{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,24]],"date-time":"2025-10-24T08:22:21Z","timestamp":1761294141709,"version":"3.37.3"},"reference-count":17,"publisher":"Oxford University Press (OUP)","issue":"19","license":[{"start":{"date-parts":[[2020,7,8]],"date-time":"2020-07-08T00:00:00Z","timestamp":1594166400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"name":"Shenzhen Matching Project","award":["GJHS20170314155751703"],"award-info":[{"award-number":["GJHS20170314155751703"]}]},{"name":"National Key Research and Develop Program of China","award":["2016YFC0105102"],"award-info":[{"award-number":["2016YFC0105102"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61871374"],"award-info":[{"award-number":["61871374"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Leading Talent of Special Support Project in Guangdong","award":["2016TX03R139"],"award-info":[{"award-number":["2016TX03R139"]}]},{"name":"Science Foundation of Guangdong","award":["2017B020229002","2015B02023301"],"award-info":[{"award-number":["2017B020229002","2015B02023301"]}]},{"name":"CAS Key Laboratory of Health Informatics"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020,12,8]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Summary<\/jats:title>\n                  <jats:p>Nowadays, it is feasible to collect massive features for quantitative representation and precision medicine, and thus, automatic ranking to figure out the most informative and discriminative ones becomes increasingly important. To address this issue, 42 feature ranking (FR) methods are integrated to form a MATLAB toolbox (matFR). The methods apply mutual information, statistical analysis, structure clustering and other principles to estimate the relative importance of features in specific measure spaces. Specifically, these methods are summarized, and an example shows how to apply a FR method to sort mammographic breast lesion features. The toolbox is easy to use and flexible to integrate additional methods. Importantly, it provides a tool to compare, investigate and interpret the features selected for various applications.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>The toolbox is freely available at http:\/\/github.com\/NicoYuCN\/matFR. A tutorial and an example with a dataset are provided.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btaa621","type":"journal-article","created":{"date-parts":[[2020,6,30]],"date-time":"2020-06-30T11:43:44Z","timestamp":1593517424000},"page":"4968-4969","source":"Crossref","is-referenced-by-count":14,"title":["matFR: a MATLAB toolbox for feature ranking"],"prefix":"10.1093","volume":"36","author":[{"given":"Zhicheng","family":"Zhang","sequence":"first","affiliation":[{"name":"Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences , Shenzhen, GD 518055, China"},{"name":"Department of Radiation Oncology, Stanford University , Stanford, CA 94305, USA"}]},{"given":"Xiaokun","family":"Liang","sequence":"additional","affiliation":[{"name":"Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences , Shenzhen, GD 518055, China"}]},{"given":"Wenjian","family":"Qin","sequence":"additional","affiliation":[{"name":"Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences , Shenzhen, GD 518055, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3412-2159","authenticated-orcid":false,"given":"Shaode","family":"Yu","sequence":"additional","affiliation":[{"name":"College of Information and Communication Engineering, Communication University of China , Beijing 100024, China"},{"name":"Key Laboratory of Convergent Media and Intelligent Technology (Communication University of China), Ministry of Education , Beijing 100024, China"},{"name":"Department of Radiation Oncology, University of Texas Southwestern Medical Center , Dallas, TX 75390, USA"}]},{"given":"Yaoqin","family":"Xie","sequence":"additional","affiliation":[{"name":"Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences , Shenzhen, GD 518055, China"}]}],"member":"286","published-online":{"date-parts":[[2020,7,8]]},"reference":[{"key":"2023062408052759100_btaa621-B1","doi-asserted-by":"crossref","first-page":"248","DOI":"10.1016\/j.cmpb.2015.12.014","article-title":"Representation learning for mammography mass lesion classification with convolutional neural networks","volume":"127","author":"Arevalo","year":"2016","journal-title":"Comput. 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