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检索条件"任意字段=IEEE Computational Systems Bioinformatics Conference"

共 878 条记录，以下是101-110 订阅

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Outlier detection for microarray data

Outlier detection for microarray data

引用

2nd International conference on bioinformatics and Biomedical Engineering, iCBBE 2008

作者： Liu, Wei-Min Biostatistics and Bioinformatics Roche Molecular Systems Inc. Pleasanton CA United States

ISBN: (纸本)9781424417483

It is important to estimate the impact of protocol changes on expression profiles based on microarray data and eventually on classification accuracy. This estimation should be based on experiments with replicates. Therefore, it is important to detect outliers in such experiments. This paper proposes an outlier detection algorithm for replicate microarray experiments. © 2008 ieee.

关键词： Gene expression

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Signal processing in bioinformatics 14

Signal processing in bioinformatics

引用

14th International conference on Digital Signal Processing (DSP 2002)

作者： Costa, LD Univ Sao Paulo Inst Fis Sao Carlos BR-13560970 Sao Carlos SP Brazil

ISBN: (纸本)0780375033

The ongoing reunification of the several biological areas - especially genetics, evolution, development and ecology - into a coherent whole will rely increasingly on the application of mathematic-computational concepts and techniques, which characterizes the new area of bioinformatics. The current article addresses such a tendency from the specific perspective of signal/image processing and analysis, outlining the main problems of interest and their specific demands. Among the several related approaches, special attention is focused on DNA chip image analysis, 3D gene expression characterization, the dynamical evolution of biological shapes, and dynamic systems models.

关键词： bioinformatics

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Generating Peptide Sequence Tags for Peptide Identification via Tandem Mass Spectrometry

Generating Peptide Sequence Tags for Peptide Identification ...

引用

9th ieee International conference on bioinformatics and BioEngineering

作者： Yu, Changyong Wang, Guoren Zhao, Yuhai Mao, Keming Wu, Junjie Zhai, Wendan Northeastern Univ Minist Educ Key Lab Med Image Comp Shenyang 110004 Peoples R China Northeastern Univ Sch Informat Sci & Engn Shenyang 110004 Peoples R China

ISBN: (纸本)9781424442942

Large scale, rapid and accurate protein identification is the crucial basis for further protein analysis in computational proteomics. Searching protein database by use of the protein tandem mass spectra has been a standard solution for solving this problem. Though several algorithms have been proposed, more sensitive and accurate approaches are still needed. In this paper, an effective database search approach is proposed. Prior to searching sequence database, an approach based on a graph-theoretic model is proposed to infer the peptide sequence tag (PST) from the tandem mass spectra data which is the partial sequence of the peptide. Also, an index approach for the protein sequence database is proposed for speeding up the database search and filtering out the incorrect protein sequences. Then, a novel scoring method for evaluating the match between the peptide sequence tag and the protein sequence is proposed for improving the accuracy of the database search result. Finally, we develop an algorithm for solving the problem and implement it as a computer program Pep Check. All the results of Pep Check are compared with those of the famous algorithms. Experimental results demonstrate that Pep Check is as accurate as or more accurate than them with the test datasets.

关键词： Peptides Mass spectroscopy Databases Large-scale systems bioinformatics Biomedical engineering Proteomics Protein sequence Amino acids Spine

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MOSCA: An Automated Pipeline for Integrated Metagenomics and Metatranscriptomics Data Analysis 12th

MOSCA: An Automated Pipeline for Integrated Metagenomics and...

引用

12th International conference on Practical Applications of computational Biology and bioinformatics (PACBB)

作者： Sequeira, Joao Carlos Rocha, Miguel Alves, Maria Madalena Salvador, Andreia Ferreira Univ Minho Ctr Biol Engn Campus Gualtar Braga Portugal

ISBN: (纸本)9783319987026;9783319987019

Metagenomics (MG) and Metatranscriptomics (MT) approaches open new perspectives on the interpretation of biological systems composed by complexmicrobial communities. Dealing with large sequencing datasets, to extract the desired information and interpret the results are big challenges associated with meta-omics studies. There are several bioinformatics pipelines forMG data analysis and less toMT. Up to date, none performs a complete analysis integrating both MG and MT data, including the assembly of reads into contigs, functional and taxonomic annotation of identified genes, differential gene expression analysis and the comparison of multiple samples. Here, we present Meta-Omics Software for Community Analysis (MOSCA) that was designed with this purpose. It integrates RNA-Seq analysis with Whole Genome Sequencing as reference. Raw sequencing reads are submitted to preprocessing for quality trimming and rRNA removal, and assembled into contigs, which afterwards are annotated by using a reference database. MOSCA performs differential gene expression and provides graphical visualization of the results and comparison of multiple samples. Validation and reproducibility of the pipeline was obtained by using simulated MG and MT datasets.

关键词： Metagenomics Metatranscriptomics bioinformatics pipeline Community analysis RNA-Seq Whole genome sequencing

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Single-Cell RNA-Seq Data Clustering: Highlighting computational Challenges and Considerations

Single-Cell RNA-Seq Data Clustering: Highlighting Computatio...

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2023 ieee International conference on bioinformatics and Biomedicine, BIBM 2023

作者： Nossier, Mohamad Moussa, Sherin M. Badr, Nagwa Ain Shams University Faculty of Computer and Information Sciences Program of Bioinformatics Department of Information Systems Cairo11566 Egypt Cairo11837 Egypt Ain Shams University Faculty of Computer and Information Sciences Department of Information Systems Cairo11566 Egypt

ISBN: (纸本)9798350337488

Recent advancements in single-cell transcriptomics have revolutionized data generation, resulting in the production of vast amounts of single-cell RNA sequencing (scRNA-seq) data. The intricate biology of tissues and organs may be better understood through the analysis of such data, which aims to not only identify known cell types but also uncover novel ones. In this paper, we outline the standard single-cell RNA-seq data analytic process with particular focus on the critical role of unsupervised clustering, since it has a significant influence over subsequent analyses and the resulting biological insights. However, navigating the computational landscape for clustering single-cell transcriptomic data poses significant challenges. Thus, in this article, we provide a simplified exploration of key computational challenges and considerations within the realm of scRNA-seq data clustering, emphasizing the application and relevance of selected unsupervised methods in current research, while acknowledging the potential for further investigation of the biological aspects in this field. Furthermore, we present recommendations on how such challenges may be addressed. This work not only sheds light on the complexities of scRNA-seq data cluster analysis, but also provides insights into how to effectively address and surmount these challenges. © 2023 ieee.

关键词： Clustering Data Analysis RNA-sequencing Single-cell Transcriptomics Unsupervised learning

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SAMasGC: Sequencing Analysis with a Multiagent System and Grid Computing

SAMasGC: Sequencing Analysis with a Multiagent System and Gr...

引用

6th International conference on Practical Applications of computational Biology and bioinformatics (PACBB)

作者： Gonzalez, Roberto Zato, Carolina Benito, Rocio Hernandez, Maria Hernandez, Jesus M. De Paz, Juan F. Univ Salamanca Dept Comp Sci Plaza Merced S-N E-37008 Salamanca Spain Univ Salamanca CSIC Ctr Canc Res IBMCC Salamanca Spain Hosp Univ Salamanca Hematol Serv Salamanca Spain

ISBN: (纸本)9783642288388

Advances in bioinformatics have contributed towards a significant increase in available information. Information analysis requires the use of distributed computing systems to best engage the process of data analysis. This study proposes a multiagent system that incorporates grid technology to facilitate distributed data analysis by dynamically incorporating the roles associated to each specific case study. The system was applied to genetic sequencing data to extract relevant information about insertions, deletions or polymorphisms.

关键词： Multiagent system Grid Computing genetic sequencing distributed computing bioinformatics

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Large-scale discovery of regulatory motifs involved in alternative splicing

Large-scale discovery of regulatory motifs involved in alter...

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7th ieee International conference on bioinformatics and Bioengineering, BIBE

作者： Zhao, Sihui Kim, Jihye Heber, Steffen Bioinformatics Research Center North Carolina State University Raleigh NC United States

ISBN: (纸本)1424415098

Alternative splicing is a highly important process in many eukaryotic organisms, but surprisingly little is known about its regulation. Often, this process involves cis-regulatory DNA motifs located within the alternative spliced exons or the flanking introns. To discover such regulatory motifs, we reanalyzed a dataset containing ∼3000 skipped exons and their relative expression levels in ten mouse tissues provided by Pan and colleagues [1]. We clustered the skipped exons by their expression profiles and applied MEME (Multiple Em for Motif Elicitation) to build a motif dictionary from each cluster. The discovered motifs were validated by comparing with known regulatory elements and by examining positional bias. By incorporating the levels of alternative splicing, our study provides not only solid targets for further experiments, but also a computational framework applicable in other organisms. The motif dictionary is available at http://***/~szhao3/***. ©2007 ieee.

关键词： bioinformatics

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2004 2nd International ieee conference Intelligent systems - Proceedings Volume 1

2004 2nd International IEEE Conference Intelligent Systems -...

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2004 2nd International ieee conference 'Intelligent systems' - Proceedings

ISBN: (纸本)0780382781

The proceedings contain 63 papers from the 2004 2nd International ieee conference Intelligent systems - Volume 1. The topics discussed include: intelligent decision making and information fusion;computational intelligence approach to real-world cooperative vehicle dispatching problem;bioinformatics: a knowledge engineering approach;local minima free neural network learning and automatic text summarization with neural networks.

关键词： Intelligent structures

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引用

2017 ieee International conference on bioinformatics and Biomedicine, BIBM 2017

作者： Sicilia, Rosa Giudice, Stella Lo Pei, Yulong Pechenizkiy, Mykola Soda, Paolo Unit of Computer Systems and Bioinformatics Department of Engineering Università Campus Bio-Medico di Roma Via Alvaro del Portillo 21 Rome00128 Italy 2M Engineering Ltd Valkenswaard Netherlands Department of Mathematics and Computer Science Eindhoven University of Technology Eindhoven5600 MB Netherlands

ISBN: (纸本)9781509030491

In the last years social networks have emerged as a critical mean for information spreading. In spite of all the positive consequences this phenomenon brings, unverified and instrumentally relevant information statements in circulation, named as rumours, are becoming a potential threat to the society. Recently, there have been several studies on topic-independent rumour detection on Twitter. In this paper we present a novel rumour detection system which focuses on a specific topic, that is health-related rumours on Twitter. To this aim, we constructed a new subset of features including influence potential and network characteristics features. We tested our approach on a real dataset observing promising results, as it is able to correctly detect about 89% of rumours, with acceptable levels of precision. © 2017 ieee.

关键词： Health

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A Scalable bioinformatics Analysis Platform based on Microservices Architecture

A Scalable Bioinformatics Analysis Platform based on Microse...

引用

2019 ieee International Research conference on Smart Computing and systems Engineering, SCSE 2019

作者： Rajapaksa, S. Wickramarachchi, A. Mallawaarachchi, V. Rasanjana, W. Perera, I. Meedeniya, D. Department of Computer Science and Engineering University of Moratuwa Sri Lanka

ISBN: (纸本)9789557041216

With the advancement of technologies, web services play a significant role to maintain infrastructure in healthcare domain due to the increasing demand of performance. In such systems, adoption of novel technologies is necessary to increase the productivity and reduce the burden of maintenance associated with legacy systems. Microservices architecture has become prominent in deploying server-side enterprise applications by allowing maintainable functionalities. However, it is challenging to utilize microservices in the domain of bioinformatics, although it enables independent process execution and maintenance. This paper introduces the utilization of microservices architecture to build an optimized platform for bioinformatics analyses. We present a hybrid architecture that consists of different hardware platforms to execute accelerated computational services, independently. The core communication is based on an Application Programming Interface (API) gateway. Furthermore, the paper presents the evaluation of results related to the performance of the proposed solution under varying biological sequences as inputs and algorithms. © 2019 University of Kelaniya.

关键词： Legacy systems

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