检索结果-内蒙古大学图书馆

2010 IEEE World Congress on Computational Intelligence

作者： Tsang, Herbert H. Jiang, Tiancheng Wiese, Kay C. Jacob, Christian Univ Calgary Dept Comp Sci Calgary AB T2N 1N4 Canada Simon Fraser Univ Sch Comp Sci Burnaby BC Canada

ISBN: (纸本)9781424481262

Ribonucleic Acid (rna) has important structural and functional roles in the cell and plays roles in many stages of protein synthesis as well. The functions of rna molecules are determined largely by their three-dimensional structure. SArna-Predict has shown excellent results in predicting rna secondary structure based on Simulated Annealing (SA). SArna-Predict uses a permutation-based representation to the rna secondary structure and this paper investigates the impact of the mutation operators in this algorithm. Experiments were performed using a sample of eleven sequences from four rna classes. The results presented in this paper demonstrate that SArna-Predict using the percentage swap translocating mutation operator can produce similar results when compared with previous research. Furthermore, the new operator has the potential of reaching a solution with a lower free energy. This supports the use of the proposed operator on rna secondary structure prediction of other known structures.

关键词： rna secondary structure prediction SArna-predict macromolecules permutation-based representation ribonucleic acid simulated annealing simulated annealing three-dimensional structure

来源：评论

学校读者我要写书评

暂无评论

Study of rna secondary structure prediction Algorithms

Study of RNA Secondary Structure Prediction Algorithms

引用

International Conference on Biotechnology, Chemical and Materials Engineering (CBCME 2011)

作者： Sun, Ying Ye, Shenghua Lu, Hongwei Shanghai Univ Sci & Technol Med Instrument & Food Engn Coll Shanghai 201800 Peoples R China Univ Toronto Div Engn Sci Toronto ON Canada

ISBN: (纸本)9783037853078

rna secondary structure study is one of the most important fields in computational molecular biology. According to different conditions, rna secondary structure prediction derives two ways. In the paper, the method to predict rna secondary structure is introduced in two ways. It includes the mathematic models and main algorithms. The paper also points out the existing problems. The main development directions of rna secondary structure prediction algorithm is also be indicated.

关键词： rna secondary structure prediction Minimum free energy model Comparative sequence method Pseudoknot

来源：评论

学校读者我要写书评

暂无评论

Evaluation of rna secondary structure prediction for both base-pairing and topology

引用

Biophysics Reports 2018年第3期4卷 123-132页

作者： Yunjie Zhao Jun Wang Chen Zeng Yi Xiao Institute of Biophysics School of Physics and Key Laboratory of Molecular Biophysics of the Ministry of EducationHuazhong University of Science and Technology Institute of Biophysics and Department of Physics Central China Normal University Department of Physics The George Washington University

secondary structures of rnas are crucial to the understanding of their tertiary structures and functions. At present, many theoretical methods are widely used to predict rna secondary structures. The performance of these methods has been evaluated but only for their ability of base-pairing ***, the topology of a rna secondary structure is more important for understanding its tertiary structure and function, especially for long rnas. In this paper, we constructed a new non-redundant rna database containing 73 rna with lengths of 50–300 nucleotides and benchmarked four popular algorithms for both base pairing and topology. The results show that the prediction accuracy of secondary structure topology is only 38%, in contrast to 70% for that of base pairing. Furthermore, the topological consistency is not strongly correlated to the base-pairing consistency. Our results will be helpful to understand the limitations of rna secondary structure prediction methods from a different point of view and also to their improvements in future.

关键词： rna secondary structure prediction Base pairs Structural topology prediction accuracy

来源：评论

学校读者我要写书评

暂无评论

GCNfold: A novel lightweight model with valid extractors for rna secondary structure prediction

引用

COMPUTERS IN BIOLOGY AND MEDICINE 2023年 164卷 107246-107246页

作者： Yang, Enbin Zhang, Hao Zang, Zinan Zhou, Zhiyong Wang, Shuo Liu, Zhen Liu, Yuanning Jilin Univ Coll Comp Sci & Technol Changchun 130012 Peoples R China Jilin Univ Key Lab Symbol Computat & Knowledge Engn Minist Educ Changchun 130012 Peoples R China Jilin Univ Coll Software Changchun 130012 Peoples R China Nagasaki Inst Appl Sci Grad Sch Engn Nagasaki 8510193 Japan

rna secondary structure is essential for predicting the tertiary structure and understanding rna function. Recent research tends to stack numerous modules to design large deep-learning models. This can increase the accuracy to more than 70%, as well as significant training costs and prediction efficiency. We proposed a model with three feature extractors called GCNfold. structure Extractor utilizes a three-layer Graph Convolutional Network (GCN) to mine the structural information of rna, such as stems, hairpin, and internal loops. structure and Sequence Fusion embeds structural information into sequences with Transformer Encoders. Long-distance Dependency Extractor captures long-range pairwise relationships by UNet. The experiments indicate that GCNfold has a small number of parameters, a fast inference speed, and a high accuracy among all models with over 80% accuracy. Additionally, GCNfold-Small takes only 90ms to infer an rna secondary structure and can achieve close to 90% accuracy on average. The GCNfold code is available on Github https://***/EnbinYang/GCNfold.

关键词： rna secondary structure prediction Knowledge and data integration Graph convolutional network Transformer UNet

来源：评论

学校读者我要写书评

暂无评论

BAT-Net: An enhanced rna secondary structure prediction via bidirectional GRU-based network with attention mechanism

引用

COMPUTATIONAL BIOLOGY AND CHEMISTRY 2022年 101卷 107765页

作者： Shen, Cong Chen, Yu Xiao, Feng Yang, Tian Wang, Xinyue Chen, Shengyong Tang, Jijun Liao, Zhijun Tianjin Univ Technol Sch Comp Sci & Engn 391 Binshui West St Tianjin 300384 Peoples R China Minist Educ Engn Res Ctr Learning Based Intelligent Syst 391 Binshui West St Tianjin 300384 Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol 1068 Xueyuan Ave Shenzhen 518055 Guangdong Peoples R China Tianjin Univ Coll Intelligence & Comp 135 Yaguan Rd Tianjin 300350 Peoples R China Tianjin Univ Key Lab Syst Bioengn Minist Educ 92 Weijin Rd Tianjin 300072 Peoples R China Univ South Carolina Dept Comp Sci & Engn 550 Assembly St Columbia SC 29208 USA Fujian Med Univ Sch Basic Med Sci 1 Xuefu North Rd Fuzhou 350122 Fujian Peoples R China

Background: rna secondary structure (RSS) has drawn growing concern, both for their pivotal roles in rna tertiary structures prediction and critical effect in penetrating the mechanism of functional non-coding rna. Computational techniques that can reduce the in vitro and in vivo experimental costs have become popular in RSS prediction. However, as an NP-hard problem, there is room for improvement that the validity of the prediction RSS with pseudoknots in traditional machine learning ***: In this essay, by integrating the bidirectional GRU (Gated Recurrent Unit) with the attention, we propose a multilayered neural network called BAT-Net to predict RSS. Different from the state-of-the-art works, BAT-Net can not only make full use of the information about the direct predecessor and direct successor of the predicted base in the rna sequence but also dynamically adjust the corresponding loss function. The experimental results on five representative datasets extracted from the rna STRAND database show that the sensitivity, precision, accuracy, and MCC (Matthews Correlation Coefficient) of the BAT-Net have improved by 8.52%, 8.28%, 5.66% and 9.82%, respectively, compared with the benchmark approaches on the best ***: BAT-Net can provide users with more credible RSS results since it has further utilized the source information of the dataset. Comparative results show that the proposed BAT-Net is superior to the other existing methods on the relevant indicators.

关键词： Bioinformatics rna secondary structure prediction Recurrent neural network Attention

来源：评论

学校读者我要写书评

暂无评论

Consensus rna secondary structure prediction using information of neighbouring columns and principal component analysis

引用

INTErnaTIONAL JOUrnaL OF COMPUTATIONAL SCIENCE AND ENGINEERING 2019年第3期19卷 430-439页

作者： Liu, Tianhang Yin, Jianping Gao, Long Chen, Wei Qiu, Minghui Natl Univ Def Technol Comp Sch Changsha Hunan Peoples R China Chinese Peoples Liberat Army Gen Hosp Med Informat Inst Beijing Peoples R China

rna is a family of biological macromolecules. It is important to all kinds of biological processes. rna structure is closely related to its functions. Hence, determining the structure is invaluable in understanding genetic diseases and creating drugs. Nowadays, rna secondary structure prediction is a field yet to be researched. In this paper, we present a novel method using rna sequence alignment to predict a consensus rna secondary structure. In essence, the goal of the method is to give a prediction about whether any two columns of an alignment correspond to a base pair or not, using the information provided by the alignment. The information includes the covariation score, the fraction of complementary nucleotides and the consensus probability matrix of the column pair and those of its neighbours. Then principal component analysis is applied to overcome the problem of over-fitting. A comparison of our method and other consensus rna secondary structure prediction methods including NeCFold, ELMFold, KnetFold, PFold and rnaalifold, in 47 families from Rfam (version 11.0) is performed. Results show that our method surpasses the other methods in terms of Matthews correlation coefficient, sensitivity and selectivity.

关键词： rna secondary structure prediction comparative sequence analysis principal component analysis PCA information of neighbouring columns

来源：评论

学校读者我要写书评

暂无评论

The four ingredients of single-sequence rna secondary structure prediction. A unifying perspective

引用

rna BIOLOGY 2013年第7期10卷 1185-1196页

作者： Rivas, Elena Howard Hughes Med Inst Ashburn VA USA

Any method for rna secondary structure prediction is determined by four ingredients. The architecture is the choice of features implemented by the model (such as stacked basepairs, loop length distributions, etc.). The architecture determines the number of parameters in the model. The scoring scheme is the nature of those parameters (whether thermodynamic, probabilistic, or weights). The parameterization stands for the specific values assigned to the parameters. These three ingredients are referred to as the model. The fourth ingredient is the folding algorithms used to predict plausible secondary structures given the model and the sequence of a structural rna. Here, I make several unifying observations drawn from looking at more than 40 years of methods for rna secondary structure prediction in the light of this classification. As a final observation, there seems to be a performance ceiling that affects all methods with complex architectures, a ceiling that impacts all scoring schemes with remarkable similarity. This suggests that modeling rna secondary structure by using intrinsic sequence-based plausible foldability will require the incorporation of other forms of information in order to constrain the folding space and to improve prediction accuracy. This could give an advantage to probabilistic scoring systems since a probabilistic framework is a natural platform to incorporate different sources of information into one single inference problem.

关键词： rna secondary structure prediction context-free grammars thermodynamic parameters probabilistic models statistical training

来源：评论

学校读者我要写书评

暂无评论

SCFGs in rna secondary structure prediction : A Hands-on Approach

SCFGs in RNA Secondary Structure Prediction : A Hands-on App...

引用

作者： Zsuzsanna Sükösd Ebbe S. Andersen Rune Lyngsø

Stochastic context-free grammars (SCFGs) were first established in the context of natural language modelling, and only later found their applications in rna secondary structure prediction. In this chapter, we discuss the basic SCFG algorithms (CYK and inside–outside algorithms) in an application-centered manner and use the pfold grammar as a case study to show how the algorithms can be adapted to a grammar in a nonstandard form. We extend our discussion to the use of grammars with additional information (such as evolutionary information) to improve the quality of predictions. Finally, we provide a brief survey of programs that use stochastic context-free grammars for rna secondary structure prediction and modelling.

关键词： rna secondary structure prediction SCFGs CYK algorithm Inside–outside algorithm Pfold

来源：评论

学校读者我要写书评

暂无评论

Genome-Wide rna secondary structure prediction

Genome-Wide RNA Secondary Structure Prediction

引用

作者： Risa Karakida Kawaguchi Hisanori Kiryu

The information of rna secondary structure has been widely applied to the inference of rna function. However, a classical prediction method is not feasible to long rnas such as mrna due to the problems of computational time and numerical errors. To overcome those problems, sliding window methods have been applied while their results are not directly comparable to global rna structure prediction. In this chapter, we introduce ParasoR, a method designed for parallel computation of genome-wide rna secondary structures. To enable genome-wide prediction, ParasoR distributes dynamic programming (DP) matrices required for structure prediction to multiple computational nodes. Using the database of not the original DP variable but the ratio of variables, ParasoR can locally compute the structure scores such as stem probability or accessibility on demand. A comprehensive analysis of local secondary structures by ParasoR is expected to be a promising way to detect the statistical constraints on long rnas. less

关键词： rna secondary structure prediction rna secondary structure Analysis rna structure prediction rna secondary structure Visualization rna secondary structure Genome-wide Parallel computation Local structure Maximal span constraint

来源：评论

学校读者我要写书评

暂无评论

Recent trends in rna informatics: a review of machine learning and deep learning for rna secondary structure prediction and rna drug discovery

引用

BRIEFINGS IN BIOINFORMATICS 2023年第4期24卷 bbad186页

作者： Sato, Kengo Hamada, Michiaki Senju Asahi cho Adachi ku Tokyo 1208551 Japan Tokyo Denki Univ Tokyo Japan Waseda Univ Tokyo Japan Natl Inst Adv Ind Sci & Technol Tokyo Japan Grad Sch Med Nippon Med Sch Tokyo Japan

Computational analysis of rna sequences constitutes a crucial step in the field of rna biology. As in other domains of the life sciences, the incorporation of artificial intelligence and machine learning techniques into rna sequence analysis has gained significant traction in recent years. Historically, thermodynamics-based methods were widely employed for the prediction of rna secondary structures;however, machine learning-based approaches have demonstrated remarkable advancements in recent years, enabling more accurate predictions. Consequently, the precision of sequence analysis pertaining to rna secondary structures, such as rna-protein interactions, has also been enhanced, making a substantial contribution to the field of rna biology. Additionally, artificial intelligence and machine learning are also introducing technical innovations in the analysis of rna-small molecule interactions for rna-targeted drug discovery and in the design of rna aptamers, where rna serves as its own ligand. This review will highlight recent trends in the prediction of rna secondary structure, rna aptamers and rna drug discovery using machine learning, deep learning and related technologies, and will also discuss potential future avenues in the field of rna informatics.

关键词： rna informatics rna secondary structure prediction rna-based therapeutics

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：