非洲,非洲,欧洲和中东地区:一种新的框架,用于使用以蛋白质为导向的变压器方法对抗癌症的分类
Zeynep Hilal Kilimci1, Mustafa Yalcin1
1Department of Information Systems Engineering, Kocaeli University, 41001, Kocaeli, Turkey.
Artificial intelligence in medicine
|August 22, 2024
概括
这项研究引入了一种高效的基于变压器的框架,用于识别抗癌 (ACP). 拟议的ESM模型实现了最先进的准确性,提高了对癌症治疗的ACP预测.
科学领域:
- 生物化学 生物化学
- 计算生物学 计算生物学
- 在瘤学瘤学.
背景情况:
- 抗癌 (ACP) 是有前途的治疗剂,因为它们选择性地向癌细胞.
- 在开发准确的预测模型来确定ACP方面仍然存在挑战.
研究的目的:
- 开发一种高效的基于变压器的框架,用于准确预测抗癌.
- 为了评估不同变压器模型 (ESM,ProtBERT,BioBERT,SciBERT) 的性能,用于ACP识别.
主要方法:
- 使用了四种变压器模型:ESM,ProtBERT,BioBERT和SciBERT.
- 在广泛使用的ACP数据集上训练并测试模型:AntiCp2,cACP-DeepGram和ACP-740.
- 开发了一个基于转换器的高效框架,用于ACP预测.
主要成果:
- 拟议的ESM框架实现了最先进的准确性:96.45%在AntiCp2上,97.66%在cACP-DeepGram上,88.51%在ACP-740.
- 与现有文学研究相比,该框架显示了更高的分类准确性.
- 开发的模型显著提高了ACP预测的可靠性和准确性.
结论:
- 基于变压器的框架,特别是ESM模型,提供了一个非常准确和可靠的方法来识别抗癌.
- 这一进步具有显著的潜力,可以加速开发新型癌症疗法.
- 公开可用的代码有助于在癌症药物发现方面进一步的研究和应用.
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