Ruffalo Matthew, Stojanov Petar, Pillutla Venkata Krishna, Varma Rohan, Bar-Joseph Ziv
Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA.
Electrical and Computer Engineering, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA.
BMC Syst Biol. 2017 Oct 10;11(1):96. doi: 10.1186/s12918-017-0471-8.
Translating in vitro results to clinical tests is a major challenge in systems biology. Here we present a new Multi-Task learning framework which integrates thousands of cell line expression experiments to reconstruct drug specific response networks in cancer.
The reconstructed networks correctly identify several shared key proteins and pathways while simultaneously highlighting many cell type specific proteins. We used top proteins from each drug network to predict survival for patients prescribed the drug.
Predictions based on proteins from the in-vitro derived networks significantly outperformed predictions based on known cancer genes indicating that Multi-Task learning can indeed identify accurate drug response networks.
将体外实验结果转化为临床试验是系统生物学中的一项重大挑战。在此，我们提出了一种新的多任务学习框架，该框架整合了数千个细胞系表达实验，以重建癌症中的药物特异性反应网络。
重建的网络正确识别了几个共享的关键蛋白质和信号通路，同时突出了许多细胞类型特异性蛋白质。我们使用每个药物网络中的顶级蛋白质来预测使用该药物的患者的生存率。
基于体外衍生网络中的蛋白质所做的预测显著优于基于已知癌症基因所做的预测，这表明多任务学习确实可以识别准确的药物反应网络。
