检索结果-内蒙古大学图书馆

8th IEEE/ACIS International Conference on Computer and Information Science

作者： Tang, Buzhou Wang, Xuan Wang, Xiaolong Harbin Inst Technol Shenzhen Grad Sch Shenzhen 518055 Peoples R China

ISBN: (纸本)9780769536415

protein secondary structure prediction is an important step to understanding protein tertiary structure. Recent studies indicate that the correlation between neighboring secondary structures are beneficial to improve prediction performance. In this paper, we propose a new large margin approach for protein secondary structure prediction, which consider the problem as a sequence labeling problem like probabilistic graphical models. It doesn't only make full use of the correlation between neighboring secondary structures like graphical chain models, but also shares the key advantages of other SVM-based methods, i.e. learming non-linear discriminant via kernel functions. The experimental results on datasets: CB513 and RS126 show that our algorithm outperforms other state-of-the-art methods.

关键词： protein secondary structure prediction large margin approach sequence labeling problem probabilistic graphical models

来源：评论

学校读者我要写书评

暂无评论

protein secondary structure prediction Based on Improved C-SVM for Unbalanced Datasets

Protein Secondary Structure Prediction Based on Improved C-S...

引用

International Conference on Soft Computing Techniques and Engineering Application (ICSCTEA)

作者： Pei, Ao Henan Normal Univ Coll Comp & Informat Engn Xinxiang Henan Peoples R China

ISBN: (纸本)9788132216957;9788132216940

When protein secondary structure is predicted, the number of helix, sheet, and coiled coil is quite different in datasets. A multiple classifier based on improved clustering support vector machine (C-SVM) algorithm is proposed to predict protein secondary structure for unbalanced training datasets. Firstly, different weights are used for different types of samples to improve classification accuracy of traditional C-SVM on unbalanced samples. Secondly, the multiple classification strategy of one-versus-one (OVO) is used to build three binary classifiers. These binary classifiers are H/E, H/C, and E/C. The Majority-Voting law is used to integrate the results of three binary classifiers. Finally, sevenfold cross-validation based on grid method is used to optimize the parameters of classifiers. Simulation results show that, compared with the other prediction methods, the classification method proposed in this paper can obtain better classification accuracy on unbalanced datasets.

关键词： protein secondary structure prediction C-SVM Unbalanced datasets One-versus-one The majority-voting law

来源：评论

学校读者我要写书评

暂无评论

protein secondary structure prediction Based on Physicochemical Features and PSSM by SVM

Protein Secondary Structure Prediction Based on Physicochemi...

引用

10th Annual IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)

作者： Huang, Yin-Fu Chen, Shu-Ying Natl Yunlin Univ Sci & Technol Dept Comp Sci & Informat Engn Yunlin Taiwan

ISBN: (纸本)9781467358750

In this paper, we propose a protein secondary structure prediction method based on the support vector machine (SVM) with position-specific scoring matrix (PSSM) profiles and four physicochemical features, including conformation parameters, net charges, hydrophobic, and side chain mass. First, the SVM with the optimal window size and the optimal parameters of the kernel function is found. Then, we train the SVM using the PSSM profiles generated from PSI-BLAST and the physicochemical features extracted from the CB513 data set. Finally, we use the filter to refine the predicted results from the trained SVM. For all the performance measures of our method, Q(3) reaches 79.52, SOV94 reaches 86.10, and SOV99 reaches 74.60;all the measures are higher than those of the SVMpsi method and the SVMfreq method. This validates that considering these physicochemical features would exhibit better performances.

关键词： protein secondary structure prediction SVM PSSM profiles physicochemical features CB513 data set

来源：评论

学校读者我要写书评

暂无评论

protein secondary structure prediction Based on Fusion of Machine Learning Classifiers 21

Protein Secondary Structure Prediction Based on Fusion of Ma...

引用

36th Annual ACM Symposium on Applied Computing (SAC)

作者： de Oliveira, Gabriel Bianchin Pedrini, Helio Dias, Zanoni Univ Estadual Campinas Inst Comp Campinas SP Brazil

ISBN: (纸本)9781450381048

protein secondary structure prediction plays an important role in protein folding and function classification. Although the works available in the literature present good results, protein secondary structure prediction is still an open problem. In this work, we present and discuss a fusion strategy using four different classifiers. The fusion is composed of bidirectional recurrent networks, random forests, Inception-v4 blocks and Inception recurrent networks. In order to evaluate our model, we used CB6133 dataset as training and testing. The fusion achieved 76.4% of Q8 accuracy using the amino acid sequence and similarity information on CB6133, surpassing state-of-the-art approaches.

关键词： protein secondary structure prediction Machine Learning Neural Networks Fusion Classifiers

来源：评论

学校读者我要写书评

暂无评论

protein secondary structure prediction using different encoding schemes and neural network architectures

Protein secondary structure prediction using different encod...

引用

6th Conference on Data Mining and Knowledge Discovery

作者： Zhong, W Pan, Y Harrison, R Tai, PC Georgia State Univ Dept Comp Sci Atlanta GA 30303 USA

ISBN: (纸本)0819453560

protein secondary structure prediction is very important for drug design, protein engineering and immunological studies. This research uses fully connected multilayer perceptron (MLP) neural network with one, two and three hidden layers to predict protein secondary structure. Orthogonal matrix, BLOSUM62 matrix and hydrophobicity matrix are used for input profiles. To increase the input information for neural networks, the combined matrix from BLOSLTM62 and orthogonal matrix and the combined matrix from BLOSUM62 and hydrophobicity matrix are also experimented. Binary classifiers indicate test accuracy of one hidden layer is better than that of two and three hidden layers. This may indicate that increasing complexity of architecture may not help neural network to recognize structural pattern of protein sequence more accurately. The results also show that the combined input profile of BLOSUM62 matrix and orthogonal matrix is the best one among five encoding schemes. While accuracy of the tertiary classifier reaches 63.20%, binary classifier for H/similar toH is 78.70%, which is comparable to other researchers' results.

关键词： Multilayer Perceptron (MLP) protein secondary structure prediction encoding scheme orthogonal matrix hydrophobicity matrix BLOSUM62 matrix

来源：评论

学校读者我要写书评

暂无评论

protein secondary structure prediction Based on Two Dimensional Deep Convolutional Neural Networks 3

Protein Secondary Structure Prediction Based on Two Dimensio...

引用

3rd IEEE International Conference on Computer and Communications (ICCC)

作者： Liu, Yihui Cheng, Jinyong Ma, Yuming Chen, Yehong Qilu Univ Technol Inst Intelligent Informat Proc Jinan Shandong Peoples R China

ISBN: (纸本)9781509063529

The highest three-state prediction accuracy of protein secondary structure is now at 82-84% without using structure templates, approaching to the theoretical limit 8890%. Increasingly larger training datasets cover more protein sequences and structures. More powerful deep learning techniques are not only able to deal with the computation load of large data, but also can capture the long-range interactions of protein sequence. In this research, we propose a new approach to design a two dimensional deep convolutional neural networks (2DCNN) with 6 convolutional layers and 5 max-pooling layers. The two dimensional convolutional neural networks keep original amino acid sequence position information based on two dimensional input matrix, and extract features of the sequence interactions better. The performance of our prediction model 2DCNN is 83.09%, 81.74%, 82.41% 83.56% 81.16%, and 80.30% for 25PDB, CB513, CASP9, CASP10, CASP11, and CASP12 datasets. Our prediction model achieves better results compared to most state of the art methods. (http://***/protein)

关键词： protein secondary structure prediction feature extraction two dimensional deep convolution neural networks (2DCNN)

来源：评论

学校读者我要写书评

暂无评论

protein secondary structure prediction Based on Generative Confrontation and Convolutional Neural Network

引用

IEEE ACCESS 2020年 8卷 199171-199178页

作者： Zhao, Yawu Zhang, Hualan Liu, Yihui Qilu Univ Technol Sch Comp Sci & Technol Shandong Acad Sci Jinan 250353 Peoples R China

In the field of bioinformatics, the prediction of protein secondary structure is a challenging task, and it is extremely important for determining the structure and function of proteins. In this paper, the generation of adversarial network and convolutional neural network model are combined for protein secondary structure prediction. Firstly, generate a confrontation network to extract protein features, and then combine the extracted features with the original PSSM data as the input of the convolutional neural network to obtain prediction results. Testsets CASP9, CASP10, CASP11, CASP12, CB513 and PDB25 obtained 87.06%, 87.24%, 87.31%, 87.39%, 88.13% and 88.93%, which are 3.88%, 4.6%, 7.97%,5.85%, 5.78%, 4.25% higher than one using the convolutional neural network alone. The experimental results show that the feature extraction ability of generating adversarial networks is very significant.

关键词： Bioinformatics generative adversarial network convolutional neural network protein secondary structure prediction

来源：评论

学校读者我要写书评

暂无评论

protein secondary structure prediction Using Cascaded Feature Learning Model

引用

APPLIED SOFT COMPUTING 2023年 140卷

作者： Geethu, S. Vimina, E. R. Amrita Vishwa Vidyapeetham Dept Comp Sci & IT Sch Comp Kochi Campus Kochi India

The protein secondary structure prediction (PSSP) is pivotal for predicting tertiary structure, which is proliferating in demand for drug design and development. Further, it can be used to learn different protein functions. Although there are many computational methods for protein structure prediction, none of them have succeeded 100% in solving the protein structure prediction problem. This paper proposes a deep learning model, namely Cascaded Feature Learning Model (CFLM) that uses a multistage transfer learning approach based on the Residual Dense Network (RDN) for predicting protein secondary structure. The model is trained with different protein datasets at various levels of transfer learning and accepts selected protein features such as solvent accessibility, physicochemical properties, PSSM (position-specific scoring matrix), and PSFM (position-specific frequency matrix) as input. The Q3 and Q8 accuracy obtained on CASP 13 and 14 benchmark datasets with the proposed approach are 91.23% and 91.45%, and 76.83% and 78.04%, respectively. The performance of CFLM is also compared with some of the recent PSSP approaches and it is observed that the proposed approach improves the prediction accuracy by 2.8% in terms of Q3 and 1.81% in terms of Q8 in the CASP 14 dataset.& COPY;2023 Elsevier B.V. All rights reserved.

关键词： protein secondary structure prediction Transfer learning Residual Dense Network (RDN) Cascaded Feature learning

来源：评论

学校读者我要写书评

暂无评论

protein secondary structure prediction using a lightweight convolutional network and label distribution aware margin loss

引用

KNOWLEDGE-BASED SYSTEMS 2022年 237卷 107771-107771页

作者： Yang, Wei Hu, Zhentao Zhou, Lin Jin, Yong Henan Univ Sch Comp & Informat Engn Henan Key Lab Big Data Anal & Proc Kaifeng 475004 Peoples R China Henan Univ Sch Artificial Intelligence Zhengzhou 450046 Peoples R China

protein secondary structure prediction (PSSP) is an important task in computational molecular biology. Recently, deep neural networks have demonstrated great potential in improving the performance of eight-class PSSP. However, the existing deep predictors usually have higher model complexity and ignore the class imbalance of eight-class secondary structure data in training. In addition, the current methods cannot guarantee that the features corresponding to the padded residue positions are always zero during the forward propagation of the network, which will cause the prediction results of the same protein chain to be different under varied zero-padding numbers. To this end, we propose a novel lightweight convolutional network ShuffleNet_SS, which adopts modified 1-dimensional batch normalization to eliminate the impact of padded residue positions on nonpadded residue positions and uses the label distribution aware margin loss to enhance the network's ability to learn rare classes. In particular, in order to enable ShuffleNet_SS to fully achieve cross-group information exchange, we further improve the standard channel shuffle operation. Experimental results on the benchmark datasets including CASP10, CASP11, CASP12, CASP13, CASP14 and CB513 show that the proposed method achieves state-of-the-art performance with much lower parameters compared to the five existing deep predictors. (C) 2021 Elsevier B.V. All rights reserved.

关键词： protein secondary structure prediction Imbalanced classification Deep convolutional network Lightweight network Channel shuffle

来源：评论

学校读者我要写书评

暂无评论

protein secondary structure prediction based on integration of CNN and LSTM model

引用

JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION 2020年 71卷 102844-102844页

作者： Cheng, Jinyong Liu, Yihui Ma, Yuming Qilu Univ Technol Shandong Acad Sci Sch Comp Sci & Technol Jinan Peoples R China

protein structure prediction is an important issue in computational biology, and protein secondary structure prediction is the basis for protein three-dimensional structure prediction. A protein secondary structure prediction method based on convolutional neural networks (CNN) and Long Short-Term Memory (LSTM) is proposed in this paper. The architecture of CNN has two convolutional layers, one max-pooling layer and one ReLU activation layer. The feature maps extracted from second convolutional layer are used to feed to softmax classifier, and the first probability output is obtained. The LSTM model has a sequence layer and a last layer. The feature is extracted from last layer and input to random forest classifier to get the second probability output. The two probabilistic outputs are weighted and integrated to obtain the prediction model EN-CSLR in this paper. Based on the advantages of integration of the two models, cross-validation experiments are performed on the 25pdb dataset, and Q(3) reaches 80.18%, which is higher than using only one model. The experimental results show that the features extracted from CNN and LSTM models can effectively improve the accuracy of protein secondary structure prediction. (C) 2020 Elsevier Inc. All rights reserved.

关键词： protein secondary structure prediction Convolution neural networks Long short-term memory Softmax Random forest

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：