Visual Programming Tool for Integration of Gene and Protein Cancer Profiling Data

描述（由申请人提供）：互联网已经把整个研究社区的共同努力，经验和知识放在科学家的指尖上。这些资源为更全面地了解癌症提供了潜在的巨大价值，并随后开发出更有效的诊断，预后和治疗策略。然而，由于缺乏工具和技术，这些资源仍然没有得到充分利用，使研究人员能够轻松访问这些资产。此外，来自这些不同资源的信息很少-如果有的话-是完整的，策划的，或语义上与研究人员内部产生的信息相同。因此，大多数生命科学项目都给研究人员提出了一项艰巨的任务，即收集、整合和分析处于各种完整性和完整性状态的异构数据集，同时经常使用效率低下的临时方法，这些方法不能充分解决这些关键差异和差异或语义异质性。拟议的项目代表了对当前生物信息学工作的贡献，为研究人员提供工具，使他们能够在没有完全解决这些巨大而复杂的问题的情况下进行有效的研究。具体来说，我们建议开发强大的和具有成本效益的可视化编程软件，使研究人员能够整合从质谱（MS）和微阵列（MA）实验在癌症研究中获得的知识。拟议的软件将建立在两个现有的INCOGEN生物信息学工具上，并将利用开放社区努力提供的不断增长的资源，如NCI癌症生物医学信息网格（caBIG）。项目团队认为，使拟议工作在本质上与这些努力互补，并纳入caBIG提议的资源（即使并非所有计划的资源都可用），而不是从第一原则开发技术和资源，是非常明智和有效的。知识整合必须在信息技术、统计学和语义学三个方面取得成功。为了实现这一目标，在我们的第一阶段工作中，我们将：1）评估MS和MA数据表示的当前标准，2）基于评估的技术开发概念验证知识集成应用程序，以及3）通过征求两个测试站点和其他科学家的反馈，评估概念验证工具对研究人员生产力的影响。最终，在第二阶段工作完成后，拟议的项目将产生一个综合研究的商业平台，该平台将提供一个可视化编程接口，以访问分布式分析和可视化工具，并建立完整的分析工作流程。该软件包将1）提高研究人员正确整合和分析来自MS和MA实验的内部数据的能力，2）提高外部资源整合和联合的效率，3）使研究人员群体能够建立多学科和多实验室合作伙伴关系，以追求对复杂疾病（如癌症）的系统级理解。

DESCRIPTION (provided by applicant): The Internet has put the combined efforts, experience and knowledge of entire research communities literally at the fingertips of scientists. These resources offer potentially enormous value toward obtaining a more comprehensive understanding of cancer and, subsequently, developing more effective diagnostic, prognostic, and treatment strategies against it. However, these resources remain vastly underutilized because of a lack of tools and technologies that allow researchers easy access to those assets. Furthermore, information from these diverse resources is rarely - if ever - complete, curated, or semantically homogeneous with the information produced in-house by the researcher. Therefore, most life science projects present researchers with the daunting task of collecting, integrating, and analyzing heterogeneous sets of data in various states of integrity and completeness, while often using inefficient, ad hoc methods that do not sufficiently address these critical differences and discrepancies or the semantic heterogeneity. The proposed project represents a contribution toward current bioinformatics efforts to provide researchers with tools that enable them to conduct effective research in the absence of a full solution to these vast and complex problems. Specifically, we propose to develop robust and cost-effective visual programming software that will allow researchers to integrate knowledge gained from mass spectrometry (MS) and microarray (MA) experiments in cancer research. The proposed software will be built upon two existing INCOGEN bioinformatics tools, and will leverage the growing resources provided by open community efforts, such as the NCI Cancer Biomedical Information Grid (caBIG). The project team deems that it is highly sensible and efficient to make the proposed work complementary in nature to such efforts and to incorporate the resources proposed by caBIG (even if not all planned resources become available) rather than developing technologies and resources from first principles. Knowledge integration must be successful in three aspects: information technology, statistics, and semantics. Toward that goal, during our Phase I work we will: 1) evaluate current standards for the representation of MS and MA data, 2) develop a proof-of-concept knowledge-integration application based on the evaluated technologies, and 3) evaluate the impact of the proof-of-concept tool on the productivity of researchers by soliciting feedback from two beta sites and other scientists. Ultimately, after completion of the Phase II work, the proposed project will result in a commercial platform for integrative research that will provide a visual programming interface to access distributed analysis and visualization tools and to build complete analysis workflows. The software package will 1) increase the ability of researchers to correctly integrate and analyze in-house data from MS and MA experiments, 2) increase the efficiency of integration and federation of external resources, and 3) enable groups of researchers to establish multidisciplinary and multi-laboratory partnerships to pursue a systems-level understanding of complex diseases such as cancer.