乐胖代购免代理版

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However, despite the wealth of high-throughput data available, the performance of these models is hindered by the lack of sufficient training data, particularly in clinical research (in vivo experiments). As a result, translating this knowledge into clinical practice, such as predicting drug responses, remains a challenging task. Transfer learning is a promising tool that bridges the gap between data domains by transferring knowledge from the source to the target domain. Researchers have proposed transfer learning to predict clinical outcomes by leveraging pre-clinical data (mouse, zebrafish), highlighting its vast potential. In this work, we present a comprehensive literature review of deep transfer learning methods for health informatics and clinical decision-making, focusing on high-throughput molecular data. Previous reviews mostly covered image-based transfer learning works, while we present a more detailed analysis of transfer learning papers. Furthermore, we evaluated original studies based on different evaluation settings across cross-validations, data splits and model architectures. The result shows that those transfer learning methods have great potential; high-throughput sequencing data and state-of-the-art deep learning models lead to significant insights and conclusions. Additionally, we explored various datasets in transfer learning papers with statistics and visualization.<\/jats:p>","DOI":"10.1093\/bib\/bbad254","type":"journal-article","created":{"date-parts":[[2023,7,17]],"date-time":"2023-07-17T02:06:35Z","timestamp":1689559595000},"source":"Crossref","is-referenced-by-count":10,"title":["Deep transfer learning for clinical decision-making based on high-throughput data: comprehensive survey with benchmark results"],"prefix":"10.1093","volume":"24","author":[{"given":"Muhammad","family":"Toseef","sequence":"first","affiliation":[{"name":"Department of Computer Science, City University of Hong Kong , Hong Kong SAR"}]},{"given":"Olutomilayo","family":"Olayemi Petinrin","sequence":"additional","affiliation":[{"name":"Department of Computer Science, City University of Hong Kong , Hong Kong SAR"}]},{"given":"Fuzhou","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Computer Science, City University of Hong Kong , Hong Kong SAR"}]},{"given":"Saifur","family":"Rahaman","sequence":"additional","affiliation":[{"name":"Department of Computer Science, City University of Hong Kong , Hong Kong SAR"}]},{"given":"Zhe","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Computer Science, City University of Hong Kong , Hong Kong SAR"}]},{"given":"Xiangtao","family":"Li","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, Jilin University , Jilin , China"}]},{"given":"Ka-Chun","family":"Wong","sequence":"additional","affiliation":[{"name":"Department of Computer Science, City University of Hong Kong , Hong Kong SAR"},{"name":"Hong Kong Institute for Data Science, City University of Hong Kong , Hong Kong SAR"}]}],"member":"286","published-online":{"date-parts":[[2023,7,14]]},"reference":[{"issue":"4","key":"2023072020170981800_ref1","doi-asserted-by":"crossref","first-page":"599","DOI":"10.1038\/nprot.2017.149","article-title":"Exponential scaling of single-cell rna-seq in the past decade","volume":"13","author":"Svensson","year":"2018","journal-title":"Nat Protoc"},{"issue":"2","key":"2023072020170981800_ref2","doi-asserted-by":"crossref","first-page":"168","DOI":"10.1111\/imcb.12404","article-title":"The future is now? 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