MOKPE:通过多元优化基于内核保存嵌入的药物向相互作用预测
Oğuz C Binatlı1, Mehmet Gönen2,3
1Graduate School of Sciences and Engineering, Koç University, 34450, Istanbul, Turkey.
BMC bioinformatics
|July 5, 2023
概括
本研究介绍了基于多重优化的内核保存嵌入 (MOKPE) 进行药物向相互作用 (DTI) 预测. MOKPE有效地模拟异质数据,在预测DTI方面表现优于现有方法.
科学领域:
- 生物信息学是一种生物信息学.
- 计算生物学 计算生物学
- 药物发现 药物发现 药物发现
背景情况:
- 生物信息学往往涉及整合来自不同,异构来源的数据.
- 确定药物向相互作用 (DTI) 对药物发现至关重要.
- 现有的方法在模拟复杂,异构的数据以进行DTI预测方面遇到了困难.
研究的目的:
- 提出一个新的框架,基于多重优化的内核保护嵌入 (MOKPE),用于模拟异质数据.
- 通过保持数据类型内部和数据类型之间的相似性,有效地预测药物向相互作用.
- 在药物发现中提高DTI识别的准确性和效率.
主要方法:
- 开发了基于多重优化的内核保护嵌入 (MOKPE) 框架.
- 预测异质药物和目标数据到一个统一的嵌入空间.
- 同时保留药物-标相互作用,药物-药物相似性和标-标相似性.
主要成果:
- 与基于相似性的最先进方法相比,MOKPE在预测DTI方面表现优越或可比.
- 通过对四个DTI网络数据集进行十倍交叉验证的十次复制进行评估.
- 成功预测了以前未见的DTI,并对预测给定网络内的未知DTI进行了评估.
结论:
- MOKPE是一种有效的框架,用于模拟生物信息学中的异质数据.
- 拟议的方法显著提高了DTI预测的准确性和效率.
- R实现和复制脚本是公开可用的,以便进一步研究.
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