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ieee symposium on computational intelligence in Big Data (CIBD)

作者： Wanting Liu Yonghong Peng Desmond J Tobin School of Engineering and Informatics & Centre of Skin Sciences University of Bradford Great Britain School of Engineering and Informatics University of Bradford Great Britain Centre of Skin Sciences University of Bradford Great Britain

The advance of high throughput biotechnology enables the generation of large amount of biomedical data. The microarray is increasingly a popular approach for the detection of genome-wide gene expression. Microarray data have thus increased significantly in public accessible database repositories, which provide valuable big data for scientific research. To deal with the challenge of microarray big data collected in different research labs using different experimental set-ups and on different bio-samples, this paper presents a primary study to evaluate the impact of two important factors (the origin of bio-samples and the quality of microarray data) on the integrated analytics of multiple microarray data. The aim is to enable the extraction of reliable and robust gene biomarkers from microarray big data. Our work showed that in order to enhance biomarker discovery from microarray big data (i) it is necessary to treat the microarray data differently in terms of their quality, (ii) it is recommended to stratifying (i.e., sub-group) the data according to the origin of bio-samples in the analytics.

关键词： Accuracy Diseases Big data Robustness Educational institutions bioinformatics Malignant tumors

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Guest Editorial for Special Section on BSB 2012

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2013年第4期10卷 817-818页

作者： de Souto, Marcilio C. P. Kann, Maricel Univ Orleans LIFO F-45067 Orleans France Univ Fed Pernambuco Ctr Informat BR-50740560 Recife PE Brazil Univ Maryland Baltimore Cty Dept Biol Sci Baltimore MD 21250 USA

This special section includes a selection of papers presented at the Seventh Brazilian symposium on bioinformatics (BSB 2012) held 15-17 August 2012 in Campo Grande (Mato Grosso do Sul), Brazil. BSB is an international symposium that covers all aspects of ioinformatics and computational biology. The symposium is organized by the special interest group in computational biology of the Brazilian Computer Society (SBC).

关键词： Special Issues And Sections Meetings bioinformatics computational Modeling

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LMP forecasting with prefiltered Gaussian process

LMP forecasting with prefiltered Gaussian process

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ieee symposium on computational intelligence Applications In Smart Grid (CIASG)

作者： Hiroyuki Mori Kaoru Nakano Dept. of Network Design Meiji University Nakano-ku Tokyo Japan Dept. of Electronics and Bioinformatics Meiji University Tama-ku Kawasaki Japan

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.

关键词： Kernel Covariance matrices Electricity Standards Approximation methods Pricing Bayes methods

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Accelerating Protein Tertiary Structure Analysis Based on FPGA

Accelerating Protein Tertiary Structure Analysis Based on FP...

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10th Annual ieee symposium on computational intelligence in bioinformatics and computational biology (CIBCB)

作者： Shen Quanhua Xia Fei Zhu Qianghua Naval Univ Engn Elect Engn Coll Wuhan 430033 Peoples R China

ISBN: (纸本)9781467358750

In the era of tertiary protein structure prediction, the most successful protein structure prediction methods involve sampling protein conformations which is a computational problem due to the increasing scale of the protein database. Recently FPGA chips have emerged as one promising application accelerator to accelerate bioinformatics algorithms by exploiting fine-grained custom design. In this paper, we propose a complete fine-grained parallel hardware implementation on FPGA XC5VLX330 chip to accelerate the BackboneDBN program for sampling realistic protein conformations using local structural bias. The experimental results show a speedup factor of more than 20x over software version running on a PC platform with Intel E7400 dual-core. However, the FPGA's power consumption is only about 30% of that of current general-purpose CPUs.

关键词： bioinformatics biological techniques field programmable gate arrays lab-on-a-chip molecular configurations parallel algorithms proteins

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Mapping of DNA sequences using Hidden Markov Model Self Organizing Maps

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

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10th Annual ieee symposium on computational intelligence in bioinformatics and computational biology (CIBCB)

作者： Dozono, Hiroshi Niina, Gen Saga Univ Dept Adv Technol Fus Saga 8408502 Japan

ISBN: (纸本)9781467358750

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： DNA biology computing genetics hidden Markov models learning (artificial intelligence) pattern classification self-organising feature maps sequences

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Guest Editors' Introduction to the Special Section on bioinformatics Research and Applications

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2013年第6期10卷 1345-1346页

作者： Mandoiu, Ion I. Wang, Jianxin Zelikovsky, Alexander Univ Connecticut Comp Sci & Engn Dept Storrs CT 06269 USA Cent S Univ Sch Informat Sci & Engn Changsha 410083 Hunan Peoples R China Georgia State Univ Dept Comp Sci Atlanta GA 30303 USA

This special section includes a selection of papers presented at the Eighth International symposium on bioinformatics Research and Application (ISBRA), which was held in Dallas, Texas, on 21-23 May 2012. The ISBRA symposium provides a forum for the exchange of ideas and results among researchers, developers, and practitioners working on all aspects of bioinformatics and computational biology and their applications. In 2012, 66 papers were submitted in response to the call for papers, out of which 26 papers appeared in the ISBRA proceedings published as volume 7292 of Springer Verlag's Lecture Notes in bioinformatics series. Extended versions of nine symposium papers were invited and accepted for publication in this special section following a rigorous review process. The selected papers cover a broad range of bioinformatics topics, including biological networks, computational complexity of problems in structural biology and genomics, and phylogenetic inference and analysis. Herein, we briefly introduce each of them.

关键词： Special Issues And Sections Research And Development bioinformatics Meetings Informatics

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Minimal-redundancy-maximal-relevance feature selection using different relevance measures for omics data classification

Minimal-redundancy-maximal-relevance feature selection using...

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10th Annual ieee symposium on computational intelligence in bioinformatics and computational biology (CIBCB)

作者： Yang, Junshan Zhu, Zexuan He, Shan Ji, Zhen Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen City Key Lab Embedded Syst Design Shenzhen 518060 Peoples R China Univ Birmingham Sch Comp Sci Birmingham B15 2TT W Midlands England

ISBN: (纸本)9781467358750

Omics refers to a field of study in biology such as genomics, proteomics, and metabolomics. Investigating fundamental biological problems based on omics data would increase our understanding of bio-systems as a whole. However, omics data is characterized with high-dimensionality and unbalance between features and samples, which poses big challenges for classical statistical analysis and machine learning methods. This paper studies a minimal-redundancy-maximal-relevance (MRMR) feature selection for omics data classification using three different relevance evaluation measures including mutual information (MI), correlation coefficient (CC), and maximal information coefficient (MIC). A linear forward search method is used to search the optimal feature subset. The experimental results on five real-world omics datasets indicate that MRMR feature selection with CC is more robust to obtain better (or competitive) classification accuracy than the other two measures.

关键词： biology computing learning (artificial intelligence) pattern classification statistical analysis

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Automatic phenotyping of multi-channel Schizosaccharomyces pombe images

Automatic phenotyping of multi-channel Schizosaccharomyces p...

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International symposium on Bioelectronics and bioinformatics (ISBB)

作者： Yen-Jen Chen Marc D. Green Sarah A. Sabatinos Susan L. Forsburg Chun-Nan Hsu Jyh-Ying Peng Institute of Biomedical Informatics National Yang-Ming University Taipei Taiwan R.O.C Molecular and Computational Biology Program University of Southern California Los Angeles USA Information Sciences Institute University of Southern California USA Department of Education and Research Taipei City Hospital Taipei Taiwan R.O.C

Schizosaccharomyces pombe shares many genes and proteins with humans and is a good model for chromosome behavior and DNA dynamics, which can be analyzed by visualizing the behavior of fluorescently tagged proteins in vivo. However, performing a genome-wide screen for changes in such proteins requires developing methods that automate analysis of multiple images. We developed a high content analysis system to robustly segment transmitted illumination images, extract cell and nucleus boundaries, and quantitatively characterize the fluorescence within each compartment. A support vector machine (SVM) is trained to automatically judge if a cell is undergoing septation, and another two SVMs are trained to classify pombe cells into various phenotypes according to its cell shape and fluorescence signal profile. We applied this system to automatically calculate the percentages of cells of different phenotypes for 4000 S. pombe mutants.

关键词： Proteins bioinformatics Image segmentation Shape DNA Educational institutions Genomics

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Managing Memory and Reducing I/O Cost for Correlation Matrix Calculation in bioinformatics

Managing Memory and Reducing I/O Cost for Correlation Matrix...

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10th Annual ieee symposium on computational intelligence in bioinformatics and computational biology (CIBCB)

作者： Krishnajith, Anaththa P. D. Kelly, Wayne Hayward, Ross Tian, Yu-Chu Queensland Univ Technol Sch Elect Engn & Comp Sci Brisbane Qld 4001 Australia

ISBN: (纸本)9781467358750

The generation of a correlation matrix from a large set of long gene sequences is a common requirement in many bioinformatics problems such as phylogenetic analysis. The generation is not only computationally intensive but also requires significant memory resources as, typically, few gene sequences can be simultaneously stored in primary memory. The standard practice in such computation is to use frequent input/output (I/O) operations. Therefore, minimizing the number of these operations will yield much faster run-times. This paper develops an approach for the faster and scalable computing of large-size correlation matrices through the full use of available memory and a reduced number of I/O operations. The approach is scalable in the sense that the same algorithms can be executed on different computing platforms with different amounts of memory and can be applied to different problems with different correlation matrix sizes. The significant performance improvement of the approach over the existing approaches is demonstrated through benchmark examples.

关键词： Correlation matrix bioinformatics computing scalable computing memory management phylogenetic analysis

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Early Lung Cancer Detection using Nucleus Segementation based Features

Early Lung Cancer Detection using Nucleus Segementation base...

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10th Annual ieee symposium on computational intelligence in bioinformatics and computational biology (CIBCB)

作者： Kancherla, Kesav Mukkamala, Srinivas New Mexico Inst Min & Technol ICASA CAaNES Socorro NM 87801 USA

ISBN: (纸本)9781467358750

In this study we propose an early lung cancer detection methodology using nucleus based features. First the sputum samples from patients are labeled with Tetrakis Carboxy Phenyl Porphine (TCPP) and fluorescent images of these samples are taken. TCPP is a porphyrin that is able to assist in labeling lung cancer cells by increasing numbers of low density lipoproteins coating on the surface of cancer. We study the performance of well know machine learning techniques in the context of lung cancer detection on Biomoda dataset. We obtained an accuracy of 81% using 71 features related to shape, intensity and color in our previous work. By adding the nucleus segmented features we improved the accuracy to 87%. Nucleus segmentation is performed by using Seeded region growing segmentation method. Our results demonstrate the potential of nucleus segmented features for detecting lung cancer.

关键词： Lung Cancer detection bioinformatics Machine Learning Seeded Region Growing segmentation

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