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A Study of Computational Problems in Computational Biology and Social Networks: Cancer Informatics and Cascade Modelling

机译：计算生物学和社会网络中的计算问题研究：癌症信息学和级联模型

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摘要

It is undoubtedly that everything in this world are related and nothing independently exists. Entities interact together to form groups, resulting in many complex networks. Examples involve functional regulation models of proteins in biology, communities of people within social network. Since complex networks are ubiquitous in daily life, network learning had been gaining momentum in a variety of discipline like computer science, economics and biology. This call for new technique in exploring the structure as well as the interactions of network since it provides insight in understanding how the network works and deepening our knowledge of the subject in hand. For example, uncovering proteins modules helps us understand what causes lead to certain disease and how protein co-regulate each others. Therefore, my dissertation takes on problems in computational biology and social network: cancer informatics and cascade model-ling. In cancer informatics, identifying specific genes that cause cancer (driver genes) is crucial in cancer research. The more drivers identified, the more options to treat the cancer with a drug to act on that gene. However, identifying driver gene is not easy. Cancer cells are undergoing rapid mutation and are compromised in regards to the body's normally DNA repair mechanisms. I employed Markov chain, Bayesian network and graphical model to identify cancer drivers. I utilize heterogeneous sources of information to discover cancer drivers and unlocking the mechanism behind cancer. Above all, I encode various pieces of biological information to form a multi-graph and trigger various Markov chains in it and rank the genes in the aftermath. We also leverage probabilistic mixed graphical model to learn the complex and uncertain relationships among various bio-medical data. On the other hand, diffusion of information over the network had drawn up great interest in research community. For example, epidemiologists observe that a person becomes ill but they can neither determine who infected the patient nor the infection rate of each individual. Therefore, it is critical to decipher the mechanism underlying the process since it validates efforts for preventing from virus infections. We come up with a new modeling to model cascade data in three different scenarios.
机译：无疑，这个世界上的所有事物都是相关的，没有独立存在的事物。实体相互作用在一起形成组，从而形成许多复杂的网络。例子包括生物学中蛋白质的功能调节模型，社交网络中的人社区。由于复杂的网络在日常生活中无处不在，因此网络学习在诸如计算机科学，经济学和生物学之类的各种学科中获得了发展。这要求探索网络的结构和相互作用的新技术，因为它为了解网络的工作原理和加深我们对现有主题的知识提供了见识。例如，发现蛋白质模块有助于我们了解导致某些疾病的原因以及蛋白质如何相互调节。因此，我的论文主要涉及计算生物学和社会网络方面的问题：癌症信息学和级联模型。在癌症信息学中，识别导致癌症的特定基因（驱动基因）在癌症研究中至关重要。确定的驱动因素越多，用对该基因起作用的药物治疗癌症的选择就越多。但是，鉴定驱动基因并不容易。癌细胞正在经历快速突变，并在机体正常的DNA修复机制方面受损。我采用了马尔可夫链，贝叶斯网络和图形模型来识别癌症驱动因素。我利用异构信息源来发现癌症驱动因素并揭示癌症背后的机制。最重要的是，我对各种生物信息进行编码，以形成多幅图，并在其中触发各种马尔可夫链，然后对基因进行排序。我们还利用概率混合图形模型来学习各种生物医学数据之间复杂而不确定的关系。另一方面，通过网络传播信息引起了研究界的极大兴趣。例如，流行病学家观察到一个人生病了，但他们既无法确定谁感染了病人，也无法确定每个人的感染率。因此，解密该过程的基础机制至关重要，因为它验证了防止病毒感染的努力。我们提出了一种新的模型，可以在三种不同的情况下对级联数据进行建模。

著录项

  • 作者

    Ma, Christopher.;

  • 作者单位

    The University of Mississippi.;

  • 授予单位 The University of Mississippi.;
  • 学科 Computer science.;Bioinformatics.;Biostatistics.;Systematic biology.
  • 学位 Ph.D.
  • 年度 2018
  • 页码 126 p.
  • 总页数 126
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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