Addressing the unmet need for visualizing conditional random fields in biological data.
Addressing the unmet need for visualizing conditional random fields in biological data.
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DOI：
10.1186/1471-2105-15-202
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发表时间：
2014-07-10
影响因子：
3
通讯作者：
Bartlett CW
中科院分区：
文献类型：
--
作者：
Ray WC;Wolock SL;Callahan NW;Dong M;Li QQ;Liang C;Magliery TJ;Bartlett CW
生物世界充满了各种现象，这些现象似乎可以通过一个典型的统计框架——图形概率模型（GPM）来理想地建模和分析。gpm的结构是一个独特的很好的匹配生物学问题，从序列比对到基因组-表型关系的建模。gpm解决的基本问题包括基于相互作用因素的复杂网络做出决策。不幸的是，虽然gpm理想地适用于生物学中的许多问题，但它们并不是一种容易应用的解决方案。对于最终用户来说，构建GPM不是一项简单的任务。此外，应用gpm还受到一个潜在事实的阻碍，即问题固有的“相互作用因素的复杂网络”可能很容易定义，但也难以计算。我们认为可视化科学可以为生物科学的许多领域做出贡献，通过开发工具来解决GPM中的原型表示和用户交互问题，特别是称为条件随机场（CRF）的各种GPM。CRF带来了额外的功能和额外的复杂性，因为CRF依赖网络可以以查询数据为条件。在本文中，我们研究了几个适用于CRFs建模的生物学问题的共同特征，强调了现有可视化和可视化分析范式对这些数据的挑战，并记录了一个名为StickWRLD的实验解决方案，该解决方案虽然留有改进空间，但已成功应用于几个生物学研究项目。软件和教程可在http://www.stickwrld.org/上获得
The biological world is replete with phenomena that appear to be ideally modeled and analyzed by one archetypal statistical framework - the Graphical Probabilistic Model (GPM). The structure of GPMs is a uniquely good match for biological problems that range from aligning sequences to modeling the genome-to-phenome relationship. The fundamental questions that GPMs address involve making decisions based on a complex web of interacting factors. Unfortunately, while GPMs ideally fit many questions in biology, they are not an easy solution to apply. Building a GPM is not a simple task for an end user. Moreover, applying GPMs is also impeded by the insidious fact that the “complex web of interacting factors” inherent to a problem might be easy to define and also intractable to compute upon. We propose that the visualization sciences can contribute to many domains of the bio-sciences, by developing tools to address archetypal representation and user interaction issues in GPMs, and in particular a variety of GPM called a Conditional Random Field(CRF). CRFs bring additional power, and additional complexity, because the CRF dependency network can be conditioned on the query data. In this manuscript we examine the shared features of several biological problems that are amenable to modeling with CRFs, highlight the challenges that existing visualization and visual analytics paradigms induce for these data, and document an experimental solution called StickWRLD which, while leaving room for improvement, has been successfully applied in several biological research projects. Software and tutorials are available at http://www.stickwrld.org/
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