通过生成对抗网络和机器学习技术的结合来预测病毒coproteins
Jorge F Beltrán1, Lisandra Herrera-Belén2, Alejandro J Yáñez3,4
1Department of Chemical Engineering, Faculty of Engineering and Science, Universidad de La Frontera, Ave. Francisco Salazar 01145, Temuco, Chile. beltran.lissabet.jf@gmail.com.
Scientific reports
|November 7, 2024
概括
我们开发了VirOncoTarget,这是一种用于预测病毒coproteins的新工具. 这种机器学习方法显著提高了识别致癌病毒蛋白的准确性,有助于癌症研究.
科学领域:
- 计算生物学 计算生物学
- 生物信息学是一种生物信息学.
- 机器学习 机器学习
背景情况:
- 病毒瘤蛋白是癌症发展的关键驱动因素.
- 鉴定病毒coproteins的传统方法是低效和昂贵的.
- 机器学习为预测生物活动提供了有希望的解决方案.
研究的目的:
- 开发一种准确和可通用的方法来预测病毒coproteins.
- 整合生成对抗网络 (GANs) 与监督学习,以提高预测.
- 建立一个用户友好的网络应用程序,用于病毒蛋白识别.
主要方法:
- 评估了多个机器学习模型:随机森林,多层感知器,光梯度增强机,极端梯度增强和支持向量机.
- 使用了生成对抗网络 (GAN) 和监督学习的组合.
- 对开发的模型进行了十倍的交叉验证和独立测试.
主要成果:
- 增强GAN的随机森林模型实现了高性能:0.976准确度,0.976F1得分,0.977精度,0.976灵敏度和1.0AUC在交叉验证中.
- 独立测试证实了该模型的稳定性,准确度为0.982,F1得分为0.982,精度为0.982,灵敏度为0.982,AUC为1.0.
- 成功开发和部署了VirOncoTarget网络应用程序.
结论:
- 开发的VirOncoTarget工具提供了一种快速可靠的病毒蛋白预测方法.
- 这种方法提高了预测模型的准确性和通用性.
- 预计VirOncoTarget将成为癌症研究和理解病毒瘤发生的宝贵资源.
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