proteomics analysis: a compressed sensing-based approach for feature selection and classification of high-dimensional proteomics mass spectrometry data","volume":"18","author":"Conrad","year":"2017","journal-title":"BMC Bioinformatics"},{"key":"2023062213522588800_btaa516-B7","first-page":"62","article-title":"One-bit compressed sensing recovery algorithm robust to perturbation","volume":"25","author":"Cui","year":"2018","journal-title":"J. China Univ. Posts Telecommun"},{"key":"2023062213522588800_btaa516-B8","doi-asserted-by":"crossref","first-page":"1300","DOI":"10.1049\/el.2018.5050","article-title":"Perturbed block orthogonal matching pursuit","volume":"54","author":"Cui","year":"2018","journal-title":"Electron. Lett"},{"key":"2023062213522588800_btaa516-B9","doi-asserted-by":"crossref","first-page":"1289","DOI":"10.1109\/TIT.2006.871582","article-title":"Compressed sensing","volume":"52","author":"Donoho","year":"2006","journal-title":"IEEE Trans. Inf. Theory"},{"key":"2023062213522588800_btaa516-B10","doi-asserted-by":"crossref","first-page":"2082","DOI":"10.1109\/TIT.2012.2234823","article-title":"Robust 1-bit compressive sensing via binary stable embeddings of sparse vectors","volume":"59","author":"Jacques","year":"2013","journal-title":"IEEE Intern. Symp. Inf. Theory"},{"key":"2023062213522588800_btaa516-B11","doi-asserted-by":"crossref","first-page":"2748","DOI":"10.1109\/TIT.2016.2527637","article-title":"One-bit compressive sensing with norm estimation","volume":"62","author":"Knudson","year":"2016","journal-title":"IEEE Trans. Inf. Theory"},{"key":"2023062213522588800_btaa516-B12","doi-asserted-by":"crossref","first-page":"i323","DOI":"10.1093\/bioinformatics\/bty252","article-title":"SIMPLE: sparse interaction model over peaks of moLEcules for fast, interpretable metabolite identification from tandem mass spectra","volume":"34","author":"Nguyen","year":"2018","journal-title":"Bioinformatics"},{"key":"2023062213522588800_btaa516-B13","doi-asserted-by":"crossref","first-page":"482","DOI":"10.1109\/TIT.2012.2207945","article-title":"Robust 1-bit compressed sensing and sparse logistic regression: a convex programming approach","volume":"59","author":"Plan","year":"2013","journal-title":"IEEE Trans. Inf. Theory"},{"key":"2023062213522588800_btaa516-B14","doi-asserted-by":"crossref","first-page":"2507","DOI":"10.1093\/bioinformatics\/btm344","article-title":"A review of feature selection techniques in bioinformatics","volume":"23","author":"Saeys","year":"2007","journal-title":"Bioinformatics"},{"key":"2023062213522588800_btaa516-B15","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1186\/s13634-016-0369-4","article-title":"One-bit compressive sampling via l0 minimization","volume":"2016","author":"Shen","year":"2016","journal-title":"EURASIP J. Adv. Signal Process"},{"key":"2023062213522588800_btaa516-B16","doi-asserted-by":"crossref","first-page":"1248","DOI":"10.1109\/ACCESS.2014.2359979","article-title":"Classification of proteomic MS data as Bayesian solution of an inverse problem","volume":"2","author":"Szacherski","year":"2014","journal-title":"IEEE Access"},{"key":"2023062213522588800_btaa516-B17","doi-asserted-by":"crossref","first-page":"164","DOI":"10.1007\/s11107-018-0813-5","article-title":"Compressively sensing nonadjacent block-sparse spectra via a block discrete chirp matrix","volume":"37","author":"Tian","year":"2019","journal-title":"Photon. Netw. Commun"},{"key":"2023062213522588800_btaa516-B18","doi-asserted-by":"crossref","first-page":"948","DOI":"10.1016\/j.acha.2018.02.002","article-title":"Sharp sufficient conditions for stable recovery of block sparse signals by block orthogonal matching pursuit","volume":"47","author":"Wen","year":"2019","journal-title":"Appl. Comput. Harmon. Anal"},{"key":"2023062213522588800_btaa516-B19","doi-asserted-by":"crossref","first-page":"1636","DOI":"10.1093\/bioinformatics\/btg210","article-title":"Comparison of statistical methods for classification of ovarian cancer using mass spectrometry data","volume":"19","author":"Wu","year":"2003","journal-title":"Bioinformatics"},{"key":"2023062213522588800_btaa516-B20","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1038\/nrc1043","article-title":"Proteomic applications for the early detection of cancer","volume":"3","author":"Wulfkuhle","year":"2003","journal-title":"Nat. Rev. Cancer"},{"key":"2023062213522588800_btaa516-B21","doi-asserted-by":"crossref","first-page":"3692","DOI":"10.1002\/pmic.200701121","article-title":"In situ proteomics with imaging mass spectrometry and principal component analysis in the Scrapper-knockout mouse brain","volume":"8","author":"Yao","year":"2008","journal-title":"Proteomics"},{"key":"2023062213522588800_btaa516-B22","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1049\/el.2017.4049","article-title":"Improved analysis of orthogonal matching pursuit in general perturbations","volume":"54","author":"Zhang","year":"2018","journal-title":"Electron. Lett"}],"container-title":["Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/academic.oup.com\/bioinformatics\/advance-article-pdf\/doi\/10.1093\/bioinformatics\/btaa516\/33773965\/btaa516.pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/36\/16\/4423\/50676379\/btaa516.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/36\/16\/4423\/50676379\/btaa516.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,6,23]],"date-time":"2023-06-23T10:30:26Z","timestamp":1687516226000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article\/36\/16\/4423\/5838182"}},"subtitle":[],"editor":[{"given":"Pier","family":"Luigi Martelli","sequence":"additional","affiliation":[],"role":[{"role":"editor","vocabulary":"crossref"}]}],"short-title":[],"issued":{"date-parts":[[2020,5,16]]},"references-count":22,"journal-issue":{"issue":"16","published-print":{"date-parts":[[2020,8,15]]}},"URL":"https:\/\/doi.org\/10.1093\/bioinformatics\/btaa516","relation":{},"ISSN":["1367-4803","1367-4811"],"issn-type":[{"value":"1367-4803","type":"print"},{"value":"1367-4811","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2020,8,15]]},"published":{"date-parts":[[2020,5,16]]}}}