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

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。我们的目标是使用计算思维来改变生物分子的设计方式，通过提供物理建模，计算数据和适应性统计学习方法的协同组合来主动考虑随机性。目前，DNA和蛋白质的全电子测序是一个投机的梦想，可能会彻底改变生物化学科学和医学。本计画以计算架构探讨全电子定序的理论与演算基础。目前，机器学习领域将从数据中提取有用特征视为在很大程度上独立于作用于所提取特征的学习算法的设计。该项目旨在发现是否可以通过设计明确说明电子测序中的物理变化和随机性的学习算法来获得显着的性能增益。潜在的计算思维可能会导致见解和方法，从而导致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。 在量子器件和统计理论和方法的设计和理解纳米和机器学习系统的高中，本科和研究生的培训是该项目所涵盖的活动的一个组成部分。该奖项是网络使能的发现和创新计划的一部分，获奖者是M教授。华盛顿大学的P.Anantram和Maya Gupta。

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.