Exploring disentangled generative factors of cancer transcriptomes

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，因为疾病的分子水平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，甚至向下到个体。无法做到这一点的结果是在任何一个庞大的阵列的肿瘤药物可供临床医生引发积极的反应，只有一小部分的患者的diseases.With最近的进展，在高通量实验，大量的测量水平的基因组畸变，核苷酸突变，基因表达，蛋白质浓度和监管信息被收购。众所周知，我们获取数据的能力超过了我们从中做出准确和有用的推断的能力。本项目将探索最先进的无监督学习方法的发展和应用，以帮助理解基因型测量和癌症表型之间的映射。将开发捕获复杂肿瘤微环境特性的生成模型。我们的目标是解开影响肿瘤的各种因素的贡献，他们的进展和对治疗的反应。这项工作将基于一个丰富的肿瘤相关数据库：癌症基因组图谱。

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.