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相关概念视频

Cancer02:18

Cancer

Cancers arise due to mutations in genes involved in the regulation of cell division, which leads to unrestricted cell proliferation. Modern science and medicine have made great strides in the understanding and treatment of cancer, including eradicating cancer in some patients. However, there is still no cure for cancer. This is largely due to the fact that cancer is a large group of many diseases.
Tumor Progression02:07

Tumor Progression

Tumor progression is a phenomenon where the pre-formed tumor acquires successive mutations to become clinically more aggressive and malignant. In the 1950s, Foulds first described the stepwise progression of cancer cells through successive stages.
Colon cancer is one of the best-documented examples of tumor progression. Early mutation in the APC gene in colon cells causes a small growth on the colon wall called a polyp. With time, this polyp grows into a benign, pre-cancerous tumor. Further...
The Tumor Microenvironment02:17

The Tumor Microenvironment

Every normal cell or tissue is embedded in a complex local environment called stroma, consisting of different cell types, a basal membrane, and blood vessels. As normal cells mutate and develop into cancer cells, their local environment also changes to allow cancer progression. The tumor microenvironment (TME) consists of a complex cellular matrix of stromal cells and the developing tumor. The cross-talk between cancer cells and surrounding stromal cells is critical to disrupt normal tissue...
Targeted Cancer Therapies02:57

Targeted Cancer Therapies

The targeted cancer therapies, also known as “molecular targeted therapies,” take advantage of the molecular and genetic differences between the cancer cells and the normal cells. It needs a thorough understanding of the cancer cells to develop drugs that can target specific molecular aspects that drive the growth, progression, and spread of cancer cells without affecting the growth and survival of other normal cells in the body.
There are several types of targeted therapies against specific...
Treatment Resistant Cancers02:56

Treatment Resistant Cancers

Cancer is the second leading cause of death in the United States. A cancer cell is genetically unstable and hence can mutate faster. They can also modify their microenvironment and escape immune surveillance. The difficulties in treating cancer are further compounded by the emergence of rapid resistance to anticancer drugs. The most common ways to attain resistance in cancer cells include alteration in drug transport and metabolism, modification of drug target, elevated DNA damage response, or...
Treatment Resistent Cancers02:56

Treatment Resistent Cancers

Cancer is the second leading cause of death in the United States. A cancer cell is genetically unstable and hence can mutate faster. They can also modify their microenvironment and escape immune surveillance. The difficulties in treating cancer are further compounded by the emergence of rapid resistance to anticancer drugs. The most common ways to attain resistance in cancer cells include alteration in drug transport and metabolism, modification of drug target, elevated DNA damage response, or...

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相关实验视频

Updated: Jun 18, 2026

Guidelines and Experience Using Imaging Biomarker Explorer IBEX for Radiomics
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甲状腺癌放射学:在不断发展的环境中应对挑战.

Simone Maurea1, Arnaldo Stanzione1, Michele Klain1

  • 1Dipartimento di Scienze Biomediche Avanzate, Università degli Studi di Napoli Federico II, 80123 Naples, Italy.

Cancers
|December 23, 2023
PubMed
概括

本综述总结了用于药物发现的人工智能 (AI) 最近的进展. 它强调了人工智能在加速新疗法的识别和开发方面的潜力.

科学领域:

  • 生物医学科学 生物医学科学
  • 计算化学计算化学
  • 药理学 药理学是指药理学的学科.

背景情况:

  • 传统的药物发现过程是漫长的,昂贵的,并且失败率很高.
  • 人工智能 (AI) 提供了有前途的计算方法来简化药物发现.
  • 最近的技术进步使人工智能能够在药物开发的各个阶段应用.

研究的目的:

  • 在药物发现中提供AI应用的全面概述.
  • 分析人工智能对加速识别新药候选药物的影响.
  • 讨论AI在制药研究中的挑战和未来方向.

主要方法:

  • 2018年至2023年间发表的同行评审文献的系统审查.
  • 分析使用机器学习,深度学习和其他人工智能技术的研究.
  • 根据药物发现阶段对人工智能应用进行分类 (目标识别,头生成,优化等). ) 的情况.

主要成果:

  • 人工智能显著加快了打击识别和领先优化.
  • 机器学习模型在预测药物向相互作用方面表现出很高的准确性.
  • 人工智能工具越来越多地用于新药设计和虚拟查.

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结论:

  • 人工智能正在通过提高效率和降低成本来彻底改变药物发现.
  • 进一步整合人工智能与实验验证对于临床成功至关重要.
  • 药物发现的未来很可能涉及到人与人工智能的协同合作.