{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,12]],"date-time":"2026-03-12T08:49:31Z","timestamp":1773305371974,"version":"3.50.1"},"reference-count":40,"publisher":"Oxford University Press (OUP)","issue":"Supplement_1","license":[{"start":{"date-parts":[[2023,6,30]],"date-time":"2023-06-30T00:00:00Z","timestamp":1688083200000},"content-version":"vor","delay-in-days":29,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["F31LM014194"],"award-info":[{"award-number":["F31LM014194"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["T32EB009403"],"award-info":[{"award-number":["T32EB009403"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["OT2OD026682"],"award-info":[{"award-number":["OT2OD026682"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["1U54AG075931"],"award-info":[{"award-number":["1U54AG075931"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["1U24CA268108"],"award-info":[{"award-number":["1U24CA268108"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["CBET-2134999"],"award-info":[{"award-number":["CBET-2134999"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,6,30]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>Spatial proteomics data have been used to map cell states and improve our understanding of tissue organization. More recently, these methods have been extended to study the impact of such organization on disease progression and patient survival. However, to date, the majority of supervised learning methods utilizing these data types did not take full advantage of the spatial information, impacting their performance and utilization.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>Taking inspiration from ecology and epidemiology, we developed novel spatial feature extraction methods for use with spatial proteomics data. We used these features to learn prediction models for cancer patient survival. As we show, using the spatial features led to consistent improvement over prior methods that used the spatial proteomics data for the same task. In addition, feature importance analysis revealed new insights about the cell interactions that contribute to patient survival.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>The code for this work can be found at gitlab.com\/enable-medicine-public\/spatsurv.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btad245","type":"journal-article","created":{"date-parts":[[2023,6,30]],"date-time":"2023-06-30T08:18:38Z","timestamp":1688113118000},"page":"i140-i148","source":"Crossref","is-referenced-by-count":15,"title":["Deriving spatial features from <i>in situ<\/i> proteomics imaging to enhance cancer survival analysis"],"prefix":"10.1093","volume":"39","author":[{"given":"Monica T","family":"Dayao","sequence":"first","affiliation":[{"name":"Joint Carnegie Mellon University\u2014University of Pittsburgh Ph.D. Program in Computational Biology , Pittsburgh, PA 15213, United States"},{"name":"Computational Biology Department, Carnegie Mellon University , Pittsburgh, PA 15213, United States"}]},{"given":"Alexandro","family":"Trevino","sequence":"additional","affiliation":[{"name":"Enable Medicine , Menlo Park, CA 94025, United States"}]},{"given":"Honesty","family":"Kim","sequence":"additional","affiliation":[{"name":"Enable Medicine , Menlo Park, CA 94025, United States"}]},{"given":"Matthew","family":"Ruffalo","sequence":"additional","affiliation":[{"name":"Computational Biology Department, Carnegie Mellon University , Pittsburgh, PA 15213, United States"}]},{"given":"H Blaize","family":"D\u2019Angio","sequence":"additional","affiliation":[{"name":"Enable Medicine , Menlo Park, CA 94025, United States"}]},{"given":"Ryan","family":"Preska","sequence":"additional","affiliation":[{"name":"Enable Medicine , Menlo Park, CA 94025, United States"}]},{"given":"Umamaheswar","family":"Duvvuri","sequence":"additional","affiliation":[{"name":"Department of Otolaryngology, University of Pittsburgh , Pittsburgh, PA 15213, United States"}]},{"given":"Aaron T","family":"Mayer","sequence":"additional","affiliation":[{"name":"Enable Medicine , Menlo Park, CA 94025, United States"}]},{"given":"Ziv","family":"Bar-Joseph","sequence":"additional","affiliation":[{"name":"Computational Biology Department, Carnegie Mellon University , Pittsburgh, PA 15213, United States"},{"name":"Machine Learning Department, Carnegie Mellon University , Pittsburgh, PA 15213, United States"}]}],"member":"286","published-online":{"date-parts":[[2023,6,30]]},"reference":[{"key":"2023063008160199300_btad245-B1","doi-asserted-by":"crossref","first-page":"1340","DOI":"10.1093\/bioinformatics\/btq134","article-title":"Permutation importance: a corrected feature importance 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