{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,10]],"date-time":"2026-10-10T06:20:12Z","timestamp":1791613212272,"version":"4.3.4"},"reference-count":26,"publisher":"Wiley","issue":"2","license":[{"start":{"date-parts":[[2025,1,8]],"date-time":"2025-01-08T00:00:00Z","timestamp":1736294400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"},{"start":{"date-parts":[[2025,1,8]],"date-time":"2025-01-08T00:00:00Z","timestamp":1736294400000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["alz-journals.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Alzheimer's &amp;amp; Dementia"],"published-print":{"date-parts":[[2025,2]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>INTRODUCTION<\/jats:title>\n                    <jats:p>The importance of protein amyloidogenesis, associated with various diseases and functional roles, has driven the creation of computational predictors of amyloidogenicity. The accuracy of these predictors, particularly those utilizing artificial intelligence technologies, heavily depends on the quality of the data.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>METHODS<\/jats:title>\n                    <jats:p>We built Cross\u2010Beta DB, a database containing high\u2010quality data on known cross\u2010\u03b2 amyloids formed under natural conditions. We used it to train and benchmark several machine\u2010learning (ML) algorithms to predict amyloid\u2010forming potential of proteins.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>RESULTS<\/jats:title>\n                    <jats:p>We developed the Cross\u2010Beta predictor using an Extra trees ML algorithm, which outperforms other amyloid predictors with the highest F1 score (0.852) and accuracy (0.844) compared to existing methods.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>DISCUSSION<\/jats:title>\n                    <jats:p>The development of the Cross\u2010Beta DB database and a new ML\u2010based Cross\u2010Beta predictor may enable the creation of personalized risk profiles for neurodegenerative diseases and other amyloidoses\u2014especially as genome sequencing becomes more affordable.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Highlights<\/jats:title>\n                    <jats:p>\n                      <jats:list list-type=\"bullet\">\n                        <jats:list-item>\n                          <jats:p>Accuracy of ML\u2010based predictors depends on the quality of training data<\/jats:p>\n                        <\/jats:list-item>\n                        <jats:list-item>\n                          <jats:p>We built Cross\u2010Beta DB, a database of high\u2010quality data on naturally\u2010occurring amyloids<\/jats:p>\n                        <\/jats:list-item>\n                        <jats:list-item>\n                          <jats:p>Using this data, we developed an amyloid predictor that outperforms other predictors<\/jats:p>\n                        <\/jats:list-item>\n                        <jats:list-item>\n                          <jats:p>This computational tool enables the creation of risk profiles for neurodegenerative diseases<\/jats:p>\n                        <\/jats:list-item>\n                      <\/jats:list>\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/alz.14510","type":"journal-article","created":{"date-parts":[[2025,1,8]],"date-time":"2025-01-08T09:38:18Z","timestamp":1736329098000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Developing machine\u2010learning\u2010based amyloidogenicity predictors with Cross\u2010Beta DB"],"prefix":"10.1002","volume":"21","author":[{"given":"Valentin","family":"Gonay","sequence":"first","affiliation":[{"name":"CRBM UMR 5237 CNRS Universit\u00e9 Montpellier Montpellier France"},{"name":"PROTERA SAS Paris France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michael P.","family":"Dunne","sequence":"additional","affiliation":[{"name":"PROTERA SAS Paris France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Javier","family":"Caceres\u2010Delpiano","sequence":"additional","affiliation":[{"name":"PROTERA (GEAEnzymes SpA) Santiago Chile"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andrey V.","family":"Kajava","sequence":"additional","affiliation":[{"name":"CRBM UMR 5237 CNRS Universit\u00e9 Montpellier Montpellier France"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2025,1,8]]},"reference":[{"key":"e_1_2_8_2_1","doi-asserted-by":"publisher","DOI":"10.1201\/9780429258763"},{"key":"e_1_2_8_3_1","doi-asserted-by":"publisher","DOI":"10.1146\/annurev.med.57.121304.131243"},{"key":"e_1_2_8_4_1","doi-asserted-by":"publisher","DOI":"10.1101\/cshperspect.a033860"},{"key":"e_1_2_8_5_1","doi-asserted-by":"publisher","DOI":"10.3390\/ai5010003"},{"key":"e_1_2_8_6_1","doi-asserted-by":"publisher","DOI":"10.1038\/nbt1012"},{"key":"e_1_2_8_7_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41598\u2010017\u201013210\u20109"},{"key":"e_1_2_8_8_1","doi-asserted-by":"publisher","DOI":"10.1080\/13506129.2020.1715363"},{"key":"e_1_2_8_9_1","doi-asserted-by":"publisher","DOI":"10.1093\/nar\/28.1.235"},{"key":"e_1_2_8_10_1","doi-asserted-by":"publisher","DOI":"10.1093\/nar\/gkac1052"},{"key":"e_1_2_8_11_1","doi-asserted-by":"publisher","DOI":"10.1016\/s0022\u20102836(05)80360\u20102"},{"key":"e_1_2_8_12_1","doi-asserted-by":"publisher","DOI":"10.1093\/nar\/gkx950"},{"key":"e_1_2_8_13_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jalz.2014.06.007"},{"key":"e_1_2_8_14_1","doi-asserted-by":"publisher","DOI":"10.1093\/nar\/gkab408"},{"key":"e_1_2_8_15_1","doi-asserted-by":"publisher","DOI":"10.1126\/science.ade2574"},{"key":"e_1_2_8_16_1","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/bts565"},{"key":"e_1_2_8_17_1","doi-asserted-by":"publisher","DOI":"10.1093\/nar\/gkab1082"},{"key":"e_1_2_8_18_1","doi-asserted-by":"publisher","DOI":"10.1007\/s13042\u2010010\u20100001\u20100"},{"key":"e_1_2_8_19_1","doi-asserted-by":"publisher","DOI":"10.1146\/annurev.bi.59.070190.005043"},{"key":"e_1_2_8_20_1","doi-asserted-by":"publisher","DOI":"10.5555\/1953048.2078195"},{"key":"e_1_2_8_21_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jsb.2022.107840"},{"key":"e_1_2_8_22_1","doi-asserted-by":"publisher","DOI":"10.1093\/nar\/gku399"},{"key":"e_1_2_8_23_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41598\u2010022\u201011897\u2010z"},{"key":"e_1_2_8_24_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41580\u2010018\u20100060\u20108"},{"key":"e_1_2_8_25_1","doi-asserted-by":"publisher","DOI":"10.1096\/fj.09\u2010145979"},{"key":"e_1_2_8_26_1","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.0505905102"},{"key":"e_1_2_8_27_1","doi-asserted-by":"publisher","DOI":"10.1110\/ps.036368.108"}],"container-title":["Alzheimer's 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