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17th ieee International conference on bioinformatics and Bioengineering (BIBE)

作者： Djorovic, Smiljana Filipovic, Nenad Milosavljevic, Aleksandar Velicki, Lazar Univ Kragujevac Fac Engn Kragujevac Serbia Res & Dev Ctr Bioengn BioIRC Kragujevac Serbia Inst Cardiovasc Dis Sremska Kamenica Serbia

ISBN: (纸本)9781538613245

The main purpose of this study was to examine and compare the biomechanical characteristics of a healthy tricuspid aortic valve (TAV) and diseased bicuspid aortic valve (BAV). The patient-specific geometrical model of TAV was created based on computed tomography (CT) scan images. On the same model, two leaflets (left and right) were manually fused in order to create the BAV model (type 1). The finite element analysis was performed using the algorithms and numerical methods for structural analysis on computational meshes. Also, equivalent material characteristics and boundary conditions were applied. As the result, displacements and Von Mises stress distribution were computed concerning anatomical differences between TAV and BAV structures. In the case of TAV, leaflets were symmetrically and centrally open, while BAV analysis resulted with regions of increased stresses on the leaflets with elliptically open valve. The performed comparative computational analysis gave better insight into the biomechanics of healthy and malformed aortic root. It may contribute to monitoring of structural characteristics due to the difficulty of obtaining such characteristics in vitro or in vivo.

关键词： tricuspid aortic valve bicuspid aortic valve computational analysis finite element method

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A hybrid CI-based knowledge discovery system on microarray gene expression data

A hybrid CI-based knowledge discovery system on microarray g...

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2005 ieee Symposium on computational intelligence in bioinformatics and computational biology, CIBCB '05

作者： Tang, Yuchun Huang, Zhen He, Yuanchen Hu, Xiaohua Zhang, Yan-Qing Sunderraman, Rajshekhar Department of Computer Science Georgia State University Atlanta GA 30302 Department of Chemistry Georgia State University Atlanta GA 30303 College of Information Science and Technology Drexel University Philadelphia PA 19104

ISBN: (纸本)0780393872

A hybrid computational intelligence-based Knowledge Discovery system is presented in this paper. The system works in three phases. In phase 1, many feature selection algorithms are utilized to select informative cancer-related genes from microarray expression data. Compared with other algorithms, our GSVM-RFE algorithm demonstrates superior performance on the microarray expression dataset for AML/ALL classification. Specifically, a compact "perfect" gene subset is reported. In phase 2, many intelligent computation models are implemented to extract useful knowledge about functions of selected genes to regulate the cancer being studied. Knowledge can ease further biomedical study because of reliable information sources, high prediction accuracy, and easiness to interpret. Currently, knowledge is represented in two formats, Web-based and Rule-based. As a future work, we plan to implement knowledge fusion algorithms in phase 3 to synthesize and consolidate hybrid knowledge into a single knowledge base to provide effective and efficient decision support for cancer diagnosis and drug discovery. © 2005 ieee.

关键词： Artificial intelligence

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Bioinformatic insights from metagenomics through visualization

Bioinformatic insights from metagenomics through visualizati...

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ieee computational Systems bioinformatics conference

作者： Havre, SL Webb-Robertson, BJ Shah, A Posse, C Gopalan, B Brockman, FJ Pacific NW Natl Lab Richland WA 99352 USA

ISBN: (纸本)0769523447

Cutting-edge biological and bioinformatics research seeks a systems perspective through the analysis of multiple types of high-throughput and other experimental data for the same sample. Systems-level analysis requires the integration and fusion of such data, typically through advanced statistics and mathematics. Visualization is a complementary computational approach that supports integration and analysis of complex data or its derivatives. We present a bioinformatics visualization prototype, Juxter, which depicts categorical information derived from or assigned to these diverse data for the purpose of comparing patterns across categorizations. The visualization allows users to easily, discern correlated and anomalous patterns in the data. These patterns, which might not be detected automatically by, algorithms, may, reveal valuable information leading to insight and discovery. We describe the visualization and interaction capabilities and demonstrate its utility in a new field, metagenomics, which combines molecular biology and genetics to identify and characterize genetic material from multi-species microbial samples.

关键词： bioinformatics visualization metagenomics categorical nominal

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Multithreaded Parallel Sequence Alignment Based on Needleman-Wunsch Algorithm 19

Multithreaded Parallel Sequence Alignment Based on Needleman...

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19th Annual ieee International conference on bioinformatics and Bioengineering (BIBE)

作者： Gancheva, Veska Georgiev, Ivaylo Tech Univ Sofia Dept Programming & Comp Technol Sofia Bulgaria Stephan Angeloff Inst Microbiol Sofia Bulgaria

ISBN: (纸本)9781728146171

Biocomputing and molecular biology are areas that change knowledge and skills for acquisition, storing, management, analysis, interpretation and dissemination of biological information. This requires the utilization of high performance computers and innovative software tools for management of the vast information, as well as deployment of innovative algorithmic techniques for analysis, interpretation and prognostication of data in order to get to insight of the design and validation of life-science experiments. Sequence alignment is an important method in DNA and protein analysis. The paper describes the computational challenges in biological sequence processing. The great challenges are to propose parallel computational models and parallel program implementations based on the algorithms for biological sequence alignment. An investigation of the efficiency of sequence alignment based on parallel multithreaded program implementation of Needleman-Wunsch algorithm is presented in this paper. Parallel computational model based on Needleman-Wunsch algorithm is designed. The proposed parallel model is verified by multithreaded parallel program implementation utilizing OpenMP. A number of experiments have been carried out for the case of various data sets and a various number of threads. Parallel performance parameters execution time and speedup are estimated experimentally. The performance estimation and scalability analyses show that the suggested model has good scalability both in respect to the workload and machine size.

关键词： bioinformatics DNA Needleman-Wunsch algorithm OpenMP parallel performance sequence alignment

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A system for recognition of biological patterns in toxins using computational intelligence

A system for recognition of biological patterns in toxins us...

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2009 ieee Symposium on computational intelligence in bioinformatics and computational biology, CIBCB 2009

作者： De Carvalho, Bernardo Penna Resende Mendes, Thais Melo De Souza Ribeiro, Ricardo Fortuna, Ricardo Veneroso, José Marcos De Alvarenga Mudado, Maurácio ABG Information Systems Fundaç ão Ezequiel Dias

ISBN: (纸本)9781424427567

This work presents an innovative way to find biological patterns in toxins in order to classify them according to their biological functions. Basing on relevant biological information (database) it was developed a system that uses computational intelligence to discover novel patterns within the primary and secondary structures of a set of toxins. The discovered patterns make it possible to differentiate these toxins by their function: binding to specific channels for sodium, calcium or potassium ions. The classification rules are built using a given toxin database which is pre-processed according to the existence of signal peptide or propeptide in the primary sequence, together with the predicted secondary structures and its physical and chemical characteristics and water affinity information. The best obtained patterns are combined together in order to generate a final rule. All the experiments were performed using 802 toxin primary sequences labeled as channel functions obtained from two public databases, ATDB and Tox-Prot. After using the system to solve three different binary classification problems, each one for a specific ion channel, a committee is used to obtain the final classification label for each toxin. The committee got a classification accuracy of 80%, with correctness of 97%, 67% and 55% respectively to sodium, potassium and calcium channels. © 2009 ieee.

关键词： Database systems

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computational methods in systems biology: 13th international conference, CMSB 2015Nantes, France, september 16–18, 2015 proceedings 13th

Computational methods in systems biology: 13th international...

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13th International conference on computational Methods in Systems biology, CMSB 2015

作者： Roux, Olivier Bourdon, Jérémie École Centrale de Nantes Nantes France Université de Nantes Nantes France

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A Swarm intelligence Framework for Reconstructing Gene Networks: Searching for Biologically Plausible Architectures

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2012年第2期9卷 358-371页

作者： Kentzoglanakis, Kyriakos Poole, Matthew Natl Inst Med Res MRC Div Math Biol London NW7 1AA England Univ Portsmouth Sch Comp Portsmouth PO1 3HE Hants England

In this paper, we investigate the problem of reverse engineering the topology of gene regulatory networks from temporal gene expression data. We adopt a computational intelligence approach comprising swarm intelligence techniques, namely particle swarm optimization (PSO) and ant colony optimization (ACO). In addition, the recurrent neural network (RNN) formalism is employed for modeling the dynamical behavior of gene regulatory systems. More specifically, ACO is used for searching the discrete space of network architectures and PSO for searching the corresponding continuous space of RNN model parameters. We propose a novel solution construction process in the context of ACO for generating biologically plausible candidate architectures. The objective is to concentrate the search effort into areas of the structure space that contain architectures which are feasible in terms of their topological resemblance to real-world networks. The proposed framework is initially applied to the reconstruction of a small artificial network that has previously been studied in the context of gene network reverse engineering. Subsequently, we consider an artificial data set with added noise for reconstructing a subnetwork of the genetic interaction network of S. cerevisiae (yeast). Finally, the framework is applied to a real-world data set for reverse engineering the SOS response system of the bacterium Escherichia coli. Results demonstrate the relative advantage of utilizing problem-specific knowledge regarding biologically plausible structural properties of gene networks over conducting a problem-agnostic search in the vast space of network architectures.

关键词： Gene regulatory networks network inference recurrent neural networks swarm intelligence particle swarm optimization ant colony optimization degree distribution

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A Survey of computational Methods and Databases for lncRNA-MiRNA Interaction Prediction

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2023年第5期20卷 2810-2826页

作者： Sheng, Nan Huang, Lan Gao, Ling Cao, Yangkun Xie, Xuping Wang, Yan Jilin Univ Coll Comp Sci & Technol Changchun 130012 Peoples R China Jilin Univ Sch Artificial Intelligence Changchun 130012 Peoples R China

Long non-coding RNAs (lncRNAs) and microRNAs (miRNAs) are two prevalent non-coding RNAs in current research. They play critical regulatory roles in the life processes of animals and plants. Studies have shown that lncRNAs can interact with miRNAs to participate in post-transcriptional regulatory processes, mainly involved in regulating cancer development, metastatic progression, and drug resistance. Additionally, these interactions have significant effects on plant growth, development, and responses to biotic and abiotic stresses. Deciphering the potential relationships between lncRNAs and miRNAs may provide new insights into our understanding of the biological functions of lncRNAs and miRNAs, and the pathogenesis of complex diseases. In contrast, gathering information on lncRNA-miRNA interactions (LMIs) through biological experiments is expensive and time-consuming. With the accumulation of multi-omics data, computational models are extremely attractive in systematically exploring potential LMIs. To the best of our knowledge, this is the first comprehensive review of computational methods for identifying LMIs. Specifically, we first summarized the available public databases for predicting animal and plant LMIs. Second, we comprehensively reviewed the computational methods for predicting LMIs and classified them into two categories, including network-based methods and sequence-based methods. Third, we analyzed the standard evaluation methods and metrics used in LMI prediction. Finally, we pointed out some problems in the current study and discuss future research directions. Relevant databases and the latest advances in LMI prediction are summarized in a GitHub repository https://***/sheng-n/lncRNA-miRNA-interaction-methods, and we'll keep it updated.

关键词： computational methods lncRNA-miRNA interaction prediction network-based methods sequence-based methods

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The Role of Reward Structure, Coordination Mechanism and Net Return in the Evolution of Cooperation

The Role of Reward Structure, Coordination Mechanism and Net...

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ieee conference on computational intelligence and Games (CIG)

作者： Rajagopalan, Padmini Rawal, Aditya Miikkulainen, Risto Wiseman, Marc A. Holekamp, Kay E. Univ Texas Austin Dept Comp Sci Austin TX 78712 USA Michigan State Univ Dept Zool E Lansing MI 48824 USA

ISBN: (纸本)9781457700095

biology can offer insight into how realistic artificial agents and complex interactions between them can be created. For instance, spotted hyenas of Western Africa typically hunt alone, but cooperate once in a while to catch zebras. Using hyenas as motivation, this paper evaluates three potential factors that affect the evolution of cooperation in a team of game agents: reward structure (i.e. whether fitness reward is given to an individual agent or shared by the team), coordination mechanism (i.e. stigmergic vs. direct communication), and net return (i.e. the size of reward relative to the difficulty of obtaining it). Through neuroevolution using Multi-Component ESP [1], three predators were evolved in a field containing multiple fixed-behavior prey. Six such experiments show that (1) although shared reward strongly promotes cooperation, it is not strictly necessary for evolving cooperation;(2) although stigmergic coordination works well in simple, unambiguous tasks (i.e. with a single prey), direct communication is more effective in more complex tasks (i.e. with multiple prey);and (3) the predators evolve to hunt alone or cooperatively based on which approach results in higher net return. Insights from these computational simulations can be used to develop more intelligent behaviors for game agents in the future.

关键词： Biological neural networks computational modeling Computer architecture conferences Games Neurons Predator prey systems artificial agents biology computational simulations cooperation evolution coordination mechanism game agents game theory multi-agent systems multicomponent ESP net return neuroevolution reward structure

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Measurements and computational intelligence in biomedical engineering

Measurements and computational intelligence in biomedical en...

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ieee International Symposium on computational intelligence for Measurement Systems and Applications

作者： Micheli-Tzanakou, E Rutgers State Univ Dept Biomed Engn Piscataway NJ 08854 USA

ISBN: (纸本)0780377834

We have used advanced signal processing and innovative image processing methods that are used outside the operating room. The software is written in C++ in a windows environment and can be used on any PC. Applications will be discussed along with computational intelligence as used for diagnostic purposes and as visualization aids inside and outside the operating room. Subjects to be discussed, include blood cell classification, mammography, evoked potentials, ophthalmology, EEG and field potentials in Parkinson's disease. The latter methods along with 3D reconstruction of MRI images of Parkinson's patients, are currently used in the operating room for target assessment and electrode placement.

关键词： Application software Biomedical engineering Biomedical measurements Biomedical signal processing Blood Cells (biology) computational intelligence Image processing Mammography Visualization

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