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基于原型监督对比学习的多功能治疗的识别

Sitong Niu¹, Henghui Fan^2,3, Fei Wang⁴

¹College of Mathematics and System sciences, Xinjiang University, Urumqi, 830046, Xinjiang, China.

Interdisciplinary sciences, computational life sciences

|December 23, 2024

概括

我们开发了PSCFA,这是一种使用原型监督对比学习和特征增强来识别多功能治疗 (MFTP) 的新型计算方法. 这种方法显著提高了治疗性的预测准确性.

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.
药物发现药物发现

背景情况:

高通量测序产生了大量的数据,需要有效的计算方法来识别多功能治疗 (MFTP).
现有的方法与类不平衡和有效的序列表示学习为MFTP预测而扎.

研究的目的:

提出PSCFA,一个新的计算框架,用于准确的MFTP预测.
解决序列表示和治疗性类鉴定中的类不平衡方面的挑战.

主要方法:

使用了两阶段的训练方案:原型的监督对比学习用于特征提取和特征增强用于分类器改进.
采用基于原型的变异自编码器,将知识从常见标签转移到不常见标签,增强特征表示.
专注于对不常见 (尾巴) 标签的功能增强,以提高分类器的性能.

主要成果:

与现有方法相比,PSCFA在MFTP预测中表现明显优越.
该方法有效地增强了特征空间分布的统一性,并改善了代表性不足的类的学习.
在治疗的计算识别方面取得了重大进展.

结论:

PSCFA提供了一个强大的和有效的计算解决方案,用于识别多功能治疗.
拟议的方法通过克服机器学习方法的关键挑战,推动了治疗性的发现领域.
这项工作为从序列数据中更准确和更有效地识别新型治疗铺平了道路.

关键词:

相反的学习学习.功能增强功能增强.多功能治疗多功能治疗.

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