相关毒素:生物信息学分类,预测和未来方向
Rui Li1, Junwen Yu1, Dongxin Ye1
1The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China.
Toxins
|February 25, 2025
概括
机器学习 (ML) 和深度学习 (DL) 通过提高分类和功能预测来加速共毒素研究. 这些计算方法有助于发现用于治疗开发的新型共毒素.
科学领域:
- 生物化学 生物化学
- 药理学 药理学是指药理学的学科.
- 计算生物学 计算生物学
背景情况:
- 毒素是来自 * Conus * 种类毒素的,具有特定的离子通道和受体相互作用.
- 它们的药理性质为药物开发和分子工具提供了潜力.
- 传统的毒素分析是劳动密集型的,推动了对计算方法的需求.
研究的目的:
- 审查最近在将机器学习 (ML) 和深度学习 (DL) 应用于毒素研究方面的进展.
- 为了比较数据库,特征提取和分类模型,在ML/DL中用于共毒素.
- 讨论使用计算方法增强治疗发现的未来方向.
主要方法:
- 关于ML和DL在毒素研究中的应用现有文献的综述.
- 比较各种数据库,特征提取技术和分类模型.
- 分析新兴趋势和未来的研究途径.
主要成果:
- ML和DL显著改善了基于序列的分类和对毒素的功能预测.
- 目前正在开发各种计算方法来进行新设计.
- 审查强调了ML/DL在加速共毒素研究中的有效性.
结论:
- 计算方法,特别是ML和DL,对于高效的毒素研究至关重要.
- 多式联运数据和精细的预测模型的整合将进一步增强治疗发现.
- 未来的研究应该专注于利用人工智能开发基于毒素的新药.
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