{"title":"深度学习:塑造未来的医学。","authors":"Konstantinos Vougas, Spyridon Almpanis, Vassilis Gorgoulis","doi":"10.1080/23723556.2020.1723462","DOIUrl":null,"url":null,"abstract":"<p><p>Predicting response to therapy is a major challenge in medicine. Machine learning algorithms are promising tools for assisting this aim. Amongst them, Deep Neural Networks are emerging as the most capable of interrogating across multiple data types. Their further development will lead to sophisticated knowledge extraction, shaping the medicine of tomorrow.</p>","PeriodicalId":520710,"journal":{"name":"Molecular & cellular oncology","volume":" ","pages":"1723462"},"PeriodicalIF":0.0000,"publicationDate":"2020-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/23723556.2020.1723462","citationCount":"2","resultStr":"{\"title\":\"Deep learning: shaping the medicine of tomorrow.\",\"authors\":\"Konstantinos Vougas, Spyridon Almpanis, Vassilis Gorgoulis\",\"doi\":\"10.1080/23723556.2020.1723462\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Predicting response to therapy is a major challenge in medicine. Machine learning algorithms are promising tools for assisting this aim. Amongst them, Deep Neural Networks are emerging as the most capable of interrogating across multiple data types. Their further development will lead to sophisticated knowledge extraction, shaping the medicine of tomorrow.</p>\",\"PeriodicalId\":520710,\"journal\":{\"name\":\"Molecular & cellular oncology\",\"volume\":\" \",\"pages\":\"1723462\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-02-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://sci-hub-pdf.com/10.1080/23723556.2020.1723462\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Molecular & cellular oncology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1080/23723556.2020.1723462\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2020/1/1 0:00:00\",\"PubModel\":\"eCollection\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Molecular & cellular oncology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1080/23723556.2020.1723462","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2020/1/1 0:00:00","PubModel":"eCollection","JCR":"","JCRName":"","Score":null,"Total":0}
Predicting response to therapy is a major challenge in medicine. Machine learning algorithms are promising tools for assisting this aim. Amongst them, Deep Neural Networks are emerging as the most capable of interrogating across multiple data types. Their further development will lead to sophisticated knowledge extraction, shaping the medicine of tomorrow.
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