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ieee symposium on computational intelligence and bioinformatics and computational biology (CIBCB)

作者： C. Pridgeon D. Corne SECAM University of Exeter Exeter UK

We approach the general problem of finding a model that discriminates between classes of nucleotide sequences. In this area, a common approach is to train a model, such as a neural network, or a hidden Markov model, to perform the discrimination, using as inputs either the raw sequences encoded in a standard form, or features derived from the raw data in a pre-processing stage. In this paper a novel discriminatory pattern structure for nucleotide sequences is introduced, called a hypermotif, and evolutionary computation is used to evolve a collection of specific hypermotifs which discriminate between classes in the data. The raw nucleotide data are then processed, transforming it into feature vectors, where the features are the individual scores on the evolved hypermotifs. Using this transformation, any classification method may then be used to build an accurate predictive model. The approach is tested on a database of eukaryotic promoters, and find that this method enables us to outperform a standard multilayer perceptron (despite using a linear discriminant as the final classifier), and provides similar performance to the best approach so far for these data (which uses a time delay neural network)

关键词： Hidden Markov models Predictive models Neural networks bioinformatics Evolutionary computation computational intelligence Testing Spatial databases Multilayer perceptrons Delay effects

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Novel Algorithm for MALDI-TOF Baseline Drift Removal

Novel Algorithm for MALDI-TOF Baseline Drift Removal

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ieee symposium on computational intelligence and bioinformatics and computational biology (CIBCB)

作者： J. Kolibal D. Howard Department of Mathematics University of Southern Mississippi Hattiesburg USA QinetiQ Limited Malvern UK

Baseline drift is an endemic problem in matrix-assisted laser desorption ionization time of flight mass spectrometry (MALDI-TOF), a device frequently used in proteomics investigations and in selected genomics work. Following an explanation of the origin of this baseline drift that sheds light on the inherent difficulty of its removal by chemical means, the stochastic Bernstein function approximation (SB), a new signal processing method, is developed into a procedure to obtain a numerically straightforward baseline shift removal. This is successfully applied to proteomics and genmomics MALDI-TOF spectra. Evolutionary computation (EC) can discover (optimize, tune) aspects of the algorithm, for example, the free parameter σ (x) of the SB method. Since baseline drift affects many other types of instrumentation for poorly understood reasons, EC suggests an approach to customize the baseline removal algorithm.

关键词： Signal processing algorithms Proteomics Chemical lasers Ionization Mass spectroscopy Genomics bioinformatics Chemical processes Stochastic processes Function approximation

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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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ieee symposium on computational intelligence and bioinformatics and computational biology (CIBCB)

作者： Yuchun Tang Yuanchen He Yan-Qing Zhang Zhen Huang Xiaohua Hu R. Sunderraman Department of Computer Science Georgia State University Atlanta GA USA Department of Chemistry and Department of Biology Georgia State University Atlanta GA USA College of Information Science and Technology Drexel University Philadelphia PA USA

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.

关键词： Gene expression Cancer Competitive intelligence Hybrid intelligent systems computational intelligence Biomedical computing computational modeling Data mining Accuracy Drugs

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Reverse Engineering Non-Linear Gene Regulatory Networks Based on the Bacteriophage λ cI Circuit

Reverse Engineering Non-Linear Gene Regulatory Networks Base...

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ieee symposium on computational intelligence and bioinformatics and computational biology (CIBCB)

作者： J. Supper C. Spieth A. Zell Centre for Bioinformatics (ZBIT) University of Tübingen Tubingen Germany

The ability to measure the transcriptional response of cells has drawn much attention to the underlying transcriptional networks. To untangle the network, numerous models with corresponding reverse engineering methods have been applied. In this work, we propose a non-linear model with adjustable degrees of complexity. The corresponding reverse engineering method uses a probabilistic scheme to reduce the reconstruction problem to subnetworks. Adequate models for gene regulatory networks must be anchored on sufficient biological knowledge. Here, the cI auto-inhibition circuit (cI circuit) is used to validate our reverse engineering method. Simulations of the cI circuit are used for the reconstruction, whereas a simplified cI circuit model assists the modeling phase. Several levels of complexity are evaluated, subsequently the reconstructed models show different properties. As a result, we reconstruct an abstract model, capturing the dynamic behavior of the cI circuit to a high degree.

关键词： Reverse engineering Biological system modeling Kinetic theory Stochastic processes Immune system bioinformatics Circuit simulation Animation Gene expression Mathematical model

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Predicting Structural Disruption of Proteins Caused by Crossover

Predicting Structural Disruption of Proteins Caused by Cross...

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ieee symposium on computational intelligence and bioinformatics and computational biology (CIBCB)

作者： D.C. Bauer M. Boden R. Thier Zheng Yuan School of Information Technology and Electrical Engineering University of Queensland QLD Australia School of Biomedical Sciences University of Queensland QLD Australia Institute for Molecular Bioscience and ARC Centre in Bioinformatics University of Queensland QLD Australia

We present a machine learning model that predicts a structural disruption score from a protein’s primary structure. SCHEMA was introduced by Frances Arnold and colleagues as a method for determining putative recombination sites of a protein on the basis of the full (PDB) description of its structure. The present method provides an alternative to SCHEMA that is able to determine the same score from sequence data only. Circumventing the need for resolving the full structure enables the exploration of yet unresolved and even hypothetical sequences for protein design efforts. Deriving the SCHEMA score from a primary structure is achieved using a two step approach: first predicting a secondary structure from the sequence and then predicting the SCHEMA score from the predicted secondary structure. The correlation coefficient for the prediction is 0.88 and indicates the feasibility of replacing SCHEMA with little loss of precision.

关键词： Predictive models Machine learning Protein engineering Information technology bioinformatics Australia Amino acids Design methodology In vitro Crystallography

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Advances in bioinformatics and computational biology - Brazilian symposium on bioinformatics, BSB 2005, Proceedings: Preface

Lecture Notes in Computer Science (including subseries Lectu...

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Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial intelligence and Lecture Notes in bioinformatics) 2005年 3594 LNBI卷 v页

作者： Setubal, Joao Carlos Verjovski-Almeida, Sergio

No abstract available

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Pathway Optimization of Biological Drug Response Networks

Pathway Optimization of Biological Drug Response Networks

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ieee symposium on computational intelligence and bioinformatics and computational biology (CIBCB)

作者： Chien-Feng Huang M. Tiee C.V. Forst Bioscience Division Los Alamos National Laboratory Los Alamos NM USA

One of the major challenges in systems biology today is to devise generally robust methods of interpreting data concerning the expression levels of the genes in an organism in a way that will shed light on the complex relationships between multiple genes and their products. The ability to better understand and predict the structures and actions of complex biological systems is of significant importance to modern drug discovery as well as our understanding of the mechanisms behind an organism’s ability to react to its environment. In this paper we present a study for robust biological pathway construction through genetic algorithms. The platform is based on the construction of biological networks given different sets of interaction information and the optimization of sub-networks constrained by the gene expression data. As an application, expression data of drug response in M. tuberculosis is used to build generic response subnetworks. Subnetworks are then compared to identify the essential key components that are common to different networks. We are thus able to identify essential nodes in specific drug response. We expect that this approach will provide robust prediction of response networks and accelerate target identification for drug development in the future.

关键词： Drugs Biochemistry Proteins Systems biology Robustness Biological systems Organisms Gene expression Genomics bioinformatics

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Feedback Memetic Algorithms for Modeling Gene Regulatory Networks

Feedback Memetic Algorithms for Modeling Gene Regulatory Net...

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ieee symposium on computational intelligence and bioinformatics and computational biology (CIBCB)

作者： C. Spieth F. Streichert J. Supper N. Speer A. Zell Centre for Bioinformatics University of Tübingen Tubingen Germany

In this paper we address the problem of finding gene regulatory networks from experimental DNA microarray data. We focus on the evaluation of the performance of memetic algorithms on the inference problem. These algorithms are used to evolve an underlying quantitative mathematical model. The dynamics of the regulatory system are modeled with two commonly used approaches, namely linear weight matrices and S-systems. Due to the complexity of the inference problem, some researchers suggested evolutionary algorithms for this purpose. We introduce memetic enhancements to this optimization process to infer the parameters of sparsely connected nonlinear systems from the observed data. Due to the limited number of available data, the inferring problem is underdetermined and ambiguous. Further on, the problem often is multimodal and therefore appropriate optimization strategies become necessary. We propose a memetic method, which separates the overall inference problem into two subproblems to find the correct network: first, the search for a valid topology, and secondly, the optimization of the parameters of the mathematical model. The performance and the properties of the proposed methods are evaluated and compared to standard algorithms found in the literature.

关键词： Feedback Biological system modeling Mathematical model Inference algorithms DNA Nonlinear dynamical systems Evolutionary computation Network topology Systems biology Differential equations

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Multi-Class Protein Subcellular Localization Prediction using Support Vector Machines

Multi-Class Protein Subcellular Localization Prediction usin...

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ieee symposium on computational intelligence and bioinformatics and computational biology (CIBCB)

作者： Peng Wai Meng J.C. Rajapakse School of Engineering Temasek Polytechnic Singapore BioInformatics Research Centre School of Computer Engineering Nanyang Technological University Singapore

Prediction of protein subcellular localization from amino acid sequence is an important step towards elucidating the function of a protein. Here, we present an approach for predicting protein subcellular localizations from eukaryotic sequences using Support Vector Machines. Apart from using amino acid compositions, our prediction approach also considers biochemical characteristics of amino acids and their distribution patterns along the primary sequence of the query proteins. Consequently, improved predictive accuracy has been achieved on the Reinhardt and Hubbard’s dataset. For the four subcellular localizations of eukaryotic proteins, the total prediction accuracy obtained using the “ leave-one-out” cross-validation test is 88.88%. To the best of our knowledge, our approach obtained by far the best prediction accuracy for mitochondrial proteins, which are notoriously difficult to predict among eukaryotic proteins. Performance comparison results also showed that our approach outperformed existing protein subcellular localization prediction methods based solely on amino acid composition.

关键词： Support vector machines Amino acids Support vector machine classification Kernel Sequences Encoding Protein engineering Electronic mail Polynomials bioinformatics

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Handling Data Sparseness in Gene Network Reconstruction

Handling Data Sparseness in Gene Network Reconstruction

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ieee symposium on computational intelligence and bioinformatics and computational biology (CIBCB)

作者： G.B. Bezerra T.V. Barra F.J. von Zuben L.N. de Castro Laboratory of Bioinformatics and Bio-Inspired Computing (LBiC) Department of Computer Engineering and Industrial Automation State University of Campinas-UNICAMP Sao Paulo Brazil Graduate Program on Informatics Catholic University of Santos Sao Paulo Brazil

One of the main problems related to regulatory network reconstruction from expression data concerns the small size and low quality of the available dataset. When trying to infer a model from little information it is necessary to give much more precedence to generalization, rather than specificity, otherwise, any attempt will be fated to overfitting. In this paper we address this issue by focusing on data sparseness and noisy information, and propose a density estimation technique that achieves regularized curves when data is scarce. We first compare the proposed method with the EM algorithm for mixture models on density estimation problems. Next, we apply the method, together with Bayesian networks, on realistic simulations of static gene networks, and compare the obtained results with the standard discrete Bayesian network model. We intend to demonstrate that adopting a discrete approach is not justifiable when only a small amount of information is available.

关键词： Intelligent networks Bayesian methods Genetics bioinformatics Computer networks Reverse engineering Cellular networks Noise robustness Laboratories Data engineering

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