THItoGene:一种深度学习方法,用于从组织学图像中预测空间转录组学
Yuran Jia1, Junliang Liu1, Li Chen2
1Institute for Bioinformatics, School of Computer Science and Technology, Harbin Institute of Technology, Harbin, 150040, China.
Briefings in bioinformatics
|December 25, 2023
概括
THItoGene使用一种新的AI方法从病理图像中预测空间基因表达. 这种方法为空间转录学提供了一种经济有效的替代方案,准确地从组织学中揭示了基因调节的洞察力.
科学领域:
- 计算生物学 计算生物学
- 基因组学就是基因组学.
- 病理学 病理学 病理学
背景情况:
- 空间转录组学为细胞调节提供了洞察力,但成本昂贵.
- 目前用于从组织学预测空间基因表达的AI方法缺乏深度信息提取能力.
研究的目的:
- 开发一种负担得起且有效的方法,从组织学图像中预测空间基因表达.
- 探索高分辨率病理图像表型与基因表达调节之间的关系.
主要方法:
- 开发了THItoGene,这是一个混合神经网络,结合了动态卷积和囊网络.
- 利用深度学习来适应地感知组织学图像中的分子信号.
- 评估了人类乳腺癌和皮肤状细胞癌数据集的性能.
主要成果:
- 与现有的方法相比,THItoGene在空间基因表达预测方面表现出卓越的性能.
- 该模型成功地破译了特定组织区域内的空间背景和丰富信号.
- 在各种人类癌症数据集上得到验证,展示了强大的预测能力.
结论:
- THItoGene提供了一个具有成本效益和准确的解决方案,用于从组织学中预测空间基因表达.
- 人工智能工具可以揭示复杂的基因调节模式和空间组织信息.
- 这种方法增强了病理图像对基因组研究的有用性.
相关概念视频
02:07Improving Translational Accuracy
Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
02:07Improving Translational Accuracy
Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...