{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,8]],"date-time":"2026-04-08T23:20:29Z","timestamp":1775690429406,"version":"3.50.1"},"reference-count":23,"publisher":"Oxford University Press (OUP)","issue":"4","license":[{"start":{"date-parts":[[2021,11,15]],"date-time":"2021-11-15T00:00:00Z","timestamp":1636934400000},"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":"National Natural Science Foundation of China [NSFC","doi-asserted-by":"crossref","award":["11871290"],"award-info":[{"award-number":["11871290"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China [NSFC","doi-asserted-by":"crossref","award":["61873185"],"award-info":[{"award-number":["61873185"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,1,27]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>Significant progress has been achieved in distance-based protein folding, due to improved prediction of inter-residue distance by deep learning. Many efforts are thus made to improve distance prediction in recent years. However, it remains unknown what is the best way of objectively assessing the accuracy of predicted distance.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>A total of 19 metrics were proposed to measure the accuracy of predicted distance. These metrics were discussed and compared quantitatively on three benchmark datasets, with distance and structure models predicted by the trRosetta pipeline. The experiments show that a few metrics, such as distance precision, have a high correlation with the model accuracy measure TM-score (Pearson\u2019s correlation coefficient &amp;gt;0.7). In addition, the metrics are applied to rank the distance prediction groups in CASP14. The ranking by our metrics coincides largely with the official version. These data suggest that the proposed metrics are effective for measuring distance prediction. We anticipate that this study paves the way for objectively monitoring the progress of inter-residue distance prediction. A web server and a standalone package are provided to implement the proposed metrics.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>http:\/\/yanglab.nankai.edu.cn\/APD.<\/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\/btab781","type":"journal-article","created":{"date-parts":[[2021,11,10]],"date-time":"2021-11-10T12:31:17Z","timestamp":1636547477000},"page":"962-969","source":"Crossref","is-referenced-by-count":9,"title":["Toward the assessment of predicted inter-residue distance"],"prefix":"10.1093","volume":"38","author":[{"given":"Zongyang","family":"Du","sequence":"first","affiliation":[{"name":"School of Mathematical Sciences, Nankai University , Tianjin 300071, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0303-6693","authenticated-orcid":false,"given":"Zhenling","family":"Peng","sequence":"additional","affiliation":[{"name":"Research Center for Mathematics and Interdisciplinary Sciences, Shandong University , Qingdao 266237, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2912-7737","authenticated-orcid":false,"given":"Jianyi","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Mathematical Sciences, Nankai University , Tianjin 300071, China"}]}],"member":"286","published-online":{"date-parts":[[2021,11,15]]},"reference":[{"key":"2023020108512108800_btab781-B1","doi-asserted-by":"crossref","first-page":"13374","DOI":"10.1038\/s41598-020-70181-0","article-title":"A fully open-source framework for deep learning protein real-valued distances","volume":"10","author":"Adhikari","year":"2020","journal-title":"Sci. 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