ProLoc-IHS:基于免疫组织化学图像和序列信息的多标签蛋白质亚细胞定位
1College of Communication Engineering, Jilin University, Renmin Street No.5988, Changchun, 130012, Jilin, China.
International journal of biological macromolecules
|May 16, 2025
概括
一个新的计算工具,ProLoc-IHS,通过整合蛋白质序列数据,准确地从免疫组织化学 (IHC) 图像中预测人类蛋白质亚细胞定位 (SCL). 这促进了细胞蛋白在组织中的分布的自动分析.
科学领域:
- 生物技术是生物技术.
- 计算生物学 计算生物学
- 蛋白质组学是指蛋白质组学.
背景情况:
- 免疫组织化学 (IHC) 成像对于研究组织中人类蛋白质亚细胞定位 (SCL) 是至关重要的.
- 手动的SCL注释是劳动密集型的,并且限制了数据集大小,需要自动化计算工具.
- 现有的计算模型往往忽略了有价值的蛋白质序列信息.
研究的目的:
- 开发一种新的计算模型,ProLoc-IHS,用于使用IHC图像预测蛋白质SCL.
- 整合视觉 (IHC图像) 和序列 (蛋白质序列) 数据,以提高预测准确度.
- 为培训和评估SCL预测模型创建一个新的双模式数据集.
主要方法:
- 从人类蛋白质图谱 (HPA) 和UniProt编制了一个双模数据集,包括IHC图像和相应的蛋白质序列.
- ProLoc-IHS使用视觉变压器 (Vit) 进行图像嵌入,并使用ProtT5进行蛋白序列嵌入.
- 嵌入式通过交叉注意模块融合,其次是具有多头注意和残余连接的特征学习模块. 二元交叉和焦点损失被用于多标签分类.
主要成果:
- 与现有的预测模型相比,ProLoc-IHS表现出优越的性能.
- 该模型有效地结合了视觉和顺序特征,用于增强SCL预测.
- 一个新的数据集和ProLoc-IHS代码被公开了.
结论:
- ProLoc-IHS在从IHC图像中自动预测蛋白质SCL方面取得了重大进展.
- 整合蛋白质序列数据大大提高了计算SCL分析的准确性.
- 开发的工具和数据集有助于在生物和病理背景下对蛋白质定位进行大规模分析.
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