Exploring disentangled generative factors of cancer transcriptomes

探索癌症转录组的解开生成因素

• 批准号: 2280533
• 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: 2019
• 资助国家: 英国
• 起止时间: 2019 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2280533
• 关键词: Exploring; disentangled; generative; factors; cancer

Cancer is a complex disease with genetic and environmental causes. The treatment of cancer has enormous complexity because molecular-level subtypes of the disease are not always apparent as phenotypes at the pathological levels, thus requiring stratification of patients, even down to individuals. The inability to do this results in any one of the vast array of oncological drugs available to clinicians eliciting a positive response only on a small fraction of sufferers of the disease.With recent advances in high throughput experimentation, large number of measurements at the level of genome aberration, nucleotide mutations, gene expressions, protein concentrations and regulatory information is acquired. It is accepted that our ability to acquire data outstrips our ability to make accurate and useful inferences from them.This project will explore the development and application of state-of-the art unsupervised learning methods to help understand the mapping between genotypic measurements and cancer phenotypes. Generative models that capture properties of the intricate tumour micro-environment will be developed. We will aim to disentangle the contributions of various factors influencing tumors, their progression and response to treatment. The work will be based on a rich repository of tumor related database: The Cancer Genome Atlas.

癌症是一种复杂的疾病，有遗传和环境原因。癌症的治疗具有巨大的复杂性，因为这种疾病的分子水平的亚型在病理水平上并不总是明显的表型，因此需要对患者进行分层，甚至深入到个人。无法做到这一点导致临床医生可用的大量肿瘤药物中的任何一种都只对一小部分患者产生阳性反应。随着最近高通量实验的进展，在基因组异常、核苷酸突变、基因表达、蛋白质浓度和调节信息的水平上获得了大量的测量。我们获得数据的能力超过了从数据中做出准确和有用的推断的能力。这个项目将探索最先进的无监督学习方法的发展和应用，以帮助理解基因测量和癌症表型之间的映射。将开发捕捉复杂的肿瘤微环境特性的生成性模型。我们的目标是理清影响肿瘤的各种因素的作用，肿瘤的进展和对治疗的反应。这项工作将基于丰富的肿瘤相关数据库：癌症基因组图谱。

项目成果

Modelling digital and manual contact tracing for COVID-19. Are low uptakes and missed contacts deal-breakers?

• DOI: 10.1371/journal.pone.0259969
• 发表时间: 2021
• 期刊: PloS one
• 影响因子: 3.7
• 作者: Rusu AC;Emonet R;Farrahi K
• 通讯作者: Farrahi K