CDI-Type I: Modeling Quantum Tunnel Current to Statistically Sequence Biomolecules

CDI-Type I：模拟量子隧道电流以对生物分子进行统计测序

• 批准号: 1027812
• 负责人: Manjeri Anantram
• 金额: $ 57.6万
• 依托单位: University of Washington
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-09-15 至 2015-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1027812&HistoricalAwards=false
• 关键词: CDI; Type; Modeling; Quantum; Tunnel

This paradigm shifting project advances the ability to read DNA and proteins and builds on designing nanotechnology systems using machine learning. The goal is to use computational thinking for changing the way biomolecules are designed by providing a synergistic combination of physical modeling, computational data, and adapted statistical learning methods that proactively account for the randomness. Currently all-electronic sequencing of DNA and proteins is a speculative dream that could revolutionize biochemical sciences and medicine. This project investigates theoretically and algorithmically the fundamentals of all-electronic sequencing using a computational framework. Currently, the field of machine learning treats the extraction of useful features from data as largely independent from the design of learning algorithms that act on the extracted features. This project aims at discovering whether significant performance gains can be obtained by designing learning algorithms that explicitly account for the physical variations and randomness in electronic sequencing. The underlying computational thinking can potentially lead to insights and methods leading to transformative advances on all-electronic sequencing of biomolecules. The research direction of this project can impact biology, medicine, nanoelectronics, and the design of machine learning systems. The training of high school, undergraduate and graduate students in quantum devices and statistical theory and methods in the design and understanding of nanoscale and machine learning systems are an integral part of the activities spanned by this project.This award is part of the Cyber-Enabled Discovery and Innovation program, and the recipients are Professors M. P. Anantram and Maya Gupta of the University of Washington.

这个范式转变项目提高了读取 DNA 和蛋白质的能力，并建立在使用机器学习设计纳米技术系统的基础上。目标是通过提供物理建模、计算数据和主动解释随机性的适应性统计学习方法的协同组合，使用计算思维来改变生物分子的设计方式。目前，DNA 和蛋白质的全电子测序只是一个推测性的梦想，它可能会彻底改变生化科学和医学。该项目使用计算框架从理论上和算法上研究全电子测序的基础知识。目前，机器学习领域认为从数据中提取有用特征在很大程度上独立于作用于提取特征的学习算法的设计。该项目旨在发现通过设计明确考虑电子测序中的物理变化和随机性的学习算法是否可以获得显着的性能提升。潜在的计算思维可能会带来见解和方法，从而推动生物分子全电子测序的变革性进展。该项目的研究方向可以影响生物学、医学、纳米电子学和机器学习系统的设计。 对高中生、本科生和研究生进行量子器件以及设计和理解纳米级和机器学习系统的统计理论和方法方面的培训是该项目所涵盖活动的一个组成部分。该奖项是 Cyber​​-Enabled 项目的一部分发现与创新项目，获奖者为华盛顿大学 M. P. Anantram 教授和 Maya Gupta 教授。