GraphCrowd: Using Crowdsourced Design to Visualize Effects of Environmental Chemicals on Signaling Networks

GraphCrowd：使用众包设计可视化环境化学品对信号网络的影响

• 批准号: 9078417
• 负责人: Kurt Luther
• 金额: $ 31.33万
• 依托单位: VIRGINIA POLYTECHNIC INST AND ST UNIV
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-08-11 至 2018-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9078417
• 关键词: Address; Affect; Algorithms; Apoptosis; Biological; Biological Assay; Biology; Cell Cycle; Cells; Chemicals; Complex; Computational Technique; Computer software; Computing Methodologies; Creativeness; Crowding; Cues; Data; Databases; Effectiveness; Exposure to; Face; Feedback; Graph; Health; Hepatocyte; Human; Imagery; Intuition; Knowledge; Lead; Life; Location; Long-Term Effects; Mechanics; Methods; Molecular; Online Systems; Organ; Output; Pathway interactions; Poison; Positioning Attribute; Process; Proteins; Psyche structure; Reaction; Research; Scientist; Seeds; Signal Transduction; Societies; System; Techniques; Technology; Testing; Time; Toxic effect; United States Environmental Protection Agency; Vent; Visual; Work; biomedical scientist; citizen science; college; crowdsourcing; design; environmental chemical; experience; improved; iterative design; man; protein metabolite; public health relevance; receptor; research study; response; volunteer

 DESCRIPTION (provided by applicant): Humans are repeatedly exposed to many man-made chemicals, and the long-term effects of most of these chemical on human health are unknown. A starting point to improve our understanding is to determine how the addition of a chemical affects the processes that occur within cells. These processes are composed of tens of thousands of proteins and different types of interactions among them that form complex networks. Computational methods can analyze these networks in combination with experimental data on a chemical to determine the sub-network that is perturbed within the cell by that chemical. Such "chemical response networks" can be visualized in displays where each protein receives a location (x and y coordinates) such that proteins connected by an interaction are closely placed. There are many different ways to automatically assign these locations. Several software packages and web-based systems implement these graph layout algorithms. However, it is often very difficult to interpret these networks since the algorithms that determine these locations have very little knowledge of the underlying biology. Biologists often manually manipulate initial, automatically determined positions until the network configuration matches their knowledge and intuition, but this process is time-consuming and requires significant expertise. In this application, we seek to develop an online system that enables large, geographically distributed groups of non-experts (crowd users) to manipulate chemical response networks in order to make their visualized layouts meaningful to expert scientists. Our system will use automatically generated biological cues to guide the users on where to place nodes and edges, how to group them, and which nodes/edges to ignore. It will also enable users to highlight sequences of interactions that suggest how a chemical may control specific proteins and processes that occur in the cell. Our system will develop layouts through an iterative design process where users serve as barnstormers (generate several layout ideas), critics (offer principled feedback on layouts), and synthesizers (combine the best ideas from multiple layouts). We will employ three different types of crowd users: paid workers on Amazon Mechanical Turk, volunteer citizen scientists from Zooniverse, and college biology majors. We will perform extensive studies to evaluate the effectiveness of each group in creating graph layouts. We will also conduct a review by an expert panel of biomedical scientists to evaluate the effectiveness of our system in creating visualizations of chemical response networks that are valuable to experts and can assist them in formulating biological hypotheses.

 描述(申请人提供)：人类反复接触许多人造化学品，这些化学品对人类健康的长期影响尚不清楚。提高我们理解的一个起点是确定一种化学物质的添加如何影响细胞内发生的过程。这些过程由数以万计的蛋白质和它们之间不同类型的相互作用组成，形成了复杂的网络。计算方法可以结合化学物质的实验数据来分析这些网络，以确定细胞内受到该化学物质干扰的子网络。这样的“化学反应网络”可以在显示器上可视化，其中每个蛋白质都有一个位置(x和y坐标)，这样通过相互作用连接的蛋白质就可以紧密地放置在一起。有许多不同的方法可以自动分配这些位置。几个软件包和基于Web的系统实现了这些图形布局算法。然而，解释这些网络通常是非常困难的，因为确定这些位置的算法对潜在的生物学知识非常少。生物学家经常手动操作初始的、自动确定的位置，直到网络配置与他们的知识和直觉相匹配，但这个过程很耗时，需要大量的fican专业知识。在这项应用中，我们寻求开发一个在线系统，使地理上分布的大型非专家群体(大众用户)能够操纵化学反应网络，以便使他们的可视化布局对专业科学家有意义。我们的系统将使用自动生成的生物线索来指导用户在哪里放置节点和边，如何对它们进行分组，以及忽略哪些节点/边。它还将使用户能够突出显示相互作用的序列，这些序列表明一种化学物质可能如何控制特定的fic蛋白和细胞中发生的过程。我们的系统将通过迭代设计过程来开发布局，在这个过程中，用户充当仓储者(生成几个布局想法)、批评者(提供布局的原则性反馈)和综合者(结合多个布局中的最佳想法)。我们将雇佣三种不同类型的大众用户：亚马逊土耳其机械公司的付费员工，来自Zooniverse的志愿者公民科学家，以及大学生物专业的学生。我们将进行广泛的研究，以评估每个小组在创建图表布局方面的有效性。我们还将由生物医学科学家组成的专家小组进行审查，以评估我们的系统在创建对专家有价值并可帮助他们制定生物学假说的化学反应网络可视化方面的有效性。