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ieee 7th International conference on bioinformatics and computational biology (ICBCB)

作者： Sheng, Yinhe Huang, Kang Zou, Jiemeng Wang, Liping Wei, Pengfei Chinese Acad Sci CAS Ctr Excellence Brain Sci & Intelligence Techn Brain Cognit & Brain Dis Inst Collaborat Innovat Ctr Brain SciShenzhen Inst Ad Shenzhen 518055 Peoples R China Xi An Jiao Tong Univ Sch Software Xian 710049 Peoples R China

ISBN: (纸本)9781728106410

The mental fatigue affects the state of one's daily life easily, therefore, understanding the neural mechanisms of mental fatigue and better detection of it have been the focus of many researchers. Quit a few previous studies have found EEG indicators and high-precision detection methods related to mental fatigue, however, how to combine these EEG indicators with detection methods for better detection remains to be solved. To classify mental fatigue based on EEG features, our previous research, which adopted GA-SVM method, have demonstrated the optimal channels are mainly distributed in the prefrontal, occipital and temporal lobes, and the optimal channel number is 5. Here, we further explored the question by developing a new method combining genetic algorithm and hierarchical clustering to study the mental fatigue caused by visual search. Our results suggest that the optimal EEG features for assessing fatigue state vary from person to person, while the corresponding optimal channel positions are consistent. The channels with the largest changes in EEG features are mainly distributed in the frontal lobe, followed by the temporal lobe and a small area of the occipital lobe, while the corresponding regions of the almost all parietal lobe and part occipital lobe show little changes in EEG features during fatigue. Current study shows that the optimal EEG features of different individuals are different in the mental fatigue detection, and they need to be determined separately, but only a few of the same channels can be used to achieve the better detection.

关键词： component mental fatigue EEG visual search hierarchical clustering

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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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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 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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Feature Selection and Polydispersity Characterization for QSPR Modelling: Predicting a Tensile Property 12th

Feature Selection and Polydispersity Characterization for QS...

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

作者： Cravero, Fiorella Schustik, Santiago Jimena Martinez, Maria Barranco, Carlos D. Diaz, Monica F. Ponzoni, Ignacio UNS CONICET PLAPIQUI Bahia Blanca Buenos Aires Argentina CIC Bahia Blanca Buenos Aires Argentina UNS CONICET Dept Ciencias & Ingn Comp Inst Ciencias & Ingn Comp Bahia Blanca Buenos Aires Argentina Pablo de Olavide Univ Intelligent Data Anal DATAi Div Comp Sci Seville 41013 Spain UNS Dept Ingn Quim Bahia Blanca Buenos Aires Argentina

ISBN: (纸本)9783319987026;9783319987019

QSPR (Quantitative Structure-Property Relationship) models proposed in Polymer Informatics typically use reduced computational representations of polymers for avoiding the complex issues related with the polydispersion of these industrial materials. In this work, the aim is to assess the effect of this oversimplification in the modelling decisions and to analyze strategies for addressing alternative characterizations of the materials that capture, at least partially, the polydispersion phenomenon. In particular, a cheminformatic study for estimating a tensile property of polymers is presented here. Four different computational representations are analyzed in combination with several machine learning approaches for selecting the most relevant molecular descriptors associated with the target property and for learning the corresponding QSPR models. The obtained results give insight about the limitations of using oversimplified representations of polymers and contribute with alternative strategies for achieving more realistic models.

关键词： QSPR Polydispersity Artificial intelligence Feature selection

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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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Maximum Likelihood Methods in biology Revisited with Tools of computational intelligence

Maximum Likelihood Methods in Biology Revisited with Tools o...

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30th Annual International conference of the ieee-Engineering-in-Medicine-and-biology-Society

作者： Seiffertt, John Vanbrunt, Andrew Wunsch, Donald C., II Missouri Univ Sci & Technol Dept Elect & Comp Engn Rolla MO 65401 USA Washington Univ Sch Med Ctr Genome Sci Div Stat Genom St Louis MO 63108 USA Univ Missouri Sci Techonl Dept Elect & Comp Engn Rolla MO 65401 USA

ISBN: (纸本)9781424418145

We investigate the problem of identification of genes correlated with the occurrence of diseases in a given population. The classical method of parametric linkage analysis is combined with newer tools and results are achieved on a model problem. This traditional method has advantages over non-parametric methods, but these advantages have been difficult to realize due to their high computational cost. We study a class of Evolutionary Algorithms from the computational intelligence literature which are designed to cut such costs considerably for optimization problems. We outline the details of this algorithm, called Particle Swarm Optimization, and present all the equations and parameter values we used to accomplish our optimization. We view this study as a launching point for a wider investigation into the leveraging of computational intelligence tools in the study of complex biological systems.

关键词： Maximum likelihood

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A symmetry-driven BP algorithm for the Discretizable Molecular Distance Geometry Problem

A symmetry-driven BP algorithm for the Discretizable Molecul...

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ieee International conference on bioinformatics and Biomedicine Workshops (BIBMW)

作者： Mucherino, A. Lavor, C. Liberti, L. Univ Rennes 1 IRISA Rennes France Univ Estadual Campinas IMECC Campinas SP Brazil Ecole Polytech LIX Palaiseau France

ISBN: (纸本)9781457716133

Branch & Prune (BP) is a deterministic algorithm for the solution of the Discretizable Molecular Distance Geometry Problem (DMDGP). This problem has important applications in the field of structural biology, in particular for the determination of the three-dimensional conformation of a molecule by using information obtained by NMR techniques. In recent works, we proved that the search domain of the DMDGP, which is represented by a binary tree, contains various symmetries which are related to the number of solutions to the problem. In the present work, we propose a variant of the BP algorithm which is able to exploit the information regarding the symmetries to speed up the search. computational experiments show that the symmetry-driven BP (symBP) outperforms the original BP algorithm in particular when instances having several solutions are considered.

关键词： Atomic layer deposition Atomic measurements Binary trees conferences Geometry NMR imaging NMR techniques Nuclear magnetic resonance Proteins binary tree biology computing branch & prune algorithm computational geometry deterministic algorithm discretizabl

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3D Genome Reconstruction with ShRec3D+and Hi-C Data

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2018年第2期15卷 460-468页

作者： Li, Jiangeng Zhang, Wei Li, Xiaodan Beijing Univ Technol Coll Elect & Control Engn Beijing 100022 Peoples R China Beijing Univ Technol Beijing Key Lab Computat Intelligence & Intellige Beijing 100022 Peoples R China

Hi-C technology, a chromosome conformation capture (3C) based method, has been developed to capture genome-wide interactions at a given resolution. The next challenge is to reconstruct 3D structure of genome from the 3C-derived data computationally. Several existing methods have been proposed to obtain a consensus structure or ensemble structures. These methods can be categorized as probabilistic models or restraint-based models. In this paper, we propose a method, named ShRec3D+, to infer a consensus 3D structure of a genome from Hi-C data. The method is a two-step algorithm which is based on ChromSDE and ShRec3D methods. First, correct the conversion factor by golden section search for converting interaction frequency data to a distance weighted graph. Second, apply shortest-path algorithm and multi-dimensional scaling (MDS) algorithm to compute the 3D coordinates of a set of genomic loci from the distance graph. We validate ShRec3DI accuracy on both simulation data and publicly Hi-C data. Our test results indicate that our method successfully corrects the parameter with a given resolution, is more accurate than ShRec3D, and is more efficient and robust than ChromSDE.

关键词： 3D genome reconstruction interaction frequencies multi-dimensional scaling (MDS) a conversion factor a distance weighted graph

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Simplification Methods for Maximum Parsimony: A Relational View

Simplification Methods for Maximum Parsimony: A Relational V...

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ieee International conference on bioinformatics and Biomedicine (BIBM)

作者： Blanco, Roberto Univ Zaragoza Aragon Inst Engn Res I3A Dept Comp Sci & Syst Engn DIIS Zaragoza Spain

ISBN: (纸本)9780769545745

The reconstruction of phylogenetic trees is one of the central challenges in evolutionary biology. Costly algorithms cannot cope with the continuous growth of potential datasets, and heuristic strategies are universally adopted to curb the increased computational burden. Here we propose alternative methods of input reduction founded on the precise mathematical definition of the maximum parsimony criterion and on the notion of conservation of biochemical properties of the molecules and the genes which encode them. We thus extend the as of yet limited existing simplification rules with exact and highly efficient methods to detect and treat parsimoniously related components in the alignment. For this, a generalized framework for heterogeneously modeled parsimony is introduced.

关键词： computational phylogenetics heterogeneous parsimony reduction rule sequence alignment Steiner problem

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