miRNAS Sequence Embedding For Pathway Detection And Selectivity

用于通路检测和选择性的 miRNAS 序列嵌入

• 批准号: 2601245
• 金额: --
• 依托单位: Queen Mary University of London
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2601245
• 关键词: miRNAS; Sequence; Embedding; Pathway; Detection

TO BE UPDATED NOV 2023 (AT YEAR 2 PROGRESSION) The search to get a medication that can curb or possibly cure diseases and afflictions is still at the forefront of medical research. What if the cure was already in our DNA? One such potential solution of study is microRNAs. One of the complexities associated with the living cell structure is the multiple, many to many interactions between microRNAs and messenger RNAs. This research hopes to understand the relationship between microRNAs, messenger RNAs and the Pathways that bring them together using multi-tasking machine learning techniques to create medications that carry the right combinations of microRNAs to solve specific ailments. This includes researching microRNAs clusters, Pathway clusters and possibly correlations amongst them to get a wholistic picture of interaction. Data used will be taken from both humans and animals. Multi-Task architectures include Path-Sluice with Task Affinity, Feature sharing and Shared Trunk Hierarchal.

寻找一种可以抑制或可能治愈疾病和痛苦的药物仍然处于医学研究的前沿。如果解药已经在我们的DNA里了呢？其中一个潜在的研究解决方案是microRNAs。活细胞结构的复杂性之一是microrna和信使rna之间的多重，多对多的相互作用。这项研究希望通过多任务机器学习技术，了解microRNAs、信使rna和将它们结合在一起的途径之间的关系，从而创造出携带microRNAs正确组合的药物，以解决特定疾病。这包括研究microrna集群、Pathway集群以及它们之间可能的相关性，以获得相互作用的整体图景。所使用的数据将取自人类和动物。多任务体系结构包括具有任务关联的路径闸、特征共享和共享中继层次结构。