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The role of RAS effectors in BCR/ABL induced chronic myelogenous leukemia
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《医学前沿（英文）》 2013年 第7卷 第4期 页码 452-461 doi: 10.1007/s11684-013-0304-0
BCR/ABL is the causative agent of chronic myelogenous leukemia (CML). Through structure/function analysis, several protein motifs have been determined to be important for the development of leukemogenesis. Tyrosine177 of BCR is a Grb2 binding site required for BCR/ABL-induced CML in mice. In the current study, we use a mouse bone marrow transduction/transplantation system to demonstrate that addition of oncogenic NRAS (NRASG12D) to a vector containing a BCR/ABLY177F mutant “rescues” the CML phenotype rapidly and efficiently. To further narrow down the pathways downstream of RAS that are responsible for this rescue effect, we utilize well-characterized RAS effector loop mutants and determine that the RAL pathway is important for rapid induction of CML. Inhibition of this pathway by a dominant negative RAL is capable of delaying disease progression. Results from the present study support the notion of RAL inhibition as a potential therapy for BCR/ABL-induced CML.
化学工程师的主动机器学习 Perspective
Dobbelaere, Yi Ouyang, Kevin De Ras, Maarten K. Sabbe, Guy B. Marin, Kevin M. Van Geem
《工程（英文）》 2023年 第27卷 第8期 页码 23-30 doi: 10.1016/j.eng.2023.02.019
By combining machine learning with the design of experiments, thereby achieving so-called active machine learning, more efficient and cheaper research can be conducted. Machine learning algorithms are more flexible and are better than traditional design of experiment algorithms at investigating processes spanning all length scales of chemical engineering. While active machine learning algorithms are maturing, their applications are falling behind. In this article, three types of challenges presented by active machine learning—namely, convincing the experimental researcher, the flexibility of data creation, and the robustness of active machine learning algorithms—are identified, and ways to overcome them are discussed. A bright future lies ahead for active machine learning in chemical engineering, thanks to increasing automation and more efficient algorithms that can drive novel discoveries.
关键词： Active machine learning Active learning Bayesian optimization Chemical engineering Design of experiments
《医学前沿（英文）》 2022年 第16卷 第1期 页码 102-110 doi: 10.1007/s11684-021-0850-9
关键词： COVID-19 RAS inhibitor hypertension all-cause mortality
标题 作者 时间 类型 操作
化学工程师的主动机器学习
Dobbelaere, Yi Ouyang, Kevin De Ras, Maarten K. Sabbe, Guy B. Marin, Kevin M. Van Geem
期刊论文
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