研究人员开发了一个计算管道,使用空间转录和机器学习来识别胰腺管腺癌的早期标记物. Researchers developed a computational pipeline to identify early markers in pancreatic ductal adenocarcinoma using spatial transcriptomics and machine learning.

Johns Hopkins Kimmel癌症中心的研究人员开发了一种计算管道,将空间记录仪学、机器学习和单细胞数据集结合起来,以识别胰腺肾上腺癌(一种致命的胰腺癌形式)中的早期分子和细胞标记。 Researchers from the Johns Hopkins Kimmel Cancer Center have developed a computational pipeline combining spatial transcriptomics, machine learning, and single-cell datasets to identify early molecular and cellular markers in pancreatic ductal adenocarcinoma (PDAC), a deadly form of pancreatic cancer. 他们对9名病人和14名先发性损伤的诊断结果可有助于制定这种癌症的早期发现战略。 Their findings in nine patients and 14 premalignant lesions could help develop early detection strategies for this cancer type. 该研究的数据和代码可供其他研究人员使用。 The study's data and code are available to other researchers.