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Biocomputing 2011 [[electronic resource] ] : Proceedings of the Pacific Symposium
| Biocomputing 2011 [[electronic resource] ] : Proceedings of the Pacific Symposium |
| Autore | Altman Russ |
| Pubbl/distr/stampa | Singapore, : World Scientific Publishing Company, 2010 |
| Descrizione fisica | 1 online resource (500 p.) |
| Disciplina | 574.0151 |
| Altri autori (Persone) |
DunkerA. Keith <1943-> (Alan Keith)
HunterLawrence <1961-> |
| Soggetto topico |
Biology -- Computer simulation -- Congresses
Biology -- Mathematical models -- Congresses Molecular biology -- Computer simulation -- Congresses Molecular biology -- Mathematical models -- Congresses |
| Soggetto non controllato |
Protein Interactions
Metabolomics Biocomputing Computational Genetics Ontology Computational Proteomics Bioinformatics |
| ISBN |
1-283-14526-X
9786613145260 981-4335-05-3 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
PREFACE; CONTENTS; INTEGRATIVE -OMICS FOR TRANSLATIONAL SCIENCE; TOWARDS INTEGRATIVE GENE PRIORITIZATION IN ALZHEIMER'S DISEASE; SYSTEMS BIOLOGY ANALYSES OF GENE EXPRESSION AND GENOME WIDEASSOCIATION STUDY DATA IN OBSTRUCTIVE SLEEP APNEA; FINDING MOST LIKELY HAPLOTYPES IN GENERAL PEDIGREESTHROUGH PARALLEL SEARCH WITH DYNAMIC LOAD BALANCING; DYNAMIC, MULTI-LEVEL NETWORK MODELS OF CLINICAL TRIALS; MINING FUNCTIONALLY RELEVANT GENE SETS FOR ANALYZINGPHYSIOLOGICALLY NOVEL CLINICAL EXPRESSION DATA; GENOTYPE PHENOTYPE MAPPING IN RNA VIRUSES - DISJUNCTIVENORMAL FORM LEARNING
GENOME-WIDE ASSOCIATION MAPPING AND RARE ALLELES: FROMPOPULATION GENOMICS TO PERSONALIZED MEDICINEAN APPLICATION AND EMPIRICAL COMPARISON OF STATISTICAL ANALYSISMETHODS FOR ASSOCIATING RARE VARIANTS TO A COMPLEX PHENOTYPE; HAPLOTYPE PHASING BY MULTI-ASSEMBLY OF SHAREDHAPLOTYPES: PHASE-DEPENDENT INTERACTIONS BETWEEN RARE VARIANTS; AN EVALUATION OF POWER TO DETECT LOW-FREQUENCY VARIANTASSOCIATIONS USING ALLELE-MATCHING TESTS THAT ACCOUNTFOR UNCERTAINTY; PENALIZED REGRESSION FOR GENOME-WIDE ASSOCIATIONSCREENING OF SEQUENCE DATA; MICROBIOME STUDIES: PSB 2011 SPECIAL SESSION INTRODUCTION ESTIMATING THE NUMBER OF SPECIES WITH CATCHALLA FRAMEWORK FOR ANALYSIS OF METAGENOMIC SEQUENCING DATA; VISUALIZATION AND STATISTICAL COMPARISONS OF MICROBIALCOMMUNITIES USING R PACKAGES ON PHYLOCHIP DATA; HUMAN MICROBIOME VISUALIZATION USING 3D TECHNOLOGY; COMPARING BACTERIAL COMMUNITIES INFERRED FROM 16S rRNA GENE SEQUENCING AND SHOTGUN METAGENOMICS; MULTI-SCALE MODELLING OF BIOSYSTEMS: FROM MOLECULAR TOMESOCALE; COMPUTATIONAL GENERATION INHIBITOR-BOUND CONFORMERS OF P38 MAPKINASE AND COMPARISON WITH EXPERIMENTS MOLECULAR DYNAMICS SIMULATIONS OF THE FULL TRIPLE HELICALREGION OF COLLAGEN TYPE I PROVIDE AN ATOMIC SCALE VIEW OF THEPROTEIN'S REGIONAL HETEROGENEITYSTRUCTURAL INSIGHTS INTO PRE-TRANSLOCATION RIBOSOME MOTIONS; NEW CONFORMATIONAL SEARCH METHOD USING GENETICALGORITHM AND KNOT THEORY FOR PROTEINS; PERSONAL GENOMICS; THE REFERENCE HUMAN GENOME DEMONSTRATES HIGH RISK OF TYPE 1DIABETES AND OTHER DISORDERS; MATCHING CANCER GENOMES TO ESTABLISHED CELL LINESFOR PERSONALIZED ONCOLOGY USE OF BIOLOGICAL KNOWLEDGE TO INFORM THE ANALYSIS OF GENE-GENEINTERACTIONS INVOLVED IN MODULATING VIROLOGIC FAILURE WITHEFAVIRENZ-CONTAINING TREATMENT REGIMENS IN ART-NAÏVE ACTG CLINICALTRIALS PARTICVISUAL INTEGRATION OF RESULTS FROM A LARGE DNA BIOBANK (BIOVU)USING SYNTHESIS-VIEW; MULTIVARIATE ANALYSIS OF REGULATORY SNPS: EMPOWERING PERSONALGENOMICS BY CONSIDERING CIS-EPISTASIS AND HETEROGENEITY; HAPLOTYPE INFERENCE FROM SHORT SEQUENCE READS USING APOPULATION GENEALOGICAL HISTORY MODEL; REVERSE ENGINEERING AND SYNTHESIS OF BIOMOLECULAR SYSTEMS; BINARY COUNTING WITH CHEMICAL REACTIONS DEFINING THE PLAYERS IN HIGHER-ORDER NETWORKS: PREDICTIVE MODELINGFOR REVERSE ENGINEERING FUNCTIONAL INFLUENCE NETWORKS |
| Record Nr. | UNINA-9910346695803321 |
Altman Russ
|
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| Singapore, : World Scientific Publishing Company, 2010 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Biocomputing 2013 - Proceedings of the Pacific Symposium [[electronic resource]]
| Biocomputing 2013 - Proceedings of the Pacific Symposium [[electronic resource]] |
| Autore | Altman Russ |
| Pubbl/distr/stampa | Singapore, : World Scientific Publishing Company, 2012 |
| Descrizione fisica | 1 online resource (471 p.) |
| Disciplina | 574.310724 |
| Soggetto topico |
Biology -- Computer simulation -- Congresses
Biology -- Mathematical models -- Congresses Biology -- Mathematical models Biology Health & Biological Sciences Biology - General |
| ISBN | 981-4447-97-8 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Modeling cell heterogeneity: from single-cell variations to mixed cells populations445; Computational Challenges of Mass Phenotyping454; The Future of Genome-Based Medicine456; 0session-intro-cdr.pdf; 1cheng; 1. Introduction; 2. Methods; 2.1. Data sources and data processing; 2.2. Pair-wise similarity scores; 2.3. Method nomenclature; 2.4. AUCs and p-values; 2.5. Expression signal strength; 3. Results; 4. Discussion; 5. Acknowledgments; 2felciano; 3phatak; 4shi; 5wang; 0intro-epigenomics.pdf; 1ahn; 2luo; 3sahu; 1gabr; 2gevaert; 3kim; 1. Introduction; 2. Methods
2.1. Introduction of the Module Cover Problem2.2. Integrated Module Cover; 2.3. Two-Step Module Cover; 3. Results; 3.1. Analysis of Glioblastoma Multiforme Data from GMDI; 3.1.1. Comparison of the Module Cover approaches. For an association to be specific in a given module, only a few regulatory associations should have highly significant p-values while the remaining loci are expected to have insignificant p-values. Thus, we defined the specificity of a module M as the area of a cumulative histogram of association significance values. Specifically, we partitioned the range from 0 to strength (M) into 10 bins of equal sizes and defined cj to be the cumulative percentage of j-th bin. Then the specificity is defi...3.1.2. Analysis of GBM data; 3.1.3. Analysis of Ovarian Cancer Data; 4. Discussion Uncovering modules that are associated with genomic alterations in a disease is a challenging task as well as an important step to understand complex diseases. To address this challenge we introduced a novel technique - module cover - that extends the concept of set cover to network modules. We provided a mathematical formalization of the problem and developed two heuristic solutions: the Integrated Module Cover approach, which greedily selects genes to cover disease cases while simultaneousl... In general, the module cover approach is especially helpful in analyzing and classifying heterogeneous disease cases by exploring the way different combinations of dys-regulated of modules relate to a particular disease subcategory. Indeed, our analysis indicated that the gene set selected by module cover approach may be used for classification. Equally important, the selected module covers may help to interpret classifications that were obtained with other methods.5. Materials; 5.1 Data Treatment for Glioblastoma Multiforme Data from GMDI Differentially Expressed Genes: Briefly, all samples were profiled using HG-U133 Plus 2.0 arrays that were normalized at the probe level with dChip (16, 19). Among probes representing each gene, we chose the probeset with the highest mean intensity in the tumor and control samples. We determined genes that are differentially expressed in each disease case compared to the non-tumor control cases with a Z-test. For a gene g and case c, we define cover(c, g) to be 1 if nominal p-value < 0.01 and... |
| Record Nr. | UNINA-9910346695703321 |
|
Altman Russ
|
||
| Singapore, : World Scientific Publishing Company, 2012 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||