相关实验视频
Updated: Jul 10, 2026
06:38
Pattern-based Search of Epigenomic Data Using GeNemo
Published on: October 8, 2017
5.1K
高度可扩展的任务分组,用于深度多任务学习,用于预测表观遗传事件
Mohammad Shiri1, Jiangwen Sun1
1Department of Computer Science, Old Dominion University, Norfolk, VA, USA.
Proceedings. IEEE International Conference on Bioinformatics and Biomedicine
|January 11, 2024
概括
本研究引入了一个可扩展的任务分组框架,以改善深度神经网络 (DNN) 训练,用于从DNA序列预测细胞事件. 该方法通过分组相关任务来减少负面转移,增强生物机制的发现.
科学领域:
- 基因组学就是基因组学.
- 计算生物学 计算生物学
- 机器学习 机器学习
背景情况:
- 深度神经网络 (DNN) 从DNA序列预测细胞事件,帮助全基因组关联研究 (GWAS).
- 多任务学习 (MTL) 增强了DNN培训,但通常会导致负面转移,在某些任务中性能降低.
- 现有的MTL框架共享单个特征提取网络,限制了可扩展性和有效性.
研究的目的:
- 在MTL中开发一个可扩展的任务分组框架,以减轻负面转移.
- 为了提高经过训练的DNN从DNA序列预测细胞事件的性能.
- 加强阐明GWAS发现背后的生物机制.
主要方法:
- 为MTL提出了一个高度可扩展的任务分组框架.
- 只有那些对彼此有潜在利益的任务,共同培训.
- 通过一次性联合培训获得的任务特定分类头的利用网络重量.
主要成果:
- 证明了拟议的任务分组框架的有效性.
- 在367个表观遗传特征的数据集上表现出优于基线方法的优势.
- 在多任务DNN培训中成功减少负面转移.
结论:
- 拟议的任务分组框架有效地解决了生物序列分析中MTL的负转移问题.
- 这种方法为现有的MTL策略提供了一个可扩展和优质的替代方案.
- 增强的DNN模型可以加速从基因组数据中发现生物机制.
相关概念视频
01:46Epigenetic Regulation
Epigenetic mechanisms play an essential role in healthy development. Conversely, precisely regulated epigenetic mechanisms are disrupted in diseases like cancer.
01:46Epigenetic Regulation
Epigenetic mechanisms play an essential role in healthy development. Conversely, precisely regulated epigenetic mechanisms are disrupted in diseases like cancer.
01:37Epigenetic Regulation
Epigenetic changes alter the physical structure of the DNA without changing the genetic sequence and often regulate whether genes are turned on or off. This regulation ensures that each cell produces only proteins necessary for its function. For example, proteins that promote bone growth are not produced in muscle cells. Epigenetic mechanisms play an essential role in healthy development. Conversely, precisely regulated epigenetic mechanisms are disrupted in diseases like cancer.
X-chromosome...
X-chromosome...