Biocomputing Core

CCNE的计算核心的设计考虑到两个主要目标：（1）提供数据 具有大规模自动化和集成软件堆栈的捕获、融合和共享环境，以及 (2)发现早期癌症，以及治疗前和治疗后对其演变的时间监测， 使用多变量状态估计技术分析捕获的生物过程的时间序列，以及 其他基于内核的技术。 全球数据共享、分析和一致性的实现需要软件（而非硬件）架构 它本质上是一个集成堆栈，将任何数据捕获设备与路由器、计算机、网络 管理软件和数据库，所有这些都连接到互联网，但还不能 communicate.我们的核心将展示这个架构是如何实现的，并将其与我们特定的项目数据一起使用 利用复杂的算法和我们团队的大量医学直觉来发现早期疾病。 随着时间的推移，我们的纳米传感器技术将被创建并集成到数据捕获和干预中， 周期，产生更高的时间分辨率，因此，更高的准确性和特异性概率 疾病识别和治疗。 成功的工作有两个高影响力的好处：（1）通过 互联网物理层在未来几年，（2）一种新的方法，用于收集和分析数据，以发现 初期疾病

The Computational Core of the CCNE has been designed with two major aims in mind: (1) providing a data capture, fusion, and sharing environment with large-scale automation and an integrated software stack, and (2) discovering incipient cancer, and pre- and post-treatment temporal monitoring of its evolution by analyzing captured time series of biological processes using multivariate state estimation techniques and other kernel-based technologies for the first time. The realization of global data sharing, analysis, and coherence requires software (not hardware) architecture that is essentially an integrated stack interfacing any data capture device with routers, computers, network management software, and databases, all of which are connected to the internet but not yet able to communicate. Our core will show how this architecture is realized and use it with our particular project data to find incipient disease using sophisticated algorithms and a great deal of medical intuition from our team. In time, our nanosensor technologies will be created and integrated into the data capture and intervention cycle, yielding even greater temporal resolution and therefore, higher probability of accurate and specific disease identification and treatment. There are two high impact benefits of successful work: (1) ubiquitous access to global data through the internet physical layer in the coming years, and (2) a new method for collecting and analyzing data to find incipient disease.