Computational Systems Bioinformatics : CSB2007 Conference Proceedings - University of California, San Diego, 13-17 August 2007.

By: Markstein, PeterContributor(s): Xu, YingMaterial type: TextTextSeries: Series on Advances in Bioinformatics and Computational BiologyPublisher: Singapore : Imperial College Press, 2007Copyright date: ©2007Description: 1 online resource (472 pages)Content type: text Media type: computer Carrier type: online resourceISBN: 9781860948732Subject(s): Bioinformatics -- Congresses | Biological systems -- Computer simulation -- Congresses | Biological systems -- Simulation methods -- Congresses | Computational biology -- CongressesGenre/Form: Electronic books.Additional physical formats: Print version:: Computational Systems Bioinformatics : CSB2007 Conference Proceedings - University of California, San Diego, 13-17 August 2007DDC classification: 572 LOC classification: QH324.2.C636 2007Online resources: Click to View
Contents:
Intro -- CONTENTS -- Preface -- Committees -- Referees -- Keynote Address -- Quantitative Aspects of Gene Regulation in Bacteria: Amplification. Threshold, and Combinatorial Control Terry Hwa -- Whole-Genome Analysis of Dorsal Gradient Thresholds in the Drosophila Embryo Julia ZeitlingeK Rob Zinzen, Dmitri Papatsenko et al. -- Invited Talks -- Learning Predictive Models of Gene Regulation Christina Leslie -- The Phylofacts Phylogenomic Encyclopedias: Structural Phylogenomic Analysis Across the Tree of Life Kimmen Golander -- Mapping and Analysis of the Human Interactome Network Kavitha Venkatesan -- 1. INTRODUCTION -- Gene-Centered Protein-DNA lnteractome Mapping A.J. Marian Walhout -- Proteomics -- Algorithm for Peptide Sequencing by Tandem Mass Spectrometry Based on Better Preprocessing and Anti-S ymmetric Computational Model Kang Ning and Hon Wai Leong -- 1. INTRODUCTION -- Preprocessing to remove noisy peaks -- The anti-symmetric problem -- 2. ANALYSIS OF PROBLEMS AND CURRENT ALGORITHMS -- 2.1. General Terminologies -- 2.2. Datasets -- 2.3. Problems Analysis -- 3. NEW COMPUTATIONAL MODELS AND ALGORITHM -- 3.1. Preprocessing to remove noisy peaks and introduce pseudo peaks -- 3.2. The Anti-symmetric Problem -- 3.3. Novel Peptide Sequencing Algorithm -- 4. EXPERIMENTS -- 4.1. Experiment Settings -- 4.2. Results -- 5. CONCLUSIONS -- References -- Algorithms for Selecting Breakpoint Locations to Optimize Diversity in Protein Engineering by Site-Directed Protein Recombination Wei Zheng, Xiaoduan Ye, Alan A4 Friedman and Chris Bailey-Kellogg -- 1. INTRODUCTION -- 2. METHODS -- 2.1. Library Diversity -- 2.2. Metrics for Breakpoint Selection -- 2.3. Dynamic Programming for Breakpoint Selection -- 3. RESULTS A N D DISCUSSION -- 4. CONCLUSION -- ACKNOWLEDGMENTS -- References.
An Algorithmic Approach to Automated High-Throughput Identification of Disulfide Connectivity in Proteins Using Tandem Mass Spectrometry Timothy Lee, Rahul Singh, Ten-Yang Yen and Bruce Macher -- 1. INTRODUCTION -- 1.1. Comparison of the Proposed Approach with Related Works -- 2. THE PROPOSED METHOD -- 2.1. Problem Formulation -- 2.2. Algorithmic Framework -- 2.2.1. Finding the MS spectrum match -- 2.2.2. Finding the MS/MS spectrum match -- 2.2.3. Finding a perfect matching of maximum weight for a fully connected graph -- 2.2.4. Consideration of missed proteolytic cleavages and intra-molecular bonded cysteines -- 2.2.5. Peak finding in the presence of noise -- 2.2.6. Addressing isotopic variation and neutral loss -- 2.2.7. Interpretation of peaks given charge state uncertainty -- 2.2.8. Overall complexity -- 3. EXPERIMENTAL RESULTS -- 3.1. Description of the Data and Experimental Procedures -- 3.2. Summary of Results -- 3.2.1. Analysis of the effect of varying threshold t on results -- 3.2.2. Comparison with MS2Assign program -- 4. CONCLUSIONS AND DISCUSSION -- Acknowledgments -- References -- Biomedical Application -- Cancer Molecular Pattern Discovery by Subspace Consensus Kernel Classification Xiaoxu Hun -- 1. INTRODUCTION -- 1 .1. Nonnegative matrix factorization -- 1.2. Contributions -- 2. INPUT SPACE, SUBSPACE AND KERNEL CLUSTERING -- 2.1. Subspace clustering -- 2.2. Kernel space clustering: conduct clustering in a high dimension space with kernel tricks -- 2.3. What's the ideal unsupervised classification algorithm for the high dimensional gene/protein expression data? -- 3. PG-NMF SUBSPACE KERNEL HIERARCHICAL CLASS1 FlCATlON -- 4. EXPERIMENTS -- 4.1. Comparing classification results from kNN, sparse-NMF and support vector machines (SVM) -- 5. CONCLUSIONS -- Acknowledgments -- References.
Efficient Algorithms for Genome-Wide tagSNP Selection Across Populations via the Linkage Disequilibrium Criterion Lan Liu, Yonghui Wu, Stefano Lonardi and Tao Jiang -- 1. INTRODUCTION -- 2. FORMULATION OF THE MCTS PROBLEM -- 3. OPTIMIZATION TECHNIQUES TO SOLVE THE MCTS PROBLEM -- 3.1. Data Reduction Rules -- 3.2. A Greedy Algorithm for MCTS -- 3.3. A Lagrangian Relaxation Algorithm for MCTS -- 4. EXPERIMENTAL RESULTS -- 4.1. Tagging the ENCODE Regions -- 4.2. Genome-wide Tagging -- 5. CONCLUSION -- ACKNOWLEDGEMENT -- References -- Transcriptional Profiling of Definitive Endoderm Derived from Human Embryonic Stem Cells Huiqing Liu, Stephen Dalton and Xng Xu -- 1. INTRODUCTION -- 2. DATA -- 3. METHODS AND PRELIMINARY RESULTS -- 3.1. Markers of hESC-DE -- 3.2. Transcription factor identification -- 3.3. Transcriptional regulation in the different phases of DE formation -- 4. ON-GOING WORK AND CONCLUSION -- Acknowledgment -- References -- Pathways, Networks and Systems Biology -- Bayesian Integration of Biological Prior Knowledge into the Reconstruction of Gene Regulatory Networks with Bayesian Networks Dirk Husmeier and Adriano I.: Werhli -- 1. INTRODUCTION -- 2. METHODOLOGY -- 2.1. Biological prior knowledge -- 2.2. MCMC sampling scheme -- 3. DATA -- 3.1. Cytometry data -- 3.2. Synthetic data -- 3.3. Biological prior knowledge -- 4. SIMULATIONS -- 4.1. Motivation -- 4.2. Reconstructing the regulatory network -- 5. RESULTS AND DISCUSSION -- 6. CONCLUSION -- ACKNOWLEDGEMENT -- References -- Using Indirect Protein-Protein Interactions for Protein Complex Predication Hon Nian Chua, Kang Ning, Wing-Kin Sung et al. -- 1 INTRODUCTION -- 2 INTRODUCTION OF INDIRECT NEIGHBORS -- 3 PCP ALGORITHM -- 4 EXPERIMENTS -- 5 DISCUSSIONS AND CONCLUSIONS -- Acknowledgements -- References.
Finding Linear Motif Pairs from Protein Interaction Networks: A Probabilistic Approach Henry C.M. Leung, MH. Siu, S.M. Yiu et al. -- 1. INTRODUCTION -- 2. PROBLEM DEFINITION -- 2.1. Motif Representation -- 2.2. (/, @-Motif Pair Finding Problem -- 2.2.1. x-score -- 2.2.2. p-score -- 3. ALGORITHM -- 3.1. Exact Algorithm -- 3.2. Heuristics Algorithm -- 4. EXPERIMENTS -- 4.1. SH3 Domains Dataset -- 4.2. Yeast Dataset -- 4.3. Running Time Comparison -- 4.4. Simulated Data -- 5. CONCLUSION -- Acknowledgments -- References -- A Markov Model Based Analysis of Stochastic Biochemical Systems Preetam Ghosh, Samik Ghosh, Kalyan Basu and Sajial K. Das -- 1. INTRODUCTION -- 2. STOCHASTIC BIOCHEMICAL SYSTEM ANALYSIS -- 3. OUR MARKOV CHAIN FORMULATION -- 3.1. The MFPT concept -- 3.2. Computing the state transition probabilities and times -- 3.2.1. Monomolecular reactions -- 3.2.2. Bimolecular reactions -- 3.2.3. Reversible Reactions -- 3.3. Pruning the Markov Chain -- 3.4. Computing the total probability of reaching a final state -- 3.5. Computing the MFPT for reaching the final state -- 3.6. Approximating the Markov Chain: Reducing complexity at the cost of accuracy -- 4. RESULTS AND ANALYSIS -- 4.1. Enzyme-Kinetics system -- 4.2. Transcriptional Regulatory System -- 5 . DISCUSSION -- 6. CONCLUSION AND FUTURE DIRECTIONS -- References -- An Information Theoretic Method for Reconstructing Local Regulatory Network Modules from Polymorphic Samples Manjunatha Jagalur and David Kulp -- 1. INTRODUCTION -- 1.1. Markov Blanket -- 1.1.1. Incremental Association Markov Blanket -- 1.2. Bayesian Networks -- 1.3. QTG Model -- 2. METHODS -- 2.1. Mixed Type Bayesian Network Under Biological Constraints -- 2.2. Markov Blanket Inference -- 2.3. Gene regulatory network reconstruction -- 3. EXPERIMENTS AND RESULTS -- 3.1. Simulations -- 3.2. Biological Significance -- 4. DISCUSSION.
ACKNOWLEDGEMENT -- References -- Using Directed Information to Build Biologically Relevant Influence Arvind Rao, Alfred 0. Hero III, David J. States and James Douglas Engel -- 1. INTRODUCTION -- 2. GENE NETWORKS -- 3. PROBLEM SETUP -- 3.1. Phylogenetic Conservation of Binding Sites -- 4. DTI FORMULATION -- 5 . A NORMALIZED DTI MEASURE -- 6. KERNEL DENSITY ESTIMATION (KDE) -- 7. BOOTSTRAPPED CONFIDENCE I N T E RVA LS -- 8. SUMMARY OF ALGORITHM -- 9. RESULTS -- 9.1. Synthetic Network -- 9.2. Directed Network inference: Gata3 Regulation in Early Kidney Development -- 9.3. Directed Network Inference: T-cell Activation -- 9.4. Phylogenetic conservation of TFBS effectors -- CONCLUSIONS -- ACKNOWLEDGEMENTS -- References -- Discovering Protein Complexes in Dense Reliable Neighborhoods of Protein Interaction Networks Xiao-Li Li, Chuan-Sheng Foo and See-Kiong Ng -- 1. INTRODUCTION -- 2. RELATED WORKS -- 3. THE PROPOSED TECHNIQUES -- 3.1. Mining for dense subgraphs -- 3.1.1. Mining for local dense subgraphs -- 3.1.2. Merging for maximal dense neighborhoods -- 3.2. Filtering for reliable subgraphs -- 3.2.1. Computing reliability of protein interactions -- 3.3. The overall DECAFF algorithm -- 4. EXPERIMENTS -- 4.1. Reference complexes and evaluation metric -- 4.2. Comparative results -- 4.3. Effect of the hub removal routine -- 4.4. Effect of parameters w and y -- 4.5. Analysis of the predicted complexes -- 5. Conclusions -- Acknowledgments -- References -- Mining Molecular Contexts of Cancer via In-Silico Conditioning Seungchan Kim, Ina Sen and Micheal Bittner -- 1. INTRODUCTION -- 2. METHODS -- 2.1. Identification of cellular contexts -- 2.2. Consistency statistics : interference and crosstalk -- 2.3. Interrogating contexts via in-silico conditioning -- 2.4. Significance test for identified contexts -- 2.5. Data quantization -- 2.6. Data quantization.
3. EVALUATION OF THE ALGORITHM.
Summary: This volume contains about 40 papers covering many of the latest developments in the fast-growing field of bioinformatics. The contributions span a wide range of topics, including computational genomics and genetics, protein function and computational proteomics, the transcriptome, structural bioinformatics, microarray data analysis, motif identification, biological pathways and systems, and biomedical applications. Abstracts from the keynote addresses and invited talks are also included. The papers not only cover theoretical aspects of bioinformatics but also delve into the application of new methods, with input from computation, engineering and biology disciplines. This multidisciplinary approach to bioinformatics gives these proceedings a unique viewpoint of the field. Sample Chapter(s). Chapter 1: Whole-Genome Analysis of Dorsal Gradient Thresholds in the Drosophila Embryo (102 KB). Contents: Learning Predictive Models of Gene Regulation (C Leslie); Algorithms for Selecting Breakpoint Locations to Optimize Diversity in Protein Engineering by Site-Directed Protein Recombination (W Zheng et al.); Cancer Molecular Pattern Discovery by Subspace Consensus Kernel Classification (X Han); Transcriptional Profiling of Definitive Endoderm Derived from Human Embryonic Stem Cells (H Liu et al.); A Markov Model Based Analysis of Stochastic Biochemical Systems (P Ghosh et al.); Clustering of Main Orthologs for Multiple Genomes (Z Fu & T Jiang); Extraction, Quantification and Visualization of Protein Pockets (X Zhang & C Bajaj); Consensus Contact Prediction by Linear Programming (X Gao et al.); An Active Visual Search Interface for Medline (W Xuan et al.); Exact and Heuristic Algorithms for Weighted Cluster Editing (S Rahmann et al.); Reconcilation with Non-binary Species Trees (B Vernot et al.); and other papers. Readership: Research and applicationSummary: community in bioinformatics, systems biology, medicine, pharmacology and biotechnology. Graduate researchers in bioinformatics and computational biology.
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Intro -- CONTENTS -- Preface -- Committees -- Referees -- Keynote Address -- Quantitative Aspects of Gene Regulation in Bacteria: Amplification. Threshold, and Combinatorial Control Terry Hwa -- Whole-Genome Analysis of Dorsal Gradient Thresholds in the Drosophila Embryo Julia ZeitlingeK Rob Zinzen, Dmitri Papatsenko et al. -- Invited Talks -- Learning Predictive Models of Gene Regulation Christina Leslie -- The Phylofacts Phylogenomic Encyclopedias: Structural Phylogenomic Analysis Across the Tree of Life Kimmen Golander -- Mapping and Analysis of the Human Interactome Network Kavitha Venkatesan -- 1. INTRODUCTION -- Gene-Centered Protein-DNA lnteractome Mapping A.J. Marian Walhout -- Proteomics -- Algorithm for Peptide Sequencing by Tandem Mass Spectrometry Based on Better Preprocessing and Anti-S ymmetric Computational Model Kang Ning and Hon Wai Leong -- 1. INTRODUCTION -- Preprocessing to remove noisy peaks -- The anti-symmetric problem -- 2. ANALYSIS OF PROBLEMS AND CURRENT ALGORITHMS -- 2.1. General Terminologies -- 2.2. Datasets -- 2.3. Problems Analysis -- 3. NEW COMPUTATIONAL MODELS AND ALGORITHM -- 3.1. Preprocessing to remove noisy peaks and introduce pseudo peaks -- 3.2. The Anti-symmetric Problem -- 3.3. Novel Peptide Sequencing Algorithm -- 4. EXPERIMENTS -- 4.1. Experiment Settings -- 4.2. Results -- 5. CONCLUSIONS -- References -- Algorithms for Selecting Breakpoint Locations to Optimize Diversity in Protein Engineering by Site-Directed Protein Recombination Wei Zheng, Xiaoduan Ye, Alan A4 Friedman and Chris Bailey-Kellogg -- 1. INTRODUCTION -- 2. METHODS -- 2.1. Library Diversity -- 2.2. Metrics for Breakpoint Selection -- 2.3. Dynamic Programming for Breakpoint Selection -- 3. RESULTS A N D DISCUSSION -- 4. CONCLUSION -- ACKNOWLEDGMENTS -- References.

An Algorithmic Approach to Automated High-Throughput Identification of Disulfide Connectivity in Proteins Using Tandem Mass Spectrometry Timothy Lee, Rahul Singh, Ten-Yang Yen and Bruce Macher -- 1. INTRODUCTION -- 1.1. Comparison of the Proposed Approach with Related Works -- 2. THE PROPOSED METHOD -- 2.1. Problem Formulation -- 2.2. Algorithmic Framework -- 2.2.1. Finding the MS spectrum match -- 2.2.2. Finding the MS/MS spectrum match -- 2.2.3. Finding a perfect matching of maximum weight for a fully connected graph -- 2.2.4. Consideration of missed proteolytic cleavages and intra-molecular bonded cysteines -- 2.2.5. Peak finding in the presence of noise -- 2.2.6. Addressing isotopic variation and neutral loss -- 2.2.7. Interpretation of peaks given charge state uncertainty -- 2.2.8. Overall complexity -- 3. EXPERIMENTAL RESULTS -- 3.1. Description of the Data and Experimental Procedures -- 3.2. Summary of Results -- 3.2.1. Analysis of the effect of varying threshold t on results -- 3.2.2. Comparison with MS2Assign program -- 4. CONCLUSIONS AND DISCUSSION -- Acknowledgments -- References -- Biomedical Application -- Cancer Molecular Pattern Discovery by Subspace Consensus Kernel Classification Xiaoxu Hun -- 1. INTRODUCTION -- 1 .1. Nonnegative matrix factorization -- 1.2. Contributions -- 2. INPUT SPACE, SUBSPACE AND KERNEL CLUSTERING -- 2.1. Subspace clustering -- 2.2. Kernel space clustering: conduct clustering in a high dimension space with kernel tricks -- 2.3. What's the ideal unsupervised classification algorithm for the high dimensional gene/protein expression data? -- 3. PG-NMF SUBSPACE KERNEL HIERARCHICAL CLASS1 FlCATlON -- 4. EXPERIMENTS -- 4.1. Comparing classification results from kNN, sparse-NMF and support vector machines (SVM) -- 5. CONCLUSIONS -- Acknowledgments -- References.

Efficient Algorithms for Genome-Wide tagSNP Selection Across Populations via the Linkage Disequilibrium Criterion Lan Liu, Yonghui Wu, Stefano Lonardi and Tao Jiang -- 1. INTRODUCTION -- 2. FORMULATION OF THE MCTS PROBLEM -- 3. OPTIMIZATION TECHNIQUES TO SOLVE THE MCTS PROBLEM -- 3.1. Data Reduction Rules -- 3.2. A Greedy Algorithm for MCTS -- 3.3. A Lagrangian Relaxation Algorithm for MCTS -- 4. EXPERIMENTAL RESULTS -- 4.1. Tagging the ENCODE Regions -- 4.2. Genome-wide Tagging -- 5. CONCLUSION -- ACKNOWLEDGEMENT -- References -- Transcriptional Profiling of Definitive Endoderm Derived from Human Embryonic Stem Cells Huiqing Liu, Stephen Dalton and Xng Xu -- 1. INTRODUCTION -- 2. DATA -- 3. METHODS AND PRELIMINARY RESULTS -- 3.1. Markers of hESC-DE -- 3.2. Transcription factor identification -- 3.3. Transcriptional regulation in the different phases of DE formation -- 4. ON-GOING WORK AND CONCLUSION -- Acknowledgment -- References -- Pathways, Networks and Systems Biology -- Bayesian Integration of Biological Prior Knowledge into the Reconstruction of Gene Regulatory Networks with Bayesian Networks Dirk Husmeier and Adriano I.: Werhli -- 1. INTRODUCTION -- 2. METHODOLOGY -- 2.1. Biological prior knowledge -- 2.2. MCMC sampling scheme -- 3. DATA -- 3.1. Cytometry data -- 3.2. Synthetic data -- 3.3. Biological prior knowledge -- 4. SIMULATIONS -- 4.1. Motivation -- 4.2. Reconstructing the regulatory network -- 5. RESULTS AND DISCUSSION -- 6. CONCLUSION -- ACKNOWLEDGEMENT -- References -- Using Indirect Protein-Protein Interactions for Protein Complex Predication Hon Nian Chua, Kang Ning, Wing-Kin Sung et al. -- 1 INTRODUCTION -- 2 INTRODUCTION OF INDIRECT NEIGHBORS -- 3 PCP ALGORITHM -- 4 EXPERIMENTS -- 5 DISCUSSIONS AND CONCLUSIONS -- Acknowledgements -- References.

Finding Linear Motif Pairs from Protein Interaction Networks: A Probabilistic Approach Henry C.M. Leung, MH. Siu, S.M. Yiu et al. -- 1. INTRODUCTION -- 2. PROBLEM DEFINITION -- 2.1. Motif Representation -- 2.2. (/, @-Motif Pair Finding Problem -- 2.2.1. x-score -- 2.2.2. p-score -- 3. ALGORITHM -- 3.1. Exact Algorithm -- 3.2. Heuristics Algorithm -- 4. EXPERIMENTS -- 4.1. SH3 Domains Dataset -- 4.2. Yeast Dataset -- 4.3. Running Time Comparison -- 4.4. Simulated Data -- 5. CONCLUSION -- Acknowledgments -- References -- A Markov Model Based Analysis of Stochastic Biochemical Systems Preetam Ghosh, Samik Ghosh, Kalyan Basu and Sajial K. Das -- 1. INTRODUCTION -- 2. STOCHASTIC BIOCHEMICAL SYSTEM ANALYSIS -- 3. OUR MARKOV CHAIN FORMULATION -- 3.1. The MFPT concept -- 3.2. Computing the state transition probabilities and times -- 3.2.1. Monomolecular reactions -- 3.2.2. Bimolecular reactions -- 3.2.3. Reversible Reactions -- 3.3. Pruning the Markov Chain -- 3.4. Computing the total probability of reaching a final state -- 3.5. Computing the MFPT for reaching the final state -- 3.6. Approximating the Markov Chain: Reducing complexity at the cost of accuracy -- 4. RESULTS AND ANALYSIS -- 4.1. Enzyme-Kinetics system -- 4.2. Transcriptional Regulatory System -- 5 . DISCUSSION -- 6. CONCLUSION AND FUTURE DIRECTIONS -- References -- An Information Theoretic Method for Reconstructing Local Regulatory Network Modules from Polymorphic Samples Manjunatha Jagalur and David Kulp -- 1. INTRODUCTION -- 1.1. Markov Blanket -- 1.1.1. Incremental Association Markov Blanket -- 1.2. Bayesian Networks -- 1.3. QTG Model -- 2. METHODS -- 2.1. Mixed Type Bayesian Network Under Biological Constraints -- 2.2. Markov Blanket Inference -- 2.3. Gene regulatory network reconstruction -- 3. EXPERIMENTS AND RESULTS -- 3.1. Simulations -- 3.2. Biological Significance -- 4. DISCUSSION.

ACKNOWLEDGEMENT -- References -- Using Directed Information to Build Biologically Relevant Influence Arvind Rao, Alfred 0. Hero III, David J. States and James Douglas Engel -- 1. INTRODUCTION -- 2. GENE NETWORKS -- 3. PROBLEM SETUP -- 3.1. Phylogenetic Conservation of Binding Sites -- 4. DTI FORMULATION -- 5 . A NORMALIZED DTI MEASURE -- 6. KERNEL DENSITY ESTIMATION (KDE) -- 7. BOOTSTRAPPED CONFIDENCE I N T E RVA LS -- 8. SUMMARY OF ALGORITHM -- 9. RESULTS -- 9.1. Synthetic Network -- 9.2. Directed Network inference: Gata3 Regulation in Early Kidney Development -- 9.3. Directed Network Inference: T-cell Activation -- 9.4. Phylogenetic conservation of TFBS effectors -- CONCLUSIONS -- ACKNOWLEDGEMENTS -- References -- Discovering Protein Complexes in Dense Reliable Neighborhoods of Protein Interaction Networks Xiao-Li Li, Chuan-Sheng Foo and See-Kiong Ng -- 1. INTRODUCTION -- 2. RELATED WORKS -- 3. THE PROPOSED TECHNIQUES -- 3.1. Mining for dense subgraphs -- 3.1.1. Mining for local dense subgraphs -- 3.1.2. Merging for maximal dense neighborhoods -- 3.2. Filtering for reliable subgraphs -- 3.2.1. Computing reliability of protein interactions -- 3.3. The overall DECAFF algorithm -- 4. EXPERIMENTS -- 4.1. Reference complexes and evaluation metric -- 4.2. Comparative results -- 4.3. Effect of the hub removal routine -- 4.4. Effect of parameters w and y -- 4.5. Analysis of the predicted complexes -- 5. Conclusions -- Acknowledgments -- References -- Mining Molecular Contexts of Cancer via In-Silico Conditioning Seungchan Kim, Ina Sen and Micheal Bittner -- 1. INTRODUCTION -- 2. METHODS -- 2.1. Identification of cellular contexts -- 2.2. Consistency statistics : interference and crosstalk -- 2.3. Interrogating contexts via in-silico conditioning -- 2.4. Significance test for identified contexts -- 2.5. Data quantization -- 2.6. Data quantization.

3. EVALUATION OF THE ALGORITHM.

This volume contains about 40 papers covering many of the latest developments in the fast-growing field of bioinformatics. The contributions span a wide range of topics, including computational genomics and genetics, protein function and computational proteomics, the transcriptome, structural bioinformatics, microarray data analysis, motif identification, biological pathways and systems, and biomedical applications. Abstracts from the keynote addresses and invited talks are also included. The papers not only cover theoretical aspects of bioinformatics but also delve into the application of new methods, with input from computation, engineering and biology disciplines. This multidisciplinary approach to bioinformatics gives these proceedings a unique viewpoint of the field. Sample Chapter(s). Chapter 1: Whole-Genome Analysis of Dorsal Gradient Thresholds in the Drosophila Embryo (102 KB). Contents: Learning Predictive Models of Gene Regulation (C Leslie); Algorithms for Selecting Breakpoint Locations to Optimize Diversity in Protein Engineering by Site-Directed Protein Recombination (W Zheng et al.); Cancer Molecular Pattern Discovery by Subspace Consensus Kernel Classification (X Han); Transcriptional Profiling of Definitive Endoderm Derived from Human Embryonic Stem Cells (H Liu et al.); A Markov Model Based Analysis of Stochastic Biochemical Systems (P Ghosh et al.); Clustering of Main Orthologs for Multiple Genomes (Z Fu & T Jiang); Extraction, Quantification and Visualization of Protein Pockets (X Zhang & C Bajaj); Consensus Contact Prediction by Linear Programming (X Gao et al.); An Active Visual Search Interface for Medline (W Xuan et al.); Exact and Heuristic Algorithms for Weighted Cluster Editing (S Rahmann et al.); Reconcilation with Non-binary Species Trees (B Vernot et al.); and other papers. Readership: Research and application

community in bioinformatics, systems biology, medicine, pharmacology and biotechnology. Graduate researchers in bioinformatics and computational biology.

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Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2018. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.

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