检索结果-内蒙古大学图书馆

ieee Conference on computational intelligence in bioinformatics and computational biology

作者： Hemalatha, N. Rajesh, M. K. Narayanan, N. K. St Aloysius Coll AIMIT Dept Comp Sci Mangalore India Cent Plantat Crops Res Inst Kasargod Kerala India Kannur Univ Dept Informat Sci Technol Kannur Kerala India

ISBN: (纸本)9781479945351

Plant development and crop yield are highly influenced by temperature. High temperature negatively affects different stages of plant development in rice, mainly booting and flowering. Identifying candidate genes associated with high-temperature stress response may provide knowledge for the improvement of heat tolerance in rice. As the rice genome sequencing has already been undertaken, a major work challenge is annotating proteins and decoding their functionalities. MAP kinase (MAPK) proteins are involved in signaling various abiotic and biotic stresses, like temperature stress or drought, wounding and pathogen infection. Moreover, MAPKs have also been implicated in cell cycle and developmental processes. In this study, an attempt has been made in developing a MAP kinase prediction tool for rice, MapPred. The computational approach has been developed using Sequential Minimum Optimization (SMO) algorithm in Weka workbench, and a sensitivity of 100% was obtained using dipeptide method. MapPred was also tested with three plants, namely Arabidopsis, maize and tomato to prove that developed tool has higher accuracy with rice than other plants which further proves the higher prediction accuracy of species-specific tools. Prediction performance of MapPred was evaluated using cross validation, independent data test and leave one out validation. Our experimental results demonstrated that proposed algorithm based on dipeptide method could be very effective in the computational approach for predicting MAPK proteins in Oryza ***.

关键词： Mitogen-Activated Protein Kinases Plant Development Thermotolerance Rice computational methods Genome stress response Thermal stresses Dipeptides

来源：评论

学校读者我要写书评

暂无评论

LMP forecasting with prefiltered Gaussian process

LMP forecasting with prefiltered Gaussian process

引用

2014 ieee symposium Series on computational intelligence, ieee SSCI 2014 - 2014 ieee symposium on computational intelligence Applications in Smart Grid, CIASG 2014

作者： Mori, Hiroyuki Nakano, Kaoru Dept. of Network Design Meiji University Nakano-ku Tokyo161-8525 Japan Dept. of Electronics and Bioinformatics Meiji University Tama-ku Kawasaki214-8571 Japan

ISBN: (纸本)9781479945474

In this paper, a new method is proposed for Locational Marginal Pricing (LMP) forecasting in Smart Grid. The marginal cost is required to supply electricity to incremental loads in case where a certain node increases power demands in a balanced power system. LMP plays an important role to maintain economic efficiency in power markets in a way that electricity flows from a low-cost area to high-cost one and the transmission network congestion in alleviated. The power market players are interested in maximizing the profits and minimizing the risks through selling and buying electricity. As a result, it is of importance to obtain accurate information on electricity pricing forecasting in advance so that their desire is reflected. This paper presents the Gaussian Process (GP) technique that comes from the extension of Support Vector Machine (SVM) that hierarchical Bayesian estimation is introduced to express the model parameters as the probabilistic variables. The advantage is that the model accuracy of GP is better than others. In this paper, GP is integrated with the k-means method of clustering to improve the performance of GP. Also, this paper makes use of the Mahalanobis kernel in GP rather than the Gaussian one so that GP is generalized to approximate nonlinear systems. The proposed method is successfully applied to real data of ISO New England in USA. © 2014 ieee.

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

Dependency Network Methods for Hierarchical Multi-label Classification of Gene Functions 5

Dependency Network Methods for Hierarchical Multi-label Clas...

引用

ieee symposium on computational intelligence and Data Mining (CIDM)

作者： Fabris, Fabio Freitas, Alex A. Univ Kent Sch Comp Canterbury CT2 7NF Kent England

ISBN: (纸本)9781479945184

Hierarchical Multi-label Classification (HMC) is a challenging real-world problem that naturally emerges in several areas. This work proposes two new algorithms using a Probabilistic Graphical Model based on Dependency Networks (DN) to solve the HMC problem of classifying gene functions into pre-established class hierarchies. DNs are especially attractive for their capability of using traditional, "out-of-the-shelf", classification algorithms to model the relationship among classes and for their ability to cope with cyclic dependencies, resulting in greater flexibility with respect to Bayesian Networks. We tested our two algorithms: the first is a stand-alone Hierarchical Dependency Network (HDN) algorithm, and the second is a hybrid between the HDN and the Predictive Clustering Tree (PCT) algorithm, a well-known classifier for HMC. Based on our experiments, the hybrid classifier, using SVMs as base classifiers, obtained higher predictive accuracy than both the standard PCT algorithm and the HDN algorithm, considering 22 bioinformatics datasets and two out of three predictive accuracy measures specific for hierarchical classification (AU((PRC) over bar) and (AUPRC) over bar (w)).

关键词： Bayes methods bioinformatics genetics network theory (graphs) pattern classification support vector machines Bayesian networks DN HMC problem PCT algorithm SVM base classifiers bioinformatics datasets class hierarchies cyclic dependencies gene function classification hierarchical multilabel classification hybrid algorithm hybrid classifier out-of-the-shelf classification algorithms predictive accuracy measures predictive clustering tree algorithm probabilistic graphical model real-world problem stand-alone HDN algorithm stand-alone hierarchical dependency network algorithm standard PCT algorithm Accuracy Classification algorithms Clustering algorithms Inference algorithms Markov processes Prediction algorithms Vectors Support Vector Network Sorting algorithm Inference algorithms Bayesian networks Prediction algorithms Clustering algorithms dwarf novae Bayes methods Pattern recognition bioinformatics Markov chain hardware motion compensation Genetics World problems Hierarchical

来源：评论

学校读者我要写书评

暂无评论

Gender classification of subjects from cerebral blood flow changes using Deep Learning 5

Gender classification of subjects from cerebral blood flow c...

引用

ieee symposium on computational intelligence and Data Mining (CIDM)

作者： Hiroyasu, Tomoyuki Hanawa, Kenya Yamamoto, Utako Doshisha Univ Kyoto 1-3 Tatara Miyakodani Kyotanabe Kyoto 6100394 Japan Doshisha Univ Grad Sch Kyouto Kyotanabe Kyoto 6100394 Japan

ISBN: (纸本)9781479945184

In this study, using Deep Learning, the gender of subjects is classified the cerebral blood flow changes that are measured by fNIRS. It is reported that cerebral blood flow changes are triggered by brain activities. Thus, if this classification has a high searching accuracy, gender classification should be related to brain activities. In the experiment, fNIRS data are derived from subjects who perform a memory task in white noise environment. From the results, it is confirmed that the learning classifier exhibits high accuracy. This fact suggests that there exists a relation between cerebral blood flow changes and biological information.

关键词： biology computing brain gender issues haemorheology infrared spectroscopy learning (artificial intelligence) white noise biological information brain activities cerebral blood flow changes deep learning fNIRS functional near infrared spectroscopy gender classification white noise environment Blood Educational institutions Neurons Noise reduction Time measurement Time series analysis White noise white noise Cerebral Blood Flow Hemorheology Information, biological functional near-infrared spectroscopy biology computing time measurement gender issue Educational institutions Noise reduction time series analysis noise environment brain activity infrared spectroscopy Cerebrum

来源：评论

学校读者我要写书评

暂无评论

An Efficient computational intelligence Technique for Classification of Protein Sequences

An Efficient Computational Intelligence Technique for Classi...

引用

International Conference on Computer and Information Sciences (ICCOINS)

作者： Iqbal, Muhammad Javed Faye, Ibrahima Said, Abas Md Samir, Brahim Belhaouari Univ Teknol PETRONAS Dept Comp & Informat Sci Tronoh Malaysia Univ Teknol PETRONAS Dept Fundamental & Appl Sci Tronoh Malaysia Alfaisal Univ Coll Sci Riyadh Saudi Arabia

ISBN: (纸本)9781479943913

Many artificial intelligence techniques have been developed to process the constantly increasing volume of data to extract meaningful information from it. The accurate annotation of the unknown protein using the classification of the protein sequence into an existing superfamily is considered a critical and challenging task in bioinformatics and computational biology. This classification would be helpful in the analysis and modeling of unknown protein to determine their structure and function. In this paper, a frequency-based feature encoding technique has been used in the proposed framework to represent amino acids of a protein's primary sequence. The technique has considered the occurrence frequency of each amino acid in a sequence. Popular classification algorithms such as decision tree, naive Bayes, neural network, random forest and support vector machine have been employed to evaluate the effectiveness of the encoding method utilized in the proposed framework. Results have indicated that the decision tree classifier significantly shows better results in terms of classification accuracy, specificity, sensitivity, F-measure, etc. The classification accuracy of 88.7% was achieved over the Yeast protein sequence data taken from the well-known UniProtKB database.

关键词： bioinformatics Feature encoding Data mining Superfamily Protein classification

来源：评论

学校读者我要写书评

暂无评论

Microarray Big Data Integrated Analysis to Identify Robust Diagnostic Signature for Triple Negative Breast Cancer

Microarray Big Data Integrated Analysis to Identify Robust D...

引用

ieee symposium SERIES ON computational intelligence

作者： Zaka, Masood U. H. Peng, Yonghong Sutton, Chris W. Univ Bradford Sch Engn & Informat Dept Comp Bradford BD7 1DP W Yorkshire England Univ Bradford Sch Life Sci Inst Canc Therapeut Bradford BD7 1DP W Yorkshire England

ISBN: (纸本)9781479945405

Triple negative breast cancers (TNBC) are clinically heterogeneous, an aggressive subtype with poor diagnosis and strong resistance to therapy. There is a need to identify novel robust biomarkers with high specificity for early detection and therapeutic intervention. Microarray gene expression-based studies have offered significant advances in molecular classification and identification of diagnostic/prognostic signatures, however sample scarcity and cohort heterogeneity remains area of concern. In this study, we performed integrated analysis on independent microarray big data studies and identified a robust 880-gene signature for TNBC diagnosis. We further identified 16-gene (OGN, ESR1, GPC3, LHFP, AGR3, LPAR1, LRRC17, TCEAL1, CIRBP, NTN4, TUBA1C, TMSB10, RPL27, RPS3A, RPS18, and NOSTRIN) that are associated to TNBC tissues. The 880-gene signature achieved excellent classification accuracy ratio on each independent expression data sets with overall average of 99.06%, is an indication of its diagnostic power. Gene ontology enrichment analysis of 880-gene signature shows that cell-cycle pathways/processes are important clinical targets for triple negative breast cancer. Further verification of 880-gene signature could provide additive knowledge for better understanding and future direction of triple negative breast cancer research.

关键词： Triple negative breast cancer Microarray Gene ontology enrichment analysis bioinformatics Gene signature

来源：评论

学校读者我要写书评

暂无评论

Deep learning of the tissue-regulated splicing code

引用

bioinformatics 2014年第12期30卷 121-129页

作者： Leung, Michael K. K. Xiong, Hui Yuan Lee, Leo J. Frey, Brendan J. Univ Toronto Dept Elect & Comp Engn Toronto ON M5S 3G4 Canada Univ Toronto Banting & Best Dept Med Res Toronto ON M5S 3E1 Canada Canadian Inst Adv Res Toronto ON M5G 1Z8 Canada

Motivation: Alternative splicing (AS) is a regulated process that directs the generation of different transcripts from single genes. A computational model that can accurately predict splicing patterns based on genomic features and cellular context is highly desirable, both in understanding this widespread phenomenon, and in exploring the effects of genetic variations on AS. Methods: Using a deep neural network, we developed a model inferred from mouse RNA-Seq data that can predict splicing patterns in individual tissues and differences in splicing patterns across tissues. Our architecture uses hidden variables that jointly represent features in genomic sequences and tissue types when making predictions. A graphics processing unit was used to greatly reduce the training time of our models with millions of parameters. Results: We show that the deep architecture surpasses the performance of the previous Bayesian method for predicting AS patterns. With the proper optimization procedure and selection of hyperparameters, we demonstrate that deep architectures can be beneficial, even with a moderately sparse dataset. An analysis of what the model has learned in terms of the genomic features is presented.

关键词： Algorithms Alternative Splicing Animals Artificial intelligence Bayes Theorem Genomics/methods Humans Mice Neural Networks (Computer) Sequence Analysis, RNA

来源：评论

学校读者我要写书评

暂无评论

Enzymatic Numerical P Systems for basic operations and sorting 7

Enzymatic Numerical P Systems for basic operations and sorti...

引用

Joint 7th International Conference on Soft Computing and Intelligent Systems (SCIS) and 15th International symposium on Advanced Intelligent Systems (ISIS)

作者： Maeda, Shohei Fujiwara, Akihiro Kyushu Inst Technol Grad Sch Comp Sci & Syst Engn Iizuka Fukuoka 8208502 Japan

ISBN: (纸本)9781479959556

Membrane computing, which is a computational model inspired by the structures and behaviors of living cells, has considerable attention as one of non-silicon based computing. In the present paper, we propose EN P systems for basic operations, and sorting. We first propose three EN P systems for computing three logic operations, OR, AND, and EX-OR functions. All of the EN P systems work in a constant number of steps. Next, we propose two EN P systems that operates as a half adder and a full adder, and then, propose two EN P system for additions of two binary numbers and n binary numbers. We show the EN P systems for two additions work in O(m) steps and O(nm) steps, respectively. Finally, we propose two EN P systems for sorting. We propose an EN P system for compare-and-exchange operation. Then, using the EN P system as sub-systems, we propose an EN P system for sorting n numbers, and show that the EN P system works in O(n) steps.

关键词： biocomputing AND functions EN P systems EX-OR functions O(m) steps O(nm) steps compare-and-exchange operation enzymatic numerical P systems membrane computing Adders Biochemistry computational modeling Numerical models Production Protocols Sorting computational Molecular biology sorting programs adders biology computing computational modeling Basic Operations numerical models grade biochemistry protocols Phosphorus P Blood-Group System Binary digits

来源：评论

学校读者我要写书评

暂无评论

9th Brazilian symposium on bioinformatics, BSB 2014

引用

9th Brazilian symposium on bioinformatics, BSB 2014

ISBN: (纸本)9783319124179

The proceedings contain 18 papers. The special focus in this conference is on bioinformatics and computational biology. The topics include: An extensible framework for genomic and metagenomic analysis;on the multichromosomal Hultman number;towards an ensemble learning strategy for metagenomic gene prediction;a metagenomic systems biology computational;an adaptable and extensible electronic health record;influence of sequence length in promoter prediction performance;evolution of genes neighborhood within reconciled phylogenies;dynamic programming for set data types;using Binary Decision Diagrams (BDDs) for memory optimization in Basic Local Alignment Search Tool (BLAST);a multi-objective evolutionary algorithm for improving multiple sequence alignments;BION2SEL;structural comparative analysis of secreted NTPDase models of Schistosoma mansoni and homo sapiens;length and symmetry on the sorting by weighted inversions problem;storage policy for genomic data in hybrid federated clouds;genome-wide identification of non-coding RNAs in Komagatella pastoris str. GS115;multi-scale simulation of T helper lymphocyte differentiation;scaffolding of ancient contigs and ancestral reconstruction in a phylogenetic framework and quality metrics for benchmarking sequences comparison tools.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Arithmetic computation in the tile assembly model: Inversion over finite field GF(2n)

Arithmetic computation in the tile assembly model: Inversion...

引用

ieee symposium on computational intelligence and bioinformatics and computational biology (CIBCB)

作者： Yongnan Li Limin Xiao School of Computer Science and Engineering Beihang University Beijing China

Tile assembly model is a formal model of DNA computing, which uses DNA, biochemistry and molecular biology instead of the traditional silicon-based computer technologies. DNA-based cryptography is an interdisciplinary area combining classical cryptography and molecular computing. This paper proposes how the tile assembly process could be used for computing inversion over finite field GF(2 n ), a basic mathematic operation often used in cryptography. This computing system is designed based on two other parallel molecular computing systems proposed in our previous works. Time complexity of this system is Θ(n 2 ) and the space complexity is Θ(n 4 ).

关键词： Tiles Assembly computational modeling Assembly systems Cryptography DNA computing DNA

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：