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相关概念视频

Applications Of NMR In Biology01:25

Applications Of NMR In Biology

3.7K
Nuclear magnetic resonance (NMR) spectroscopy is a very valuable analytical technique for researchers. It has been used for more than 50 years as an analytical tool. F. Bloch and E. Purcell formulated NMR in 1946 and won the 1952 Nobel Prize in Physics  for their work. Biological macromolecules such as proteins, nucleic acids, lipids, and organic molecules including pharmaceutical compounds, can be studied using this versatile tool that exploits the magnetic properties of certain nuclei.
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Molecular Models02:00

Molecular Models

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Physical models representing molecular architectures of chemical compounds play essential roles in understanding chemistry. The use of molecular models makes it easier to visualize the structures and shapes of atoms and molecules.
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Super-resolution Fluorescence Microscopy01:37

Super-resolution Fluorescence Microscopy

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Super-resolution fluorescence microscopy (SRFM) provides a better resolution than conventional fluorescence microscopy by reducing the point spread function (PSF). PSF is the light intensity distribution from a point that causes it to appear blurred. Due to PSF, each fluorescing point appears bigger than its actual size, and it is the PSF interference of nearby fluorophores that causes the blurred image. Various approaches to achieving higher resolution through SRFM have recently been...
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Applications of IR Spectroscopy: Overview01:11

Applications of IR Spectroscopy: Overview

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The non-destructive nature and ability to provide valuable chemical information make IR spectroscopy a versatile technique with broad applications in various scientific and industrial fields. IR spectroscopy is commonly used to identify and characterize organic and inorganic compounds. It provides information about the functional groups present in a molecule and the bonding between atoms. This helps in the structural elucidation of compounds during organic synthesis, pharmaceutical research,...
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Quantifying Spatially Resolved Hydration Thermodynamics Using Grid Inhomogeneous Solvation Theory [Article v1.0].

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Updated: Jul 8, 2025

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning
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DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning

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对分子和材料进行深度学习.

Andrew D White1

  • 1Department of Chemical Engineering, University of Rochester, Rochester, NY.

Living journal of computational molecular science
|December 19, 2023
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概括
此摘要是机器生成的。

这本教科书为化学和材料科学提供了深度学习 (DL) 的实用指南. 它涵盖了基本的DL概念及其对分子数据的独特应用.

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A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data
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A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

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Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
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A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data
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科学领域:

  • 化学和材料科学 化学和材料科学
  • 人工智能的人工智能
  • 计算科学 计算科学

背景情况:

  • 深度学习 (DL) 在科学研究中越来越重要.
  • 现有的DL资源缺乏对化学和材料科学应用的关注.
  • 分子数据的独特挑战需要专门的DL方法.

研究的目的:

  • 为化学和材料科学提供深度学习的系统介绍.
  • 弥合目前这些领域的DL教育教材的差距.
  • 为研究人员提供知识,以便将DL应用于分子数据.

主要方法:

  • 涵盖了DL的基本数学.
  • 解释基本的机器学习概念.
  • 详细介绍了常见的神经网络架构.
  • 提供实施的实际指导.

主要成果:

  • 一个全面的概述DL的原则和技术.
  • 在化学和材料科学中,DL应用的具体例子.
  • 为该领域的从业者提供基础知识.

结论:

  • 这本教科书是科学家进入DL的关键资源.
  • 它解决了研究人员与分子数据工作的特定需求.
  • "活文档"方法确保内容保持最新.