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A multi-objective genetic algorithm for discovering non-dominated motifs in DNA sequences

A multi-objective genetic algorithm for discovering non-domi...

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7th International conference on Hybrid Intelligent systems, HIS 2007

作者： Kaya, Mehmet Data Mining and Bioinformatics Laboratory Department of Computer Engineering Firat University 23119 Elazig Turkey

ISBN: (纸本)0769529461

This paper presents a novel motif discovery algorithm based on multi-objective genetic algorithms to extract non-dominated motifs in DNA sequences. The main advantage of our approach is that a large number of tradeoff (non-dominated) motifs can be obtained by a single run with respect to conflicting objectives: similarity, motif length and support maximization. In this paper, the method extracts non-dominated motifs taking into account two-objective at a time while one of the objectives is set to a prespecified value. So, user is given to the authority of incorporating to motif discovery process. Our approach can be applied to any data set with a sequential character. Furthermore, it allows any choice of similarity measures for finding motifs. By analyzing the discovered non-dominated motifs, the decision maker can understand the tradeoff between the objectives. We compare the approach with the three well-known motif discovery methods, AlignACE, MEME and Weeder. Experimental results on real data set extracted from TRANSFAC database demonstrate that the proposed method exhibits good performance over the other methods in terms of runtime and accuracy of prediction. © 2007 ieee.

关键词： DNA sequences

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Anton, a Special-Purpose Machine for Molecular Dynamics Simulation 07

Anton, a Special-Purpose Machine for Molecular Dynamics Simu...

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34th Annual International Symposium on Computer Architecture

作者： Shaw, David E. Deneroff, Martin M. Dror, Ron O. Kuskin, Jeffrey S. Larson, Richard H. Salmon, John K. Young, Cliff Batson, Brannon Bowers, Kevin J. Chao, Jack C. Eastwood, Michael P. Gagliardo, Joseph Grossman, J. P. Ho, C. Richard Ierardi, Douglas J. Kolossvary, Istvan Klepeis, John L. Layman, Timothy McLeavey, Christine Moraes, Mark A. Mueller, Rolf Priest, Edward C. Shan, Yibing Spengler, Jochen Theobald, Michael Towles, Brian Wang, Stanley C. DE Shaw Res LLC New York NY 10036 USA

ISBN: (纸本)9781595937063

The ability to perform long, accurate molecular dynamics (MD) simulations involving proteins and other biological macromolecules could in principle provide answers to some of the most important currently outstanding questions in the fields of biology, chemistry and medicine. A wide range of biologically interesting phenomena, however, occur over time scales on the order of a millisecond-about three orders of magnitude beyond the duration of the longest current MD simulations. In this paper, we describe a massively parallel machine called Anton, which should be capable of executing millisecond-scale classical MD simulations of such biomolecular systems. The machine, which is scheduled for completion by the end of 2008, is based on 512 identical MD-specific ASICs that interact in a tightly coupled manner using a specialized high-speed communication network. Anton has been designed to use both novel parallel algorithms and special-purpose logic to dramatically accelerate those calculations that dominate the time required for a typical MD simulation. The remainder of the simulation algorithm is executed by a programmable portion of each chip that achieves a substantial degree of parallelism while preserving the flexibility necessary to accommodate anticipated advances in physical models and simulation methods.

关键词： Molecular dynamics Special-purpose machine Biomolecular system simulation computational biology Protein structure Protein folding computational drug design bioinformatics

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Decision fusion of circulating markers for breast cancer detection in premenopausal women

Decision fusion of circulating markers for breast cancer det...

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7th ieee International conference on bioinformatics and Bioengineering, BIBE

作者： Jesneck, Jonathan L. Mukherjee, Sayan Nolte, Loren W. Lokshin, Anna E. Marks, Jeffrey R. Lo, Joseph Department of Biomedical Engineering Department of Statistical Science Computational Biology and Bioinformatics Program Duke Advanced Imaging Labs. Institute for Genome Sciences and Policy Department of Computer Science Duke University Department of Electrical and Computer Engineering Division of Hematology/Oncology University of Pittsburgh School of Medicine University of Pittsburgh Cancer Institute Pittsburgh PA 15213 United States Department of Experimental Surgery Duke University Medical Center Medical Physics Graduate Program Duke University Durham NC 27708 United States

ISBN: (纸本)1424415098

Current mammographic screeningfor breast cancer is less effective for younger women. To complement mammography for premenopausal women, we investigated the feasibility screening test using 98 blood serum proteins. Because the data set was very noisy and contained only weak features, we used a classifier designed for noisy data: decision fusion. Decision fusion outperformed both a support vector machine (SVM) and linear regression with forward stepwise feature selection on all three two-class classification tasks: normal tissue vs. cancer, normal tissue vs. benign lesions, and benign lesions vs. cancer. Decision fusion detected cancer moderately well (AUC=0.84 on normal vs. cancer), demonstrating promise as a screening tool. Decision fusion also detected benign lesions similarly well (AUC=0.83 on normal vs. benign lesions) and was the only classifier to achieve any success in separating benign from malignant lesions (AUC=0.64 on benign vs. cancer). The classification results suggest that the assayed proteins are more indicative of a secondary effect, such as immune response, rather than specific for breast cancer. In conclusion, the decision fusion classifier demonstrated some promise in detecting breast lesions and outperformed other classifiers, especially for the very noisy classification problem of distinguishing benign from malignant lesions. ©2007 ieee.

关键词： Tissue

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computational grid-based 3-tier ART1 data mining for bioinformatics applications

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3rd International conference on Fuzzy systems and Knowledge Discovery

作者： Cho, Kyu Cheol Park, Da Hye Lee, Jong Sik Inha Univ Sch Comp Sci & Engn Nam Ku Inchon 402751 South Korea

ISBN: (纸本)3540459162

computational Grid technology has been noticed as an issue to solve large-scale bioinformatics-related problems and improves data accuracy and processing speed on multiple computation platforms with distributed bioDATA sets. This paper focuses on a GPCR data mining processing which is an important bioinformatics application. This paper proposes a Grid-based 3-tier ART1 classifier which operates an ART1 clustering data mining using grid computational resources with distributed GPCR data sets. This Grid-based 3-tier ART1 classifier is able to process a large-scale bioinformatics application in guaranteeing high bioDATA accuracy with reasonable processing resources. This paper evaluates performance of the Grid-based ART1 classifier in comparing to the ART1-based classifier and the ART1 optimum classifier. The data mining processing time of the Grid-based ART1 classifier is 18% data mining processing time of the ART1 optimum classifier and is the 12% data mining processing time of the ART1-based classifier. And we evaluate performance of the Grid-based 3-tier ART1 classifier in comparing to the Grid-based ART1 classifier. As data sets become larger, data mining processing time of the Grid-based 3-tier ART1 classifier more decrease than that of the Grid-based ART1 classifier. computational Grid in bioinformatics applications gives a great promise of high performance processing with large-scale and geographically distributed bioDATA sets.

关键词： Data mining

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Locality and parallelism optimization for dynamic programming algorithm in bioinformatics

Locality and parallelism optimization for dynamic programmin...

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2006 ACM/ieee conference on Supercomputing, SC'06

作者： Tan, Guangming Feng, Shengzhong Sun, Ninghui Key Laboratory of Computer System and Architecture Institute of Computing Technology Chinese Academy of Sciences Graduate School Chinese Academy of Sciences

ISBN: (纸本)0769527000

Dynamic programming has been one of the most efficient approaches to sequence analysis and structure prediction in biology. However, their performance is limited due to the drastic increase in both the number of biological data and variety of the computer architectures. With regard to such predicament, this paper creates excellent algorithms aimed at addressing the challenges of improving memory efficiency and network latency tolerance for nonserial polyadic dynamic programming where the dependences are nonuniform. By relaxing the nonuniform dependences, we proposed a new cache oblivious scheme to enhance its performance on memory hierarchy architectures. Moreover we develop and extend a tiling technique to parallelize this nonserial polyadic dynamic programming using an alternate block-cyclic mapping strategy for balancing the computational and memory load, where an analytical parameterized model is formulated to determine the tile volume size that minimizes the total execution time and an algorithmic transformation is used to schedule the tile to overlap communication with computation to further minimize communication overhead on parallel architectures. The numerical experiments were carried out on several high performance computer systems. The new cache-oblivious dynamic programming algorithm achieve 2-10 speedup and the parallel tiling algorithm with communication-computation overlapping shows a desired potential for fine-grained parallel computing on massively parallel computer systems. © 2006 ieee.

关键词： Dynamic programming

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Front gene to drug: A proof of concept for a plausible computational pathway

Front gene to drug: A proof of concept for a plausible compu...

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ISDA 2006: Sixth International conference on Intelligent systems Design and Applications

作者： Shenoy, Sandhya Jayaram, B. Latha, N. Narang, Pooja Jain, Tarun Bhushan, Kumkum Shaikh, Saher Afshan Bose, Surojit Sharma, Pankaj Singhal, Poonam Gandhimathi, A. Agrawal, Praveen Pandey, Vidhu Dutta, Samrat Sandhu, Gurvisha Gupta, Anuj Shekhar, Shashank Tripathi, Shailesh Department of Chemistry Supercomputing Facility for Bioinformatics and Computational Biology Indian Institute of Technology Hauz Khas New Delhi-110016 India

ISBN: (纸本)0769525288

The last decade has witnessed an exponential growth of sequence information in the field of biological macromolecules such as proteins and nucleic acids and their interactions with other molecules. computational analyses for structure-function predictions based on such information are increasingly becoming an essential and integral part of modern biology. With rapid advances in the area, there is a growing need to develop efficient versatile bioinformatics software packages, which are hypothesis driven. 'Gene to Drug' is an attempt in this pursuit and an integration of heterologous applications of different technologies developed in-house and their translation into in silico products that cater to a majority of bioinformatics applications. This paper briefly presents the science behind Gene to Drug and how grid services and high performance computing platforms can be harnessed to bridge the gap between biomolecular sequence, structure and function. © 2006 ieee.

关键词： Macromolecules

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proceedings 2005 ieee computational systems bioinformatics conference

Proceedings 2005 IEEE Computational Systems Bioinformatics C...

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2005 ieee computational systems bioinformatics conference, csb 2005

ISBN: (纸本)0769523447

The proceedings contain 42 papers. The topics discussed include: challenges in creating an infrastructure for physics-based simulation of biological structures;ontologies for biologists - a community model for the annotation of genomic data;the center for computational biology;gene teams with relaxed proximity constraint;accurate prediction of orthologous gene groups in microbes;discriminative discovery of transcription factor binding sites from location data;identification of post-translational modifications via blind search of mass-spectra;peptide charge state determination for low-resolution tandem mass spectra;deformable modeling for improved calculation of molecular velocities from single-particle tracking;tree decomposition based fast search of RNA structures including pseudoknots in genomes;and investigation into biomedical literature classification using support vector machines.

关键词： Information technology

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Locality and Parallelism Optimization for Dynamic Programming Algorithm in bioinformatics

Locality and Parallelism Optimization for Dynamic Programmin...

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Supercomputing conference

作者： Guangming Tan Shengzhong Feng Ninghui Sun Graduate School of Chinese Academy of Sciences China Key Laboratory of Computer System and Architecture Institute of Computing Technology Chinese Academy and Sciences China

关键词： Dynamic programming Heuristic algorithms bioinformatics Concurrent computing Parallel processing Computer architecture Tiles Sequences Delay Memory architecture

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proceedings. 2005 ieee computational systems bioinformatics conference

Proceedings. 2005 IEEE Computational Systems Bioinformatics ...

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proceedings of the ieee computational systems bioinformatics conference, csb

The following topics are dealt with: biology computing; genetics; microorganisms; pattern classification; pattern clustering; phylogeny and evolution; transcriptomes; proteomics; structural bioinformatics; pathways an...

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proceedings of the 2005 ieee computational systems bioinformatics conference Workshops(csbW'05)

Proceedings of the 2005 IEEE Computational Systems Bioinform...

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2005 ieee computational systems bioinformatics conference, Workshops and Poster Abstracts

ISBN: (纸本)0769524427

The proceedings contain 127 papers. The topics discussed include: sequential classification for microarray and clinical data;efficient image texture analysis and classification for prostate ultrasound diagnosis;diagnosis and biomarker identification on SELDI proteomics data by ADTboost;inverse design of large molecules using linear Diophantine equations;reducing gene regulatory networks by decomposition;a diffusion model to estimate the inter-arrival time of charged molecules in stochastic event based modeling of complex biological networks;operon prediction in cyanobacteria using comparative genomics;patterns of gene deletion following genome duplication in yeast;classification methods for HIV-1 medicated neuronal damage;key features of the UCSC genome site;data integration in the Mouse Genome Informatics (MGI) database;HIV structural and biothermodynamics databases: A resource for the pharmaceutical and biotechnology industry;a new clustering strategy with stochastic merging and removing based on kernel functions;head and neck cancer metastasis prediction via artificial neural networks;and problem solving environment approach to integrating diverse biological data sources.

关键词： Biotechnology

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