图 书 馆
利用机器学习算法预测抗癌分子活性Predicting anti-cancer molecule activity using machine learning algorithms |
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| 课程网址: | http://videolectures.net/licsb08_santos_pam/ |
| 主讲教师: | Jose Santos |
| 开课单位: | 伦敦帝国理工学院 |
| 开课时间: | 2008-08-17 |
| 课程语种: | 英语 |
| 中文简介: |  在本文中，我们研究了4.000种独特化合物对60种细胞系（例如白血病，前列腺癌，乳腺癌）的抗癌活性。小分子在生物学中起着重要作用，因为它们可以用作更复杂分子的构建基块，并且还可以与抑制或促进其作用的蛋白质相互作用。在这种情况下，将这种化合物添加到细胞中的后果可能很深远，因为蛋白质可能会参与非常复杂的链反应。这样，可以设计可以是有用药物的小分子。在这里，我们仅专注于预测给定分子的特性：它是否会针对给定的癌细胞系显示出抗癌活性（被测量为引起至少50％的细胞生长抑制）。由于全球数据库中小分子的数量正在增加，并且正确进行实验室测试的能力受到限制，因此这种计算预测非常重要。 |
| 课程简介: | In this paper we study the anti-cancer activity of - 4.000 unique compounds against a set of 60 cell lines (e.g. Leukemia, Prostate, Breast). Small molecules play an important role in biology as they can be used as building blocks for more complex molecules and also interact with proteins inhibiting or promoting their action. In this case the consequence of adding such a compound to a cell can be far reaching as the protein may be involved in a very complex chain reaction. As such it is possible to design small molecules which can be useful drugs. Here we concentrate only in predicting a property of a given molecule: whether it will show anti-cancer activity (measured as causing at least 50% cell growing inhibition) against a given cancerous cell line. This computational prediction is important as there are a growing number of small molecules in databases worldwide and the capacity for proper lab testing is limited. |
| 关 键 词: | 生物学; 癌细胞 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2020-09-28：wuyq |
| 最后编审: | 2021-09-15：zyk |
| 阅读次数: | 59 |