检索结果-内蒙古大学图书馆

2008 ieee symposium on computational intelligence in bioinformatics and computational biology, CIBCB '08

ISBN: (纸本)9781424417780

The proceedings contain 39 papers. The topics discussed include: classifying synthetic and biological DNA sequences with side effect machines;a hybrid clustering/evolutionary algorithm for RNA folding;an information theoretic approach for the discovery of irregular and repetitive patterns in genomic data;PCA-based linear combinations of oligonucleotide frequencies for metagenomic DNA fragment binning;exploring chaos automata for protein sequences;cancer classification with incremental gene selection based on DNA microarray data;temporal and structural analysis of biological networks in combination with microarray data;a graph-based representation of gene expression profiles in DNA microarrays;network motifs in context: an exploration of the evolution of oscillatory dynamics in transcriptional networks;and evolution strategy with greedy probe selection heuristics for the non-unique oligonucleotide probe selection problem.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Signature Discovery for Personalized Medicine

Signature Discovery for Personalized Medicine

引用

11th ieee International Conference on intelligence and Security Informatics (ieee ISI)

作者： Yeung, Ka Yee Univ Washington Seattle WA 98195 USA

ISBN: (纸本)9781467362139;9781467362146

Various types of genome-wide data, such as sequence and gene expression data, have been generated and are available from public databases. These genome-wide data present major computational challenges as the number of variables far exceeds the number of observations. Many computational tools have been developed for the analyses of these high dimensional data, and these methods have led to improved understanding of molecular biology. In particular, signature discovery (also known as variable selection or feature selection), a machine learning technique in which subsets of variables are selected to build robust models, are useful in mining these highdimensional functional genomic data. In this paper, we will review the applications of signature discovery methods in mining these high dimensional data. Specifically, we will focus on two applications, namely, the identification of signature genes predictive of disease phenotypes and the inference of regulatory networks. Signature genes predictive of disease phenotypes can be potentially used in the diagnosis and prognosis of diseases. Regulatory networks that capture the gene-to-gene influences can be used to provide the context of therapeutic intervention.

关键词： bioinformatics Machine learning Regression analysis Systems biology

来源：评论

学校读者我要写书评

暂无评论

Intellectual Property Protection for bioinformatics and computational intelligence

Intellectual Property Protection for Bioinformatics and Comp...

引用

ieee symposium on computational intelligence in bioinformatics and computational biology

作者： Dennis Fernandez Antonia Maninang Shumpei Kobayashi Fernandez & Associates LLP Stanford Hospital University of California

ISBN: (纸本)9781467358743

bioinformatics and computational biology are two fast-growing fields that require the direct application of computational intelligence. The American patent system is currently going through the biggest reformation since the passage of Patent Act of 1952, and thus intellectual property rights (IPR) and strategies continue to be increasingly vital in these fields. In order to better understand the status quo of intellectual property (IP) specifically in the fields of biology that apply computational intelligence, basic IP definitions, recent IP developments, and advanced protection strategies are presented and discussed.

关键词： computational biology bioinformatics Intellectual Property America Invents Act Patent Portfolio

来源：评论

学校读者我要写书评

暂无评论

Enhanced Probabilistic Latent Semantic Analysis with Weighting Schemes to Predict Genomic Annotations

Enhanced Probabilistic Latent Semantic Analysis with Weighti...

引用

ieee 13th International Conference on bioinformatics and Bioengineering (BIBE)

作者： Pinoli, Pietro Chicco, Davide Masseroli, Marco Politecn Milan Dipartimento Elettron Informaz & Bioingn Milan Italy

ISBN: (纸本)9781479931637

Genomic annotations with functional controlled terms, such as the Gene Ontology (GO) ones, are paramount in modern biology. Yet, they are known to be incomplete, since the current biological knowledge is far to be definitive. In this scenario, computational methods that are able to support and quicken the curation of these annotations can be very useful. In a previous work, we discussed the benefits of using the Probabilistic Latent Semantic Analysis algorithm in order to predict novel GO annotations, compared to some Singular Value Decomposition (SVD) based approaches. In this paper, we propose a further enhancement of that method, which aims at weighting the available associations between genes and functional terms before using them as input to the predictive system. The tests that we performed on the annotations of human genes to GO functional terms showed the efficacy of our approach.

关键词： biology computing genomics ontologies (artificial intelligence)

来源：评论

学校读者我要写书评

暂无评论

Reconfiguration-Based Implementation of SVM Classifier on FPGA for Classifying Microarray Data

Reconfiguration-Based Implementation of SVM Classifier on FP...

引用

35th Annual International Conference of the ieee-Engineering-in-Medicine-and-biology-Society (EMBC)

作者： Hussain, Hanaa M. Benkrid, Khaled Seker, Huseyin Publ Author Appl Educ & Training Dept Elect Engn Coll Technol Studies Shuwaikh 70654 Kuwait Univ Edinburgh Sch Engn & Elect Edinburgh EH9 3JL Midlothian Scotland De Montfort Univ Ctr Computat Intelligence Bio Hlth Informat Res Grp Leicester LE1 9BH Leics England

ISBN: (纸本)9781457702167

Classifying Microarray data, which are of high dimensional nature, requires high computational power. Support Vector Machines-based classifier (SVM) is among the most common and successful classifiers used in the analysis of Microarray data but also requires high computational power due to its complex mathematical architecture. Implementing SVM on hardware exploits the parallelism available within the algorithm kernels to accelerate the classification of Microarray data. In this work, a flexible, dynamically and partially reconfigurable implementation of the SVM classifier on Field Programmable Gate Array (FPGA) is presented. The SVM architecture achieved up to 85x speed-up over equivalent general purpose processor (GPP) showing the capability of FPGAs in enhancing the performance of SVM-based analysis of Microarray data as well as future bioinformatics applications.

关键词： bioinformatics Field Programmable Gate Array General Purpose Processor Microarray Support Vector Machine Dynamic Partial Reconfiguration

来源：评论

学校读者我要写书评

暂无评论

Parallel processing of approximate sequence matching using disk-based suffix tree on multi-core CPU

Parallel processing of approximate sequence matching using d...

引用

2013 ieee 6th International Workshop on computational intelligence and Applications, IWCIA 2013

作者： Watanuki, Yosuke Tamura, Keiichi Kitakami, Hajime Takahashi, Yoshihumi Graduate School of Information Sciences Hiroshima City University Hiroshima Japan

ISBN: (纸本)9781467357265

Suffix trees, which are trie structures that present the suffixes of given sequences (e.g., strings), are widely used for sequence search in different application domains such as, text data mining, web intelligence, bioinformatics and computational biology. In particular, suffix trees are useful in bioinformatics applications, because they can search similar sub-sequences and extract frequent sequence patterns efficiently. In recent years, efficient construction of a suffix tree that allows faster sequence searches has become one of the most important challenges, because the number and size of the data that are stored in sequence databases have been increasing exponentially. This paper proposes a novel parallelization model for approximate sequence matching that uses disk-based suffix trees, which are built on hard disks not on memory, on a multi-core CPU. In the proposed parallelization model, we divide an entire sequence database into two or more sub-databases called partitions. For each partition, we build a suffix tree and define a task as an approximate sequence matching on one suffix tree. Moreover, the proposed parallelization model involves a multiple buffering management system to avoid conflicts among CPU-cores. We evaluated the proposed parallelization model using an actual amino acid sequence database on a PC. The experimental results show a substantial improvement in computation performance. © 2013 ieee.

关键词： bioinformatics

来源：评论

学校读者我要写书评

暂无评论

Designing predictors of halophilic and non-halophilic proteins using support vector machines

Designing predictors of halophilic and non-halophilic protei...

引用

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

作者： Hui-Ling Huang Yerukala Sathipati Srinivasulu Phasit Charoenkwan Hua-Chin Lee Shinn-Ying Ho Department of Biological Science and Technology Institute of Bioinformatics and Systems Biology National Chiao Tung University Hsinchu Taiwan Institute of Bioinformatics and Systems Biology National Chiao Tung University Hsinchu Taiwan

ISBN: (纸本)9781467358743

Finding the molecular features causes the halophilicity in the halostable organisms is helpful to understand the halophilic adaption. In this study, we proposed a prediction method for halophilic proteins by using a machine learning method. The stages of this study are six-fold. First, we establish a non-redundant dataset of the halophilic proteins, collected from NCBI, Uniprotkb and EMBL-EBI databases. The dataset consists of 245 positive and negative proteins with sequence identity

关键词： Proteins Amino acids Support vector machines Accuracy bioinformatics Organisms Genetic algorithms

来源：评论

学校读者我要写书评

暂无评论

Mapping of DNA sequences using Hidden Markov Model Self Organizing Maps

Mapping of DNA sequences using Hidden Markov Model Self Orga...

引用

ieee symposium on computational intelligence in bioinformatics and computational biology

作者： Hiroshi Dozono Gen Niina Department of Advance Technology Fusion Saga Univeristy

ISBN: (纸本)9781467358743

Recently, next generation sequencing techniques have begun to produce huge amounts of sequencing data. To analyze these data, an efficient method that can handle large amounts of information is required. In this paper, we proposed a method for classifying sets of DNA sequences by using a hidden Markov model self-organizing map. For this purpose, a learning algorithm that requires low computational costs was developed. The availability of this method was examined in experiments classifying DNA sequences of various types of genes.

关键词： Hidden Markov models DNA Probes bioinformatics Context Vectors Algorithm design and analysis

来源：评论

学校读者我要写书评

暂无评论

An Optimization Rule for In Silico Identification of Targeted Overproduction in Metabolic Pathways

引用

ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2013年第4期10卷 914-926页

作者： Das, Mouli Murthy, C. A. De, Rajat K. Indian Stat Inst Machine Intelligence Unit Kolkata 700108 W Bengal India

In an extension of previous work, here we introduce a second-order optimization method for determining optimal paths from the substrate to a target product of a metabolic network, through which the amount of the target is maximum. An objective function for the said purpose, along with certain linear constraints, is considered and minimized. The basis vectors spanning the null space of the stoichiometric matrix, depicting the metabolic network, are computed, and their convex combinations satisfying the constraints are considered as flux vectors. A set of other constraints, incorporating weighting coefficients corresponding to the enzymes in the pathway, are considered. These weighting coefficients appear in the objective function to be minimized. During minimization, the values of these weighting coefficients are estimated and learned. These values, on minimization, represent an optimal pathway, depicting optimal enzyme concentrations, leading to overproduction of the target. The results on various networks demonstrate the usefulness of the methodology in the domain of metabolic engineering. A comparison with the standard gradient descent and the extreme pathway analysis technique is also performed. Unlike the gradient descent method, the present method,

关键词： Local minima Newton-Raphson method underdetermined problem metabolic pathways learning parameter

来源：评论

学校读者我要写书评

暂无评论

Protein Secondary Structure Prediction Using Support Vector Machines (SVMs)

Protein Secondary Structure Prediction Using Support Vector ...

引用

ieee International Conference on Machine intelligence Research and Advancement (ICMIRA)

作者： Patel, Mayuri Shah, Hitesh GH Patel Coll Engn & Technol Dept Informat Technol Vallabh Vidyanagar 388120 Gujarat India GH Patel Coll Engn & Technol Elect & Commun Engn Dept Vallabh Vidyanagar 388120 Gujarat India

ISBN: (纸本)9780769550138

bioinformatics or computational biology is field of science in which biology, computer science and information technology merges into a single discipline. In modern computation biology, protein secondary structure prediction is a major problem. Secondary structure prediction is depends on its amino acid sequence. Current studies prefer machine learning techniques for classification and regression task. Recently many researchers used various data mining and machine learning tool for protein structure prediction. Our intention is to use model based (i.e., supervised learning) approach for protein secondary structure prediction and our objective is to enhance the prediction of 2D protein structure problem using advance machine learning techniques like, linear and non-linear support vector machine with different kernel functions. The datasets used for this problem are Protein Data Bank (PDB) sets, which is based on structural classification of protein (SCOP), RS126 and CB513.

关键词： bioinformatics CB513 Feature Selection Protein Data Bank (PDB) RS126

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：