Kexin Chen

Jul 26, 2024 | nature.com | Kexin Chen

AbstractAccurate discrimination between patients with and without cancer from cfDNA is crucial for early cancer diagnosis. Herein, we develop and validate a deep-learning-based model entitled end-motif inspection via transformer (EMIT) for discriminating individuals with and without cancer by learning feature representations from cfDNA end-motifs. EMIT is a self-supervised learning approach that models rankings of cfDNA end-motifs.

Jul 18, 2024 | onlinelibrary.wiley.com | Lianghua Wang |Kexin Chen |Xiuping Yuan |Jianjun Yin

Supporting Information As a service to our authors and readers, this journal provides supporting information supplied by the authors. Such materials are peer reviewed and may be re-organized for online delivery, but are not copy-edited or typeset. Technical support issues arising from supporting information (other than missing files) should be addressed to the authors.

Jul 18, 2024 | pericles.pericles-prod.literatumonline.com | Lianghua Wang |Kexin Chen |Xiuping Yuan |Jianjun Yin

Supporting Information As a service to our authors and readers, this journal provides supporting information supplied by the authors. Such materials are peer reviewed and may be re-organized for online delivery, but are not copy-edited or typeset. Technical support issues arising from supporting information (other than missing files) should be addressed to the authors.