Deep Learning for Inference from Biological Data

从生物数据进行推理的深度学习

• 批准号: 2508119
• 金额: --
• 依托单位: University of Southampton
• 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-2508119
• 关键词: Deep; Learning; Inference; Biological; Data

With the rapid increase in our ability to make various measurements about biology at the genomic, epi-genetic, gene expression and population levels, there is widespread acknowledgement within the biological community that mathematical and computational modelling is a necessary tool to be able to understand how molecular interaction and genetically encoded information translates into biological function, and how to translate such understanding to help in the treatment of complex diseases. Of particular interest is the measurement of gene expression at the different levels of transcriptome and proteome, how this mapping is regulated and what kind of information encoded in the DNA sequence are determinants of it. We have been building a strong collaboration between ECS and the School of Medicine in this domain and so far have: (i) an outlier detection algorithm (published in Bioinformatics) for detection of post-translationally regulated proteins, which later formed the subject of a successful KTP bid; (ii) a publication that builds on the above (in the Journal of Immunology) looking at new data that is mapped to micro RNAs regulating protein levels; (iii) A manuscript on the subject of translation regulation (submission to Nucleic Acids Research imminent) and (iv) a grant submission this round to BBSRC (currently under review). This project will be in this rich domain, focusing on two areas we have hitherto not addressed: (i) the role of splicing and its regulation; and (ii) extracting information from the sequence level with focus on regulatory elements in the non-coding regions of the genome. We expect a combination of measured and sequence-derived features to be beneficial in making accurate inferences and will be developing advanced representations learned from deep learning techniques to achieve this.

随着我们在基因组、表观遗传、基因表达和种群水平上对生物学进行各种测量的能力的迅速提高，生物界普遍认识到数学和计算建模是一种必要的工具，能够理解分子相互作用和遗传编码信息如何转化为生物功能，以及如何将这种理解转化为帮助治疗复杂疾病。特别感兴趣的是在转录组和蛋白质组的不同水平上测量基因表达，这种映射是如何调节的，以及DNA序列中编码的信息是什么样的决定因素。我们一直在ECS和医学院在这一领域建立强有力的合作，到目前为止已经：（i）异常值检测算法（发表于Bioinformatics）用于检测后免疫调节蛋白，其后来形成了成功的KTP投标的主题;（ii）以上述各项为基础的出版物（在免疫学杂志上）研究映射到调节蛋白质水平的微RNA的新数据;（iii）关于翻译调节主题的手稿（即将提交给核酸研究）和（iv）本轮向BBSRC提交的资助申请（目前正在审查中）。该项目将在这个丰富的领域，重点放在两个领域，我们迄今尚未解决：（i）剪接的作用及其调控;和（ii）从序列水平提取信息，重点是在基因组的非编码区的调控元件。我们希望测量和序列衍生特征的组合有助于做出准确的推断，并将开发从深度学习技术中学习的高级表示来实现这一目标。