蛋白质二级结构

这两种技术通常是不用于蛋白质二级结构和p转角预测的研究领域的。

Both of those techniques are commonly not found with the domain of protein secondary structure and beta-turns prediction .

基于改进BP神经网络预测蛋白质二级结构

Prediction Protein Secondary Structure by Improved BP Neural Network

mRNA序列、结构、能量和蛋白质二级结构的相关性

The Statistical Relationship between mRNA Sequence , Structure , Energy and Protein Secondary Structure

蛋白质二级结构预测结果表明，3个ω-3脂肪酸脱氢酶蛋白主要由α螺旋和p折叠组成。

Protein second-structure indicated that safflower ω - 3 fatty acid desaturase were composed by α - helix and β - sheet .

基于SqlServer的蛋白质二级结构预测样本集数据库的构建

Constitution of database on data sets for prediction of protein secondary structures based on SQL server

利用BP神经网络，建立了蛋白质二级结构预测的评测模型。

Using the BP nerve network , this paper established the evaluation model of protein secondary structure prediction .

蜂王浆不同贮存条件下蛋白质二级结构的Fourier变换红外光谱研究

FTIR Assessment of the Secondary Structure of Proteins in Royal Jelly under Different Storage Conditions Fourier transform infrared spectrometer

FT-IR在研究蛋白质二级结构中的应用

Research on protein secondary structure by Fourier transformation infrared spectroscopy

蛋白质二级结构预测中的HMM及I/OHMM方法研究

Research of HMM and I / O HMM used in protein secondary structure prediction

为了提高蛋白质二级结构预测精度，本文尝试采用一种基于串联BP网络集成的二级结构预测模型。

To improve the prediction results of protein secondary structure , we developed a neural network ensemble model based on dual-layer feed forward BP network .

表明：基于氨基酸组成和有偏自协方差函数为特征矢量的BP神经网络预测蛋白质二级结构含量的方法可有效提高预测精度。

It is shown that the BP neural network method combined with the amino-acid composition and the biased auto-covariance function features could effectively improve the prediction accuracy .

FT-IR显示mDC中的蛋白质二级结构发生了较大变化。

The results of FT-IR spectrometer showed the changes in imDC 's secondary structures of proteins .

应用标准隐Markov模型进行蛋白质二级结构预测时，必须根据同源家族蛋白库进行学习训练，从已有的知识出发进行二级结构预测。

When applying the standard hidden Markov model to predict the protein secondary structure , we must set out from the knowledge according to homological protein secondary structure database .

FT-Raman光谱对蛋白质二级结构的定量分析

A Quantitative Study on Secondary Structure of Proteins by FT Raman Spectroscopy

实验结果显示，用富含“生物进化信息”的Profile编码方式可以得到较高的预测结果，同时也表明，充分利用生物本身所具有的生物信息对提高蛋白质二级结构预测精度是非常重要的。

Results indicated that Profile encoding that preserves the redundant evolutionary information got higher prediction performance , and that how to use biologic evolutionary information effectively is very important to improve accuracy of protein secondary structure prediction .

方法：采用远紫外圆二色谱分析法，对正常人及甲旁亢患者血清的PTH（1-34）进行蛋白质二级结构的测定。

Methods : To determine the secondary structure of PTH ( 1-34 ) in normal and hyperparathyroidism condition using far-UV circular dichroism spectroscopy .

在线用蛋白质二级结构预测软件分析MAGE-4抗原表位，PCR获取MAGE-4抗原特征表位基因。

Analyze MAGE-4 antigen epitope by online predict software of protein secondary structure and amplify the epitope gene by PCR .

本文用常用的得分矩阵代替传统的Qian编码作为神经网络的输入层预测了200个蛋白质二级结构。

The present paper describes the artificial neural network for the prediction of the protein secondary structure on the basis of common score matrix instead of Qian code as the input layer .

基于GEP-BP网络集成的蛋白质二级结构预测方法研究

Study of protein secondary structure prediction methods based on GEP-BP network ensemble

蛋白质二级结构比较分析表明耐热株和非耐热株的二级结构上的差异主要在HN蛋白的α螺旋区。

Protein secondary structure comparative analysis showed that differences on the secondary structure between non-heat-resistant strains and heat-resistant strains were mainly in the alpha helical region of the HN protein .

依据Gamier原理预测了突变对S12蛋白质二级结构形成趋势的影响。

According to the principle of Garnier , we predicted that there might be alterations in the secondary structural propensity of protein S12 due to the mutation .

GP-MaxEnt模型的蛋白质二级结构预测

Protein secondary structure prediction based on GP-MaxEnt model

H195及其周围氨基酸的突变可导致蛋白质二级结构的改变和热稳定性的不同程度下降。

Site mutation of H195 and the amino acids around it can change the secondary structure and decrease thermal stability of Cs-cMDH in various degrees .

提出了一种新的基于贝叶斯神经网络（BNN）的蛋白质二级结构预测方法。

A novel model based on Bayesian neural networks for prediction of protein secondary structure is provided and a comparison of the performance of Bayesian neural networks ( BNN ) with traditional BP neural networks ( BPNN ) is made .

豆浆凝固过程中大豆蛋白质二级结构的研究

Study on the Protein Secondary Structure in the Soymilk Coagulation Process

应用傅里叶红外光谱研究强声波作用下植物壁蛋白质二级结构变化

The secondary structure changes of plant cell wall proteins aroused by

一种预测蛋白质二级结构的简便方法

A Simple and Convenient Method for Predicting Secondary Structures of Proteins

加工条件对再制干酪蛋白质二级结构的影响

Effects of Processing Conditions on Protein Secondary Structure of Processed Cheese

基于关联规则与遗传算法的蛋白质二级结构预测

Protein Secondary Structure Prediction Based on Association Rules and Genetic Algorithm

基于多神经网络的蛋白质二级结构预测模型

The model of Protein Secondary Structure based on the multi-modal neural network