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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191-200 订阅

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Predicting Time Sequences in Gene Expression Using Dynamic Neuro-Fuzzy Networks

Predicting Time Sequences in Gene Expression Using Dynamic N...

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ieee conference on computational Intelligence in bioinformatics and computational Biology CIBCB

作者： Ahmadi, Ali Tari, Sadaf Iranpour KN Toosi Univ Technol Fac Comp Engn Tehran Iran

ISBN: (纸本)9781479969265

Gene regulation at the cellular level is a dynamic process;and neural networks with the capability of being trained and regulated based on training data, and fuzzy systems with their interpretability, are suitable algorithms for this type of computations. In this research, methods based on neuro-fuzzy networks were employed for predicting the complicated relationships that exist among genes. In the proposed method, genes that have the greatest effect on each other are found and considered as regulatory genes. Then the types of their relationships including those with inhibitory, activating, and neutralizing effects are determined, and the gene regulatory network is mapped. The standard microarray dataset related to 12 typical genes that influence the germination cycle of the yeast species Saccharomyces cerevisiae was used for training. Obtained results were validated using previous biological laboratory results. It was found that the proposed method reduced the number of extracted rules for input-output space fragmentation by 15%, which substantially reduced the required computations, while the sum square error of the algorithm declined by -0.3 compared to the method that resembled the proposed algorithm the most.

关键词： bioinformatics gene expression gene regulatory network neuro-fuzzy networks

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Intelligent Mobile Web Service for bioinformatics systems

Intelligent Mobile Web Service for Bioinformatics Systems

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International conference on Informative and Cybernetics for computational Social systems (ICCSS)

作者： Chuang, Chen-Chia Jeng, Jin-Tsong Hsiao, Chih-Ching Ho, Yu-Yang Liu, Yue-Shiang Natl Ilan Univ Dept Elect Engn Yilan 260 Taiwan Kao Yuan Univ Dept Informat Technol Kaohsiung Taiwan Natl Formosa Univ Dept Comp Sci & Informat Engn Huwei Township Yunlin Taiwan

ISBN: (纸本)9781467372459

In this paper, to integrate android systems, intelligent computing and web service on an intelligent mobile web service computing systems for bioinformatics is proposed. That is, users can send parameters to control the intelligent computing system under Matlab/C#/Java and query from client-side to server-side in the proposed bioinformatics systems with their android phone. Besides, the proposed system can also deal with disease genes search under web service with bioinformatics web. At the same time, web service is important communication protocol in the proposed systems. Consequently, the proposed systems can provide that user can modify parameters for controlling the proposed intelligent computing, deal with disease genes search under web service with bioinformatics web and obtain results from the proposed systems by android phone arbitrarily.

关键词： Android systems Web Service bioinformatics mobile computing

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Distributed rank-1 dictionary learning: Towards fast and scalable solutions for fMRI big data analytics

Distributed rank-1 dictionary learning: Towards fast and sca...

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ieee International conference on Big Data

作者： Milad Makkie Xiang Li Tianming Liu Shannon Quinn Binbin Lin Jieping Ye Department of Computer Science The University of Georgia Athens GA Department of Computational Medicine and Bioinformatics University of Michigan Ann Arbor MI

ISBN: (纸本)9781467390064

The use of functional brain imaging for research and diagnosis has benefitted greatly from the recent advancements in neuroimaging technologies, as well as the explosive growth in size and availability of fMRI data. While it has been shown in literature that using multiple and large scale fMRI datasets can improve reproducibility and lead to new discoveries, the computational and informatics systems supporting the analysis and visualization of such fMRI big data are extremely limited and largely under-discussed. We propose to address these shortcomings in this work, based on previous success in using dictionary learning method for functional network decomposition studies on fMRI data. We presented a distributed dictionary learning framework based on rank-1 matrix decomposition with sparseness constraint (D-r1DL framework). The framework was implemented using the Spark distributed computing engine and deployed on three different processing units: an in-house server, in-house high performance clusters, and the Amazon Elastic Compute Cloud (EC2) service. The whole analysis pipeline was integrated with our neuroinformatics system for data management, user input/output, and real-time visualization. Performance and accuracy of D-r1DL on both individual and group-wise fMRI Human Connectome Project (HCP) dataset shows that the proposed framework is highly scalable. The resulting group-wise functional network decompositions are highly accurate, and the fast processing time confirm this claim. In addition, D-r1DL can provide real-time user feedback and results visualization which are vital for large-scale data analysis.

关键词： Dictionaries Data visualization Sparks Big data Real-time systems Loading Distributed databases

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An 8-bit 2 MS/s Successive Approximation Register Analog-To-Digital Converter for bioinformatics and computational Biology Application 12

An 8-bit 2 MS/s Successive Approximation Register Analog-To-...

引用

ieee 12th International conference on Networking, Sensing and Control

作者： Lai, Wen Cheng Huang, Jhin-Fang Hsieh, Cheng Gu Kao, Fan-Tsai Natl Taiwan Univ Sci & Technol Taipei Taiwan

ISBN: (纸本)9781479980697

In this paper, a 1.8-V 8-bit successive approximation register (SAR) analog-to-digital converter (ADC) implemented in TSMC 0.18-um CMOS process is presented. By applying SAR control logic that reduces half comparator and digital circuit power consumption, the proposed SAR ADC achieves low power consumption. Also, bootstrapped switch method is used to solve varying on-resistance of analog sampling switch caused from varying input signal. Measured results show that at the supply voltage of 1.8 V and sampling rate of 2 MS/s, the proposed SAR ADC achieves a signal-to-noise and distortion ratio (SNDR) of 42.7 dB, an effective number of bits (ENOB) of 6.8 bits, a differential nonlinearity (DNL) of 0.98 LSB, an integral nonlinearity (INL) of 2.01 LSB and a power consumption of 128 mu W. The overall chip area is only 0.67 mm(2) with a small ADC core area of 0.226 mm(2) for bioinformatics and computational Application.

关键词： Analog-to-digital converter successive approximation register bio-signals and systems

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Biomarker Discovery, High Performance and Cloud Computing: A Comprehensive Review

Biomarker Discovery, High Performance and Cloud Computing: A...

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ieee International conference on bioinformatics and Biomedicine - Medical Informatics and Decision Making

作者： Blayney, Jaine Haberland, Valeriia Lightbody, Gaye Browne, Fiona Queens Univ Belfast Sch Med Dent & Biomed Sci Belfast BT7 1NN Antrim North Ireland Univ London Tungsten Ctr Intelligent Data Analyt London WC1E 7HU England Univ Ulster Sch Comp & Math Coleraine BT52 1SA Londonderry North Ireland

ISBN: (纸本)9781467367981

The analysis of biological markers (biomarkers) have the ability to improve clinical outcomes of a disease through prediction and early detection. The constant improvement of next generation sequencing (NGS) technologies coupled with falling equipment price are driving research and application especially in the medical domain. NGS technology is being applied to cancer research promising greater understanding of carcinogenesis. However, as sequence capacity grows, algorithmic speed is becoming an important bottleneck. To understand these challenges we present a review on difficulties currently faced in discovering biomarkers. The review places the spotlight on sequencing technologies and the bottlenecks encountered in these pipelines. Cloud computing and high performance computing technologies such as Grid are summarized in tackling computational challenges presented by technologies and algorithms used in biomarker research.

关键词： bioinformatics Decision support systems Genomics Robustness Sequential analysis

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Vessel segmentation in low contrast X-ray angiogram images

Vessel segmentation in low contrast X-ray angiogram images

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ieee International conference on Image Processing

作者： B. Felfelian H. R. Fazlali N. Karimi S. M. R. Soroushmehr S. Samavi B. Nallamothu K. Najarían Department of Electrical and Computer Engineering Isfahan University of Technology Isfahan Iran Department of Emergency Medicine University of Michigan Ann Arbor U.S.A. Departruent of Internal Medicine University of Michigan Ann Arbor U.S.A. Deptartment of Computational Medicine and Bioinformatics University of Michigan Ann Arbor U.S.A.

Coronary artery disease is one of the major causes of death throughout the world. An effective method for diagnosing this disease is X-ray angiography. The images are usually of poor quality and low contrast. This is due to non-uniform illumination, appearance of other body organs and artifacts, low SNR values, etc. Accurate segmentation of arteries is a challenging and important task. In this paper we first extract coronary arteries region of interest (ROI) using Hessian filter. Then, we combine these results with the flux flow measurements for accurate identification of vessel pixels. Post processing is performed to eliminate falsely identified vessel pixels. Finally, we segment the coronary arteries by selecting the largest connected component. Qualitative and quantitative evaluations of our method show high effectiveness of the proposed method. In terms of capturing major vessels our method is successful in 96% of cases.

关键词： Decision support systems Biomedical imaging Indexes Q measurement Computers Biology Computer vision

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A Takagi-Sugeno fuzzy neural network-based algorithm with single-channel EEG signal for the discrimination between light and deep sleep stages

A Takagi-Sugeno fuzzy neural network-based algorithm with si...

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ieee Biomedical Circuits and systems (BIOCAS)

作者： Tsung-Han Tsai Lih-Jen Kau Kun-Mao Chao Electronics and Bioinformatics National Taiwan University Taiwan Department of Electronic Engineering National Taipei University of Technology Taiwan Department of Computer Science & Information Engineering National Taiwan University Taiwan

ISBN: (纸本)9781509029600

We propose in this paper an efficient and effective algorithm for the discrimination between light and deep sleep stages of a subject based on the single-channel electroencephalography (EEG) signal. To realize the proposed approach, the acquired EEG signal is first convolved with a set of three filters to extract the corresponding component of δ, θ, and α waves. After that, we calculate the average point energy (A.P.E.) of the extracted δ, θ, and α components to construct a three-dimensional feature vector, which will be used as the input of a four-layer Takagi-Sugeno fuzzy neural network (TS-FNN) for further discrimination process. Finally, a value which indicates the depth degree of sleep will be given in the fourth layer of TS-FNN. In order that a satisfactory accuracy can be obtained with the proposed TS-FNN classifier, EEG signals of four subjects in the Sleep-EDF database will be used as the training data. During the performance evaluation process, the Sleep-EDF database will be used again, but with EEG signal from different subjects. As we will see the proposed algorithm is quite feasible for real-time applications due to moderate computational cost. Moreover, our experiment shows that the classification accuracy up to 81.98% can be obtained with the proposed approach when 3418 epochs are evaluated. Besides, the sensitivity and specificity for deep sleep stages are 74.19% and 84.25% respectively, while that for light sleep stage are 84.25% and 74.19% respectively, which outperforms that by most of the existing state-of-the-art algorithms.

关键词： Sleep Electroencephalography Databases Training Feature extraction Support vector machines Testing

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Secure access to patient's health records using SpeechXRays a mutli-channel biometrics platform for user authentication

Secure access to patient's health records using SpeechXRays ...

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Annual International conference of the ieee Engineering in Medicine and Biology Society (EMBC)

作者： Emmanouil G. Spanakis Marios Spanakis Apostolos Karantanas Kostas Marias Computational Biomedicine Laboratory Foundation for Research and Technology - Hellas Heraklion Crete Greece Department of Medical Imaging Heraklion University Hospital

The most commonly used method for user authentication in ICT services or systems is the application of identification tools such as passwords or personal identification numbers (PINs). The rapid development in ICT technology regarding smart devices (laptops, tablets and smartphones) has allowed also the advance of hardware components that capture several biometric traits such as fingerprints and voice. These components are aiming among others to overcome weaknesses and flaws of password usage under the prism of improved user authentication with higher level of security, privacy and usability. To this respect, the potential application of biometrics for secure user authentication regarding access in systems with sensitive data (i.e. patient's data from electronic health records) shows great potentials. SpeechXRays aims to provide a user recognition platform based on biometrics of voice acoustics analysis and audio-visual identity verification. Among others, the platform aims to be applied as an authentication tool for medical personnel in order to gain specific access to patient's electronic health records. In this work a short description of SpeechXrays implementation tool regarding eHealth is provided and analyzed. This study explores security and privacy issues, and offers a comprehensive overview of biometrics technology applications in addressing the e-Health security challenges. We present and describe the necessary requirement for an eHealth platform concerning biometric security.

关键词： Biometrics (access control) Authentication bioinformatics Biomedical imaging Hospitals Databases

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Network based models and path based features for gene prioritization

Network based models and path based features for gene priori...

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International conference on Computer Supported Cooperative Work in Design

作者： Yuechang Liu Hanghang Tong Xie Lei Yong Tang School of Computer Science Jiaying University Meizhou Guangdong School of Computing Informatics and Decision Systems Engineering Arizona State University Tempe AZ USA Computer Science Hunter College The City University of New York USA School of Computer Science South China Normal University Guangzhou Guangdong

ISBN: (纸本)9781509019168

Network analysis has been shown to be an effective and cheap way to screen genes that are associated to diseases and chemicals. The identification of features that are used to order potentially related genes is key to do this job. Though many network models and structure based features have been proposed in the literature, they do not perform well enough for such gene prioritization task, especially when the heterogeneity of such networks is taken account. In this paper, a type of heterogeneous network called Generalized Bi-relational Network (GBN) is formalized. A series of path based features on GBN are defined. Though some of the features have been used in other literature, it is the first time to evaluate them in both supervised and unsupervised learning models. The experiment on real chemical-disease-gene networks shows that the features proposed in this paper gain promising performance in both supervised and unsupervised framework.

关键词： Diseases computational modeling Chemicals Computer science Electronic mail Social network services bioinformatics

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Accelerating Needleman-Wunsch Global Alignment Algorithm with GPUs 12

Accelerating Needleman-Wunsch Global Alignment Algorithm wit...

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12th ieee/ACS International conference of Computer systems and Applications (AICCSA)

作者： Fakirah, Maged Shehab, Mohammed A. Jararweh, Yaser Al-Ayyoub, Mahmoud Jordan Univ Sci & Technol Irbid Jordan

ISBN: (纸本)9781509004782

Over the recent decades, bioinformatics has acquired a major concern due to the rapid growth in biological data that includes protein structures and genome sequences. Many considerable efforts have been conducted by computer scientists, mathematicians and biologists to coup with complex biological problems such as sequence alignment problem, using several techniques to formulate/model the targeted biological problems as computational problems and design algorithms to solve them in an accurate and efficient manner. NeedlemanWunsch algorithm as well as other alignment algorithms have been the subject of many studies to improve their performance due to their importance and the large scale of the data they have to handle (e.g., aligning strings of hundreds of thousands of characters). Approaches included a mixture of different parallel implementations using specialized hardware such as Graphical Processing Units (GPUs) and a vectorized approach of reading and processing the input data. In this work, a parallel implementation of NW algorithm is presented using GPU due to its efficiency and high speed, to solve the slowness problem associated with this algorithm when processing large data sets, as well as to enhance the performance of the algorithm especially when processing vectors of adjacent cells parallel to the matrix miner diagonal. The experiments show that the proposed implementation improves the performance of the algorithm by 99%.

关键词： bioinformatics

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