ASPTF:一种计算工具,通过使用机器学习算法来预测植物中的非生物应激反应性转录因子
Upendra Kumar Pradhan1, Anuradha Mahapatra2, Sanchita Naha3
1Division of Statistical Genetics, ICAR-Indian Agricultural Statistics Research Institute, PUSA, New Delhi 110012, India.
Biochimica et biophysica acta. General subjects
|March 15, 2024
概括
识别植物转录因子 (TFs) 是开发耐压作物的关键. 一个机器学习模型准确地预测了参与非生物应激反应的TF,帮助作物育种工作.
科学领域:
- 植物科学 植物科学
- 计算生物学是一种计算生物学.
- 基因组学就是基因组学.
背景情况:
- 无生物应激显著影响作物生长和产量.
- 转录因子 (TFs) 是压力反应期间植物基因表达的关键调节者.
- 识别应激反应的TF对于培育适应气候变化的作物品种至关重要.
研究的目的:
- 开发一种用于预测与植物非生物应激反应相关的转录因子 (TFs) 的计算模型.
- 通过特征选择和机器学习算法来提高TF预测的准确性.
- 提供一个用户友好的工具来识别涉及非生物应激耐受性的植物TF.
主要方法:
- 生成四个序列衍生特征以数字编码TF序列.
- 采用十个浅层和四个深度学习算法进行预测.
- 利用特征选择技术,包括光梯度增强机器变量重要性测量 (LGBM-VIM),以识别信息特征.
主要成果:
- 使用LGBM-VIM选定的特征的LightGBM (LGBM) 算法实现了高交叉验证性能 (精度:86.81%,auROC:92.98%,auPRC:94.03%).
- 独立测试证实了该模型的稳定性,准确度为81.98%,auROC为90.65%,auPRC为91.30%.
- 预测服务器ASPTF被开发并在线提供.
结论:
- 开发的机器学习方法有效地预测了植物转录因子 (TFs) 对非生物压力的反应.
- ASPTF 网络服务器有助于识别与压力相关的TF,补充实验方法.
- 这一策略有助于培育对非生物压力的耐受性提高的作物品种.
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