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Combination Therapies and Personalized Medicine02:50

Combination Therapies and Personalized Medicine

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Combining two or more treatment methods increases the life span of cancer patients while reducing damage to vital organs or tissue from the overuse of a single treatment. Combination therapy also targets different cancer-inducing pathways, thus reducing the chances of developing resistance to treatment.
The combination of the drug acetazolamide and sulforaphane is a good example of combination therapy to treat cancer. The cells in the interior of a large tumor often die due to the hypoxic and...
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Combined Effects of Drugs: Synergism01:27

Combined Effects of Drugs: Synergism

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Synergism is a useful mechanism where combining two or more drugs is more effective than each constituent used alone. Such combinations are also called supra-additive interactions. The drugs collectively enhance the final therapeutic effect by acting on different targets. Another advantage is that the low dose of each constituent drug is sufficient to achieve the desired effect. This helps reduce the duration of therapy and lower the adverse effects of these drugs.
Such synergistic combinations...
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Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

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Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence...
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Transformers01:26

Transformers

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A device that transforms voltages from one value to another using induction is called a transformer. A transformer consists of two separate coils, or windings, wrapped around the same soft iron core. However, they are electrically insulated from each other.
The iron core has a substantial relative permeability. Therefore, the magnetic field lines generated due to the current in one winding are almost entirely confined within the core, such that the same magnetic flux permeates each turn of both...
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Combined Effects of Drugs: Antagonism01:30

Combined Effects of Drugs: Antagonism

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The combined effects of drugs can result in various interactions, of which an important type is antagonism. Antagonism is a mechanism where one drug inhibits or counteracts the effects of another drug. Antagonism can occur through various means, including receptor binding, allosteric modulation, functional interaction, chemical reactions, and pharmacokinetic processes.
The most common type is receptor antagonism, where one drug acts as an antagonist to block the effects of another drug by...
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Types Of Transformers01:16

Types Of Transformers

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Transformers can provide desired voltages to a circuit by modifying the number of turns in the secondary windings.
If the ratio of the number of turns in the secondary winding to that of the primary winding is greater than one, then the transformer is said to be a step-up transformer. In a step-up transformer, the voltage at the secondary winding is greater than the voltage applied at the primary winding.
However, if this ratio is less than one, the transformer is said to be a step-down...
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Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
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DeepTraSynergy:使用多式联络深度学习与变压器的药物组合.

Fatemeh Rafiei1, Hojjat Zeraati1, Karim Abbasi2

  • 1Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran 1417613151, Iran.

Bioinformatics (Oxford, England)
|July 19, 2023
PubMed
概括
此摘要是机器生成的。

DeepTraSynergy是一种新的深度学习模型,准确地预测癌症治疗的协同药物组合. 这种多任务方法利用多模式数据,在药物协同效应预测方面表现优于现有的方法.

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科学领域:

  • 计算生物学是一种计算生物学.
  • 生物信息学是一种生物信息学.
  • 机器学习在药物发现中的作用

背景情况:

  • 药物组合对于有效的癌症治疗至关重要,提供更高的疗效和选择性.
  • 预测药物协同作用是复杂的,但对于开发新型治疗策略至关重要.

研究的目的:

  • 介绍DeepTraSynergy,这是一个用于预测药物组合协同作用的深度学习模型.
  • 利用多模式数据,包括药物标,蛋白质-蛋白质和细胞标相互作用,以提高预测准确度.

主要方法:

  • DeepTraSynergy使用变压器来表示药物特征.
  • 多任务学习框架预测了药物向相互作用,毒性和药物组合协同作用.
  • 辅助任务 (毒性和药物向相互作用的预测) 增强了初级协同效应的预测.

主要成果:

  • 在DrugCombDB (0.7715) 和瘤-屏幕 (0.8052) 数据集上,DeepTraSynergy在预测协同药物组合方面取得了很高的准确性.
  • 该模型的性能优于现有的经典和最先进的方法.
  • 蛋白质-蛋白质相互作用网络的整合显著改善了预测性能.

结论:

  • DeepTraSynergy提供了一种强大而有效的深度学习方法,用于药物组合协同预测.
  • 多任务框架和多模式数据集成是其卓越性能的关键.
  • 这种方法有望加速开发有效的组合癌症疗法.