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Support Vector Machine methods for the prediction of cancer growth

Support Vector Machine methods for the prediction of cancer ...

3rd International Joint Conference on Computational Sciences and Optimization, CSO 2010: Theoretical Development and Engineering Practice

作者： Chen, Xi Ching, Wai-Ki Aoki-Kinoshita, Kiyoko F. Furuta, Koh Advanced Modeling and Applied Computing Laboratory Department of Mathematics University of Hong Kong Hong Kong Hong Kong Department of Bioinformatics Faculty of Engineering Soka University Tokyo Japan Division of Clinical Laboratories National Cancer Center Hospital Tokyo Japan

ISBN: (纸本)9780769540306

In this paper, we study the application of Support Vector Machine (SVM) in the prediction of cancer growth. SVM is known to be an efficient method and it has been widely used for classification problems. Here we propose a classifier which can differentiate patients having different levels of cancer growth with a high classification rate. To further improve the accuracy of classification, we propose to determine the optimal size of the training set and perform feature selection using rfe-gist, a special function of SVM. © 2010 IEEE.

关键词： Support vector machines

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TOPSAN: A collaborative annotation environment for structural genomics

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BMC bioinformatics 2010年第1期11卷 1-11页

作者： Weekes, Dana Krishna, S.S. Bakolitsa, Constantina Wilson, Ian A. Godzik, Adam Wooley, John Joint Center for Structural Genomics Bioinformatics Core Sanford-Burnham Medical Research Institute 10901 N. Torrey Pines Road La Jolla CA 92037 United States Joint Center for Structural Genomics The Scripps Research Institute 10550 N. Torrey Pines Road La Jolla CA 92037 United States Joint Center for Structural Genomics Bioinformatics Core Center for Research in Biological Systems University of California San Diego 9500 Gilman Drive La Jolla CA 92093 United States Joint Center for Molecular Modeling Sanford-Burnham Medical Research Institute 10901 N. Torrey Pines Road La Jolla CA 92037 United States

Background: Many protein structures determined in high-throughput structural genomics centers, despite their significant novelty and importance, are available only as PDB depositions and are not accompanied by a peer-reviewed manuscript. Because of this they are not accessible by the standard tools of literature searches, remaining underutilized by the broad biological ***: To address this issue we have developed TOPSAN, The Open Protein Structure Annotation Network, a web-based platform that combines the openness of the wiki model with the quality control of scientific communication. TOPSAN enables research collaborations and scientific dialogue among globally distributed participants, the results of which are reviewed by experts and eventually validated by peer review. The immediate goal of TOPSAN is to harness the combined experience, knowledge, and data from such collaborations in order to enhance the impact of the astonishing number and diversity of structures being determined by structural genomics centers and high-throughput structural ***: TOPSAN combines features of automated annotation databases and formal, peer-reviewed scientific research literature, providing an ideal vehicle to bridge a gap between rapidly accumulating data from high-throughput technologies and a much slower pace for its analysis and integration with other, relevant research. © 2010 Weekes et al;licensee BioMed Central Ltd.

关键词： Quality control

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Delay Discrete Dynamical Models for Genetic Regulatory Networks

Delay Discrete Dynamical Models for Genetic Regulatory Netwo...

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第四届最优化与系统生物学国际研讨会

作者： Hao Jiang Wai-Ki Ching Kiyoko F.Aoki-Kinoshita DianJing Guo Advanced Modeling and Applied Computing Laboratory Department of Mathematics The University of Hong Kong Department of Bioinformatics Faculty of Engineering Soka UniversityTokyo Japan Department of Biology The Chinese University of Hong Kong

In this paper, we study the problem of constructing a regulatory network of yeast in oxidative stress process. Discrete Dynamic System (DDS) model has been introduced in describing Gene Regulatory Networks (GRNs). However, delay effect was not taken into consideration within the model. A Time-delay DDS model composed of linear difference equations is developed to represent temporal interactions among significantly expressed genes. Interpolation and re-sampling are imposed to equalize the non-uniformity of sampling time points. Statistical significance plays an active role in obtaining the optimal interaction matrix of GRNs. The constructed gene network using linear multiple regression has a very good match with the original data. Simulation results are given to demonstrate the effectiveness of our proposed model.

关键词： Gene Regulatory Networks Linear Multiple Regression Discrete Dynamic System Model k-means Clustering

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Exploratory analysis of the BioAssay Network with implications to therapeutic discovery

Exploratory analysis of the BioAssay Network with implicatio...

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IEEE International Conference on bioinformatics and Biomedicine (BIBM)

作者： Jintao Zhang Gerald H. Lushington Jun Huan Center of Bioinformatics University of Kansas Lawrence KS USA Molecular Graphics & Modeling Laboratory University of Kansas Lawrence KS USA Department of Electrical Engineering and Computer Sciences University of Kansas Lawrence KS USA

Despite intense investment growth and technology development, there is an observed bottleneck in drug discovery and development over the past decade. NIH started the Molecular Libraries Initiative (MLI) in 2004 to enlarge the pool for potential drug targets, especially from the “undruggable” part of human genome, and potential drug candidates from much broader types of drug-like small molecules. In this paper we used the concepts of network biology to integrate MLI data with other biological databases such as DrugBank and UniHI, and evaluated the potential of MLI target proteins being new drug targets. Our analysis provided some measures of the value of the MLI data as a resource for both basic chemical biology research and future therapeutic discovery.

关键词： Drugs Proteins Humans Compounds bioinformatics Databases

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Support Vector Machine Methods for the Prediction of Cancer Growth

Support Vector Machine Methods for the Prediction of Cancer ...

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The Third International Joint Conference on Computational Science and Optimization(第三届计算科学与优化国际大会 CSO 2010)

作者： Wai-Ki Ching Xi Chen Kiyoko F. Aoki-Kinoshita Koh Furuta The Advanced Modeling and Applied Computing Laboratory Department of Mathematics The University of H Department of Bioinformatics Faculty of Engineering Soka University Tokyo Japan Division of Clinical Laboratories National Cancer Center Hospital Tokyo Japan

ISBN: (纸本)9781424468126;9780769540306

关键词： Support vector machines Cancer Support vector machine classification Metastasis Training data Neoplasms Degradation Lymph nodes Diseases Optimization methods

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Visualisation of cyclic and multi-branched molecules with VMD

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Journal of Molecular Graphics and Modelling 2009年第2期28卷 131-139页

作者： Cross, Simon Kuttel, Michelle M. Stone, John E. Gain, James E. Computer Science Department University of Cape Town Private Bag X3 Rondebosch 7701 South Africa Theoretical and Computational Biophysics Group NIH Resource for Macromolecular Modeling and Bioinformatics University of Illinois at Urbana Champaign IL United States

We report the addition of two visualisation algorithms, termed PaperChain and Twister, to the freely available Visual Molecular Dynamics (VMD) package. These algorithms produce visualisations of complex cyclic molecules and multi-branched polysaccharides and are a generalization and optimization of those we previously developed in a standalone package for carbohydrates. PaperChain highlights each ring in a molecular structure with a polygon, which is coloured according to the ring pucker. Twister traces glycosidic bonds with a ribbon that twists according to the relative orientation of successive sugar residues. Combination of these novel algorithms and new ring selection statements with the large set of visualisations already available in VMD allows for unprecedented flexibility in the level of detail displayed for glycoconjugate, glycoprotein and carbohydrate-binding protein structures, as well as other cyclic structures. We highlight the efficacy of these algorithms with selected illustrative examples, clearly demonstrating the value of the new visualisations, not only for structure validation, but also for facilitating insights into molecular structure and mechanism. © 2009 Elsevier Inc. All rights reserved.

关键词： Molecular dynamics

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Content-based image retrieval by indexing random subwindows with randomized trees

Content-based image retrieval by indexing random subwindows ...

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作者： Marée, Raphaël Geurts, Pierre Wehenkel, Louis GIGA Bioinformatics Platform University of Liège Belgium Systems and Modeling Unit Department of Electrical Engineering and Computer Science University of Liège Belgium FNRS Belgium

We propose a new method for content-based image retrieval which exploits the similarity measure and indexing structure of totally randomized tree ensembles induced from a set of subwindows randomly extracted from a sample of images. We also present the possibility of updating the model as new images come in, and the capability of comparing new images using a model previously constructed from a different set of images. The approach is quantitatively evaluated on various types of images and achieves high recognition rates despite its conceptual simplicity and computational efficiency. © 2009 Information Processing Society of Japan.

关键词： Computational efficiency

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Efficient computation of impact degrees for multiple reactions in metabolic networks with cycles

Efficient computation of impact degrees for multiple reactio...

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3rd ACM International Workshop on Data and Text Mining in bioinformatics, DTMBIO'09, Co-located with the 18th ACM International Conference on Information and Knowledge Management, CIKM 2009

作者： Cong, Yang Tamura, Takeyuki Akutsu, Tatsuya Ching, Wai-Ki Advanced Modeling and Applied Computing Laboratory Department of Mathematics University of Hong Kong Pokfulam Road Hong Kong Hong Kong Bioinformatics Center Institute for Chemical Research Kyoto University Gokasho Uji Kyoto 611-0011 Japan

ISBN: (纸本)9781605588032

Analysis of the robustness of a metabolic network against of single or multiple reaction(s) is useful for mining important enzymes/genes. For that purpose, the impact degree was proposed by Jiang et al. In this short paper, we extend the impact degree for metabolic networks containing cycles and develop a simple algorithm for its computation. Furthermore, we propose an improved algorithm for computing impact degrees for deletions of multiple reactions. The results of preliminary computational experiments suggest that the improved algorithm is several tens of times faster than a simple algorithm. Copyright 2009 ACM.

关键词： Metabolism

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The Voronoi Diagram of Half-Balls and its Application to the Prediction of the 3D Structure of Proteins

The Voronoi Diagram of Half-Balls and its Application to the...

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International Symposium on Voronoi Diagrams in Science and Engineering (ISVD)

作者： François Anton Thomas Hamelryck Department of Informatics and Mathematical Modeling Technical University of Denmark Kongens Lyngby Denmark Bioinformatics Center University of Copenhagen Copenhagen Denmark

The calculation of the overlap volume of half-spheres or ellipsoids is of direct interest in structural bioinformatics, which is concerned with the computational study of biological macromolecules on a genomic scale. We present an algorithm for computing the Delaunay graph and the overlap volume of a set of half-balls using exact predicates that detect the disjointness or non-disjointness of two half-balls and the validity of the generalized Voronoi vertex of four half-balls using geometric invariants and action (multiplication map) matrices. We prove the correctness of the algorithm and the optimality of the degree of the predicates by using geometric invariants and Gro¿bner bases. The main application of these certified computations is to predict the 3D structure of proteins.

关键词： Proteins bioinformatics Ellipsoids Biology computing Biological system modeling Solvents Molecular biophysics Genomics Spine Polymers

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Electronic structure and magnetic properties of Ni3nAln clusters

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Physical Review B 2009年第12期80卷 125419-125419页

作者： Vaishali Shah D. G. Kanhere Institute of Bioinformatics and Biotechnology University of Pune Pune 411 007 India Department of Physics University of Pune Pune 411 007 India Centre for Modeling and Simulation University of Pune Pune 411 007 India

The equilibrium structure, electronic, and magnetic properties of Ni3nAln, (n=1, 8) clusters are investigated using ab initio total-energy calculations based on density functional theory. Asymmetric and amorphous structures are observed for n>1 clusters, in contrast with the earlier work reported based on Gupta potentials; where, clusters with Ni3Al compositions were found to be symmetric. Magnetic moment per atom in these clusters is significantly enhanced with respect to the bulk. The distribution of magnetic charge on Ni and Al atoms is highly inhomogeneous and depends on their number of Al and Ni neighbors. The Al atoms quench the magnetic moments of Ni3nAln clusters when compared with the magnetic moments of pure Ni clusters. The analysis of the charge density shows a net transfer of charge from s-type orbital of Ni to p-type orbital of Al. The density of states exhibits features like Heusler alloys. The implications of this on the conductance in such clusters are discussed.

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