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Bioactive Compounds from Microbes
| Bioactive Compounds from Microbes |
| Autore | Katharina Riedel |
| Pubbl/distr/stampa | Frontiers Media SA, 2017 |
| Descrizione fisica | 1 online resource (142 p.) |
| Collana | Frontiers Research Topics |
| Soggetto topico | Microbiology (non-medical) |
| Soggetto non controllato |
antibiotics
antitumor activity food-encrypted peptides Gut Microbiota gut-brain axis human-microbes cross talk immune-system modulation Metabolomics Metagenomics |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910220054303321 |
Katharina Riedel
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| Frontiers Media SA, 2017 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Biocomputing 2011 : Proceedings of the Pacific Symposium
| Biocomputing 2011 : 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 |
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Altman Russ
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| Singapore, : World Scientific Publishing Company, 2010 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Biocomputing 2013 - Proceedings of the Pacific Symposium
| Biocomputing 2013 - Proceedings of the Pacific Symposium |
| 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 |
| Soggetto non controllato |
Protein Interactions
Metabolomics Biocomputing Computational Genetics Ontology Computational Proteomics Bioinformatics |
| 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 |
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Altman Russ
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| Singapore, : World Scientific Publishing Company, 2012 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Biocomputing 2018 - Proceedings Of The Pacific Symposium
| Biocomputing 2018 - Proceedings Of The Pacific Symposium |
| Autore | Altman Russ B |
| Pubbl/distr/stampa | World Scientific Publishing Co, 2017 |
| Descrizione fisica | 1 online resource (649 pages) |
| Altri autori (Persone) |
DunkerA. Keith <1943-> (Alan Keith)
HunterLawrence <1961-> RitchieMarylyn D MurrayTiffany A KleinTeri E |
| Soggetto non controllato |
Protein Interactions
Metabolomics Biocomputing Computational Genetics Ontology Computational Proteomics Bioinformatics |
| ISBN | 981-323-553-5 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Altri titoli varianti | Biocomputing 2018 |
| Record Nr. | UNINA-9910346695003321 |
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Altman Russ B
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| World Scientific Publishing Co, 2017 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Biocomputing 2019 - Proceedings Of The Pacific Symposium
| Biocomputing 2019 - Proceedings Of The Pacific Symposium |
| Autore | Altman Russ B |
| Pubbl/distr/stampa | World Scientific Publishing Co, 2018 |
| Descrizione fisica | 1 online resource (471 pages) |
| Altri autori (Persone) |
DunkerA. Keith <1943-> (Alan Keith)
HunterLawrence <1961-> RitchieMarylyn D MurrayTiffany A KleinTeri E |
| Soggetto non controllato |
Protein Interactions
Metabolomics Biocomputing Computational Genetics Ontology Computational Proteomics Bioinformatics |
| ISBN |
981-327-982-6
981-327-981-8 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- PATTERN RECOGNITION IN BIOMEDICAL DATA: CHALLENGES IN PUTTING BIG DATA TO WORK -- Session introduction -- Introduction -- References -- Learning Contextual Hierarchical Structure of Medical Concepts with Poincairé Embeddings to Clarify Phenotypes -- 1. Introduction -- 2. Methods -- 2.1. Source Code -- 2.2. Data Source -- 2.3. Data Selection and Preprocessing -- 2.3.1. Reference ICD9 Example -- 2.3.2. Real Member Analyses -- 2.4. Poincaré Embeddings -- 2.5. Processing and Evaluating Embeddings -- 3. Results -- 3.1. ICD9 Hierarchy Evaluation -- 3.2. Poincaré Embeddings on 10 Million Members -- 3.3. Comparison with Euclidean Embeddings -- 3.4. Cohort Specific Embeddings -- 4. Discussion and Conclusion -- 5. Acknowledgments -- References -- The Effectiveness of Multitask Learning for Phenotyping with Electronic Health Records Data -- 1. Introduction -- 2. Background -- 2.1. Multitask nets -- 3. Methods -- 3.1. Dataset Construction and Design -- 3.2. Experimental Design -- 4. Experiments and Results -- 4.1. When Does Multitask Learning Improve Performance? -- 4.2. Relationship Between Performance and Number of Tasks -- 4.3. Comparison with Logistic Regression Baseline -- 4.4. Interaction between Phenotype Prevalence and Complexity -- 5. Limitations -- 6. Conclusion -- Acknowledgments -- References -- ODAL: A one-shot distributed algorithm to perform logistic regressions on electronic health records data from multiple clinical sites -- 1. Introduction -- 1.1. Integrate evidence from multiple clinical sites -- 1.2. Distributed Computing -- 2. Material and Method -- 2.1. Clinical Cohort and Motivating Problem -- 2.2. Algorithm -- 2.3. Simulation Design -- 3. Results -- 3.1. Simulation Results -- 3.2. Fetal Loss Prediction via ODAL -- 4. Discussion -- References.
PVC Detection Using a Convolutional Autoencoder and Random Forest Classifier -- 1. Introduction -- 2. Methods -- 2.1. Data Set and Implementation -- 2.2. Proposed PVC Detection Method -- 2.2.1. Feature Extraction -- 2.2.2. Classification -- 3. Results -- 3.1. Full Database Evaluation -- 3.2. Timing Disturbance Evaluation -- 3.3. Cross-Patient Training Evaluation -- 3.4. Estimated Parameters and Convergence -- 4. Discussion -- References -- Removing Confounding Factors Associated Weights in Deep Neural Networks Improves the Prediction Accuracy for Healthcare Applications -- 1. Introduction -- 2. Related Work -- 3. Confounder Filtering (CF) Method -- 3.1. Overview -- 3.2. Method -- 3.3. Availability -- 4. Experiments -- 4.1. lung adenocarcinoma prediction -- 4.1.1. Data -- 4.1.2. Results -- 4.2. Segmentation on right ventricle(RV) of Heart -- 4.2.1. Data -- 4.2.2. Results -- 4.3. Students' confusion status prediction -- 4.3.1. Data -- 4.3.2. Results -- 4.4. Brain tumor prediction -- 4.4.1. Data -- 4.4.2. Results -- 4.5. Analyses of the method behaviors -- 5. Conclusion -- 6. Acknowledgement -- References -- DeepDom: Predicting protein domain boundary from sequence alone using stacked bidirectional LSTM -- 1. Introduction -- 2. METHODS -- 2.1 Data Set Preparation -- 2.2 Input Encoding -- 2.3 Model Architecture -- 2.4 Evaluation criteria -- 3. RESULTS AND DISCUSSION -- 3.1 Parameter configuration experiments on test data -- 3.2 Comparison with Other Domain Boundary Predictors -- 3.2.1 Free modeling targets from CASP 9 -- 3.2.2 Multi-domain targets from CASP 9 -- 3.2.3 Discontinuous domain target from CASP 8 -- 4. CONCLUSION -- 5. ACKNOWLEDGEMENTS -- REFERENCES -- Res2s2aM: Deep residual network-based model for identifying functional noncoding SNPs in trait-associated regions -- 1. Introduction -- 2. Background theory. 3. Dataset for training and testing -- 3.1. Source databases -- 3.2. Dataset generation -- 4. Methods -- 4.1. ResNet architecture in our model -- 4.2. Tandem inputs of forward- and reverse-strand sequences -- 4.3. Biallelic high-level network structure -- 4.4. Incorporating HaploReg SNP annotation features -- 4.5. Training of models -- 5. Results -- 6. Conclusions and discussion -- Acknowledgements -- References -- DNA Steganalysis Using Deep Recurrent Neural Networks -- 1. Introduction -- 2. Background -- 2.1. Notations -- 2.2. Hiding Messages -- 2.3. Determination of Message-Hiding Regions -- 3. Methods -- 3.1. Proposed DNA Steganalysis Principle -- 3.2. Proposed Steganalysis RNN Model -- 4. Results -- 4.1. Dataset -- 4.2. Input Representation -- 4.3. Model Training -- 4.4. Evaluation Procedure -- 4.5. Performance Comparison -- 5. Discussion -- Acknowledgments -- References -- Bi-directional Recurrent Neural Network Models for Geographic Location Extraction in Biomedical Literature -- 1. Introduction -- 2. Related Work -- 3. Methods -- 3.1. Toponym Detection -- 3.1.1. Recurrent Neural Networks -- 3.1.2. LSTM -- 3.1.3. Other Gated RNN Architectures -- 3.1.4. Hyperparameter search and optimization -- 3.2. Toponym Disambiguation -- 3.2.1. Building Geonames Index -- 3.2.2. Searching Geonames Index -- 4. Results and Discussion -- 4.1. Toponym Disambiguation -- 4.2. Toponym Resolution -- 5. Limitations and Future Work -- 6. Conclusion -- Acknowledgments -- Funding -- References -- Automatic Human-like Mining and Constructing Reliable Genetic Association Database with Deep Reinforcement Learning -- 1. Introduction -- 2. Related Work -- 3. Method -- 3.1. Model Framework -- 3.2. Deep Reinforcement Learning for Organizing Actions -- 3.3. Preprocessing and Name Entity Recognition with UMLS -- 3.4. Bidirectional LSTM for Relation Classification. 3.5. Algorithm -- 3.6. Implementation Specification -- 4. Experiments -- 4.1. Data -- 4.2. Evaluation -- 4.3. Results -- 4.3.1. Improved Reliability -- 4.3.2. Robustness in Real-world Situations -- 4.3.3. Number of Articles Read -- 5. Conclusions and Future Work -- 6. Acknowledgement -- References -- Estimating classification accuracy in positive-unlabeled learning: characterization and correction strategies -- 1. Introduction -- 2. Methods -- 2.1. Performance measures: definitions and estimation -- 2.2. Positive-unlabeled setting -- 2.3. Performance measure correction -- 3. Experiments and Results -- 3.1. A case study -- 3.2. Data sets -- 3.3. Experimental protocols -- 3.4. Results -- 4. Conclusions -- Acknowledgements -- References -- PLATYPUS: A Multiple-View Learning Predictive Framework for Cancer Drug Sensitivity Prediction -- 1. Introduction -- 2. System and methods -- 2.1. Data -- 2.2. Single views and co-training -- 2.3. Maximizing agreement across views through label assignment -- 3. Results -- 3.1. Preliminary experiments to optimize PLATYPUS performance -- 3.2. Predicting drug sensitivity in cell lines -- 3.3. Key features from PLATYPUS models -- 4. Conclusions -- Acknowledgments -- References -- Computational KIR copy number discovery reveals interaction between inhibitory receptor burden and survival -- 1. Introduction -- 2. Materials and Methods -- 2.1 Data collection -- 2.2 K-mer selection -- 2.3 NGS pipeline and k-mer extraction -- 2.4 Data cleaning -- 2.5 Normalization of k-mer frequencies -- 2.6 Copy number segregation and cutoff selection -- 2.7 Validation of copy number -- 2.8 Survival analysis -- 2.9 Additional immune analysis -- 3. Results and Discussions -- 3.1 Establishing unique k-mers -- 3.2 Varying coverage of KIR region by exome capture kit -- 3.3 Inference of KIR copy number -- 3.4 Population variation of the KIR region. 3.5 KIR inhibitory gene burden correlates with survival in cervical and uterine cancer -- 5. Conclusions -- 6. Acknowledgements -- 7. Supplementary Material -- References -- Exploring microRNA Regulation of Cancer with Context-Aware Deep Cancer Classifier -- 1. Introduction -- 2. Data -- 2.1. Preprocessing -- 3. Deep Cancer Classifier -- 3.1. Training & -- testing -- 3.2. Parameter tuning -- 3.3. Feature importance -- 4. Results and Discussion -- 4.1. Model selection -- 4.2. Classifier performance -- 4.3. Comparison with other methods -- 4.4. Feature importance -- 5. Conclusion -- References -- Implementing and Evaluating A Gaussian Mixture Framework for Identifying Gene Function from TnSeq Data -- 1. Introduction -- 1.1. TnSeq Motivation and Background -- 1.2. Motivation and New Methods -- 2. Methods -- 2.1. TnSeq Experimental Data -- 2.2. Mixture framework -- 2.3. Classification methods -- 2.3.1. Novel method - EM -- 2.3.2. Current method - t-statistic -- 2.3.3. Bayesian hierarchical model -- 2.3.4. Data partitioning for the Bayesian model -- 2.4. Simulation -- 2.5. Real data -- 3. Results -- 3.1.1. Classification rate -- 3.1.2. False positive rate -- 3.1.3. Positive classification rate -- 3.1.4. Cross entropy -- 3.2. Simulation Results -- 3.3. Comparisons on real data -- 3.4. Software -- 4. Discussion -- References -- SNPs2ChIP: Latent Factors of ChIP-seq to infer functions of non-coding SNPs -- 1. Introduction -- 2. Results -- 2.1. SNPs2ChIP analysis framework overview -- 2.2. Batch normalization of heterogeneous epigenetic features -- 2.3. Latent factor discovery and their biological characterization -- 2.4. SNPs2ChIP identifies relevant functions of the non-coding genome -- 2.4.1. Genome-wide SNPs coverage of the reference datasets -- 2.4.2. Non-coding GWAS SNPs of systemic lupus erythematosus -- 2.4.3. ChIP-seq peaks for vitamin D receptors. 2.5. Robustness Analysis in the latent factor identification. |
| Altri titoli varianti |
Biocomputing 2019
Biocomputing 2019:Proceedings of the Pacific Symposium:Pacific Symposium on Biocomputing 2019 |
| Record Nr. | UNINA-9910349464803321 |
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Altman Russ B
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| World Scientific Publishing Co, 2018 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Biocomputing 2021 - Proceedings Of The Pacific Symposium
| Biocomputing 2021 - Proceedings Of The Pacific Symposium |
| Autore | Altman Russ B |
| Pubbl/distr/stampa | World Scientific Publishing Co, 2020 |
| Descrizione fisica | 1 online resource (372 pages) |
| Altri autori (Persone) |
DunkerA. Keith <1943-> (Alan Keith)
HunterLawrence <1961-> RitchieMarylyn D MurrayTiffany A KleinTeri E |
| Soggetto non controllato |
Protein Interactions
Metabolomics Biocomputing Computational Genetics Ontology Computational Proteomics Bioinformatics |
| ISBN | 981-12-3270-9 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Contents -- Preface -- ACHIEVING TRUSTWORTHY BIOMEDICAL DATA -- Session Introduction: Achieving Trustworthy Biomedical Data Solutions -- 1. Introduction -- 2. Preserving Privacy and Explaining Decisions of Artificial Intelligence -- 3. Sharing Genomic and Health Records -- 4. Deploying Digital Health Solutions -- 5. Crowdsourcing Healthcare -- 6. Considering the Bioethics -- 7. Anticipating the Future -- References -- Selection of Trustworthy Crowd Workers for Telemedical Diagnosis of Pediatric Autism Spectrum Disorder -- 1. Introduction -- 2. Methods -- 2.1. Clinically representative videos -- 2.2. Crowdsourcing task for Microworkers -- 2.3. Classifier to evaluate performance -- 2.4. Metrics evaluated -- 2.5. Prediction of crowd worker performance from metrics -- 3. Results -- 3.1. Correlation between metrics and probability of the correct class -- 3.2. Regression prediction of the mean probability of the correct class -- 4. Discussion and Future Work -- 5. Conclusion -- 6. Acknowledgments -- References -- Differential Privacy Protection Against Membership Inference Attack on Machine Learning for Genomic Data -- 1. Introduction -- 2. Related Work -- 3. Methods -- 3.1. Membership inference attack (MIA) -- 3.2. Di erential privacy (DP) -- 4. Experimental Setup -- 4.1. Dataset -- 4.2. Implementation of target models -- 4.3. Implementation of DP -- 4.4. Implementation of MIA -- 4.5. Evaluation metrics -- 5. Results -- 5.1. Vulnerability of target model against MIA without DP protection -- 5.2. Impact of privacy budget on the target model accuracy -- 5.3. E ectiveness of DP against MIA -- 5.4. E ect of model sparsity -- 6. Conclusion -- References -- Making Compassionate Use More Useful: Using Real-World Data, Real-World Evidence and Digital Twins to Supplement or Supplant Randomized Controlled Trials -- 1. Introduction.
1.1 Compassionate use -- 1.2 Compassionate use during the pandemic -- 1.3 What is an RCT? -- 1.3 EA data and NDAs -- 2. Real-World Information -- 2.1 Real-world data in trials -- 2.2 Real-world data and real-world evidence -- 2.2 Real-world limitations -- 3.0 Making RWD Work -- 3.1 Digital twins -- 4.0 Conclusions -- References -- ADVANCED METHODS FOR BIG DATA ANALYTICS IN WOMEN'S HEALTH -- Session Introduction: Advanced Methods for Big Data Analytics in Women's Health -- 1. Introduction -- 2. Session Summary -- 2.1. Full-length papers -- 3. Discussion -- References -- Intimate Partner Violence and Injury Prediction from Radiology Reports -- 1. Introduction -- 2. Related Work -- 2.1. Intimate partner violence -- 2.2. Clinical prediction -- 2.3. Natural language processing -- 3. Dataset -- 3.1. IPV patient selection -- 3.2. Control group selection -- 3.3. Injury labels -- 3.4. Data cleaning -- 3.5. Demographic data -- 4. Methodology -- 4.1. Experiment setup -- 4.2. Models -- 4.3. Evaluation -- 4.3.1. Prediction and predictive features -- 4.3.2. Error analysis -- 4.3.3. Report-program date gap -- 5. Results -- 5.1. IPV and injury prediction and predictive features -- 5.2. Error analysis -- 5.3. Report-program date gap -- 6. Discussion and conclusion -- References -- Not All C-sections Are the Same: Investigating Emergency vs. Elective C-section deliveries as an Adverse Pregnancy Outcome -- 1. Background and Significance -- 2. Methods -- 2.1. Dataset characteristics -- 2.2. Identification of delivery outcomes -- 2.2.1. Cesarean section deliveries -- 2.2.2. Preterm birth, stillbirth, and multiple birth deliveries -- 2.3. Integration of data from encounter records -- 2.4. Generalized regression models -- 3. Results -- 3.1. Utilization of cesarean section codes -- 3.2. Admission types recorded in encounter records. 3.3. Age distribution by delivery admit type -- 3.4. Number of deliveries by weekday and admit type -- 4. Generalized regression model -- 4.1. Surgical Incision Type for C-section and Effect on Emergency Admission -- 5. Discussion -- References -- Co-occurrence Patterns of Intimate Partner Violence -- 1. Introduction -- 2. Materials and Methods -- 2.1. Description of Data and Pre-Processing -- 2.2. Co-Occurrence of Violence Types -- 2.3. Co-Occurrence Network of Individual Violence Items -- 2.4. Radial Visualization -- 2.5. Clustering of Survivors and Identification of Subgroups -- 2.6. Health Problems and Trauma Symptoms -- 3. Results -- 4. Discussion -- 5. Acknowledgments -- References -- BIOCOMPUTING AND AI FOR INFECTIOUS DISEASE MODELLING AND THERAPEUTICS -- Session Introduction: AI for Infectious Disease Modelling and Therapeutics -- 1. Background -- 2. Introduction -- 3. Social Media and COVID-19 -- 4. Biomedical literature and COVID-19 plus neglected tropical diseases -- 5. Genomics and HCV -- 6. Protein intrinsically disordered regions and SARS-CoV-2 -- 7. Protein-protein interactions and SARS-CoV-2 -- References -- Characterization of Anonymous Physician Perspectives on COVID-19 Using Social Media Data -- 1. Introduction -- 2. Methods -- 2.1. Data Collection -- 2.2. N-gram Frequency Measures -- 2.3. Sentiment Analysis -- 3. Results -- 3.1. Frequency of terms and n-grams -- 3.2. Sentiment analysis -- 3.3. Sentiments of tweets containing specific terms -- 4. Discussion and Conclusion -- 5. Acknowledgments -- References -- Semantic Changepoint Detection for Finding Potentially Novel Research Publications -- 1. Introduction -- 2. Methods -- 2.1. Data collection and general procedures -- 2.2. Title and abstract entropies -- 2.3. Bayesian changepoint analysis -- 2.4. Differential word clouds -- 2.5. Title and abstract embeddings. 2.6. Semantic novelty -- 2.6.1. Strategy T1: Novel paper detection based on semantic distance -- 2.6.2. Strategy T2: Detection of novel papers that may constitute a trend -- 2.6.3. Strategy Y1: Detection of a group of novel papers based on their mean vector -- 2.6.4. Strategy Y2: Proportion of novel papers -- 3. Results and Discussion -- 4. Conclusions -- 5. Supplementary Information -- 6. Acknowledgements -- References -- TreeFix-TP: Phylogenetic Error-Correction for Infectious Disease Transmission Network Inference -- 1. Background -- 2. Methods -- 2.1. Minimizing inter-host transmissions -- 2.2. Description of TreeFix-TP -- 2.3. Evaluation using simulated data sets -- 2.3.1. Data set generation -- 2.3.2. Evaluating reconstruction accuracy -- 3. Results -- 3.1. Phylogenetic error correction results -- 3.2. Source recovery in HCV outbreaks -- 3.3. Running time and scalability -- 4. Discussion and Conclusions -- Acknowledgments -- Authors' Contributions -- Supplementary Material -- References -- SARS-CoV-2 Drug Discovery based on Intrinsically Disordered Regions -- 1. Introduction -- 2. Methods -- 2.1. Molecular docking -- 2.1.1. Data collection -- 2.1.2. Data preprocessing -- 2.1.3. Target file generation -- 2.1.4. Flexible docking -- 2.1.5. Ensemble docking -- 2.2. Statistical model -- 2.2.1. Chemprop -- 2.2.2. Data and training -- 3. Results -- 3.1. Interaction modelling -- 3.2. Activity prediction -- 4. Conclusion -- 5. Acknowledgements -- References -- Feasibility of the Vaccine Development for SARS-CoV-2 and Other Viruses Using the Shell Disorder Analysis -- 1. Introduction -- 1.1. SARS-COV-2 Vaccine -- 1.2. Shell disorder analysis of HIV and other viruses -- 1.3. Spinoff projects including coronaviruses: Shell disorder and modes of transmission -- 1.4. Yet another spinoff: Correlations between the inner shell disorder and virulence. 2. Results -- 2.1. Clustering of CoV based mainly on NPID -- 2.2 Outer shell disorder is an indicator for the presence or absence of effective vaccines -- 2.3. A disordered outer shell provides an immune evasion tactic: Viral shapeshifting -- 2.4. SARS-CoV-2: Exceptionally hard shell (low MPID) associated with burrowing animals and buried feces -- 2.5. Behavior of the animal hosts matters in the evolutions of the viruses: EIAV vs. HIV -- 2.6. Feasibility of developing attenuated vaccine strains for SARS-CoV-2 -- 3. Discussion -- 3.1. Links between respiratory transmission, N (Inner shell) disorder, and virulence: Viral load in body fluids vs. vital organs -- 3.2. Greater disorder in the inner shell proteins provide means for the more efficient replication of viral particles -- 3.3 Two modes of immune evasion: "Trojan Horse" (inner shell disorder) and "viral shapeshifting" (outer shell disorder) -- 3.4. FIV, HIV-1 and HIV-2: Similarities and differences -- 3.5. FIV vaccine enigma: Questionable efficacy -- 4. Conclusions -- 4.1. Development of the SARS-CoV-2 vaccine is feasible and vaccine strains can be found in nature -- 5. Materials and Methods -- References -- Protein Sequence Models for Prediction and Comparative Analysis of the SARS-CoV-2−Human Interactome -- 1. Introduction -- 2. Methods -- 2.1. Generalized Additive Models with interactions (GA2M) -- 3. Gold Standard Interaction Datasets -- 3.1. Dealing with the lack of negative examples -- 3.2. Features -- 4. Experiments -- 4.1. TAPE: Transformer based model for protein sequences -- 5. Results -- 5.1. Prediction performance and validation of predicted interactions -- 5.2. Enrichment analysis of predicted human binding partners -- 6. Discussion -- 6.1. Visualizing the virus-human interactions -- 6.2. Highly ranked sequence features -- 6.3. Structural analysis -- 7. Prior Work -- 8. Conclusion. 9. Acknowledgements. |
| Altri titoli varianti | Biocomputing 2021 |
| Record Nr. | UNINA-9910433229003321 |
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Altman Russ B
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| World Scientific Publishing Co, 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Fruit Ripening: From Present Knowledge to Future Development
| Fruit Ripening: From Present Knowledge to Future Development |
| Autore | Palma José M |
| Pubbl/distr/stampa | Frontiers Media SA, 2019 |
| Descrizione fisica | 1 online resource (185 p.) |
| Soggetto topico |
Botany and plant sciences
Science: general issues |
| Soggetto non controllato |
Antioxidants
Fleshy fruits fruit quality gene editing Metabolomics Metagenomics microRNA phytohormones signaling |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Altri titoli varianti | Fruit Ripening |
| Record Nr. | UNINA-9910557446903321 |
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Palma José M
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| Frontiers Media SA, 2019 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Integration of OMICS Data to Understand Plant Metabolism
| Integration of OMICS Data to Understand Plant Metabolism |
| Autore | Labate Carlos Alberto |
| Pubbl/distr/stampa | Frontiers Media SA, 2019 |
| Descrizione fisica | 1 online resource (153 p.) |
| Soggetto topico |
Botany & plant sciences
Science: general issues |
| Soggetto non controllato |
Metabolomics
omnics data integration Plant metabolism Proteomics |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910557770803321 |
|
Labate Carlos Alberto
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| Frontiers Media SA, 2019 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Metabolism and Metabolomics of Liver in Health and Disease
| Metabolism and Metabolomics of Liver in Health and Disease |
| Autore | Wahli Walter |
| Pubbl/distr/stampa | Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2021 |
| Descrizione fisica | 1 online resource (268 p.) |
| Soggetto topico | Medicine and Nursing |
| Soggetto non controllato |
1H-NMR spectroscopy
acetaminophen acupuncture alcoholic liver disease arachidonic acid bile acids biomarker blood carbohydrate response element-binding protein cells cholestasis ChREBP cirrhosis de novo lipogenesis diabetes docosahexaenoic acid feces fibrosis Fibrosis GC-MS Genomics glucose homeostasis glucose production glycogen glycogen storage disease type I glycolysis heat stress HepaRG hepatotoxicity hexosamine imflammation in vitro inflammation lipid homeostasis lipid metabolism lipidomics liquid chromatography liquid chromatography-mass spectrometry liver Liver biopsy liver function mass spectrometry metabolic pathway metabolic profile metabolic stress metabolomics Metabolomics metabolomics quantitative profiling multivariate statistical analysis NAFL NAFLD NASH nicotinamide non-alcoholic fatty liver disease non-alcoholic steatohepatitis nonalcoholic fatty liver nonalcoholic fatty liver disease nonalcoholic steatohepatitis nuclear magnetic resonance spectroscopy oxidative stress pentose phosphate pathway premalignant primary mouse hepatocytes Proteomics rat plasma reference toxicants sodium saccharin standard operating procedures steatosis tandem mass spectrometry tissue transcription factors Transcriptomics urine |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910557148103321 |
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Wahli Walter
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| Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2021 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Metabolomics in Crop Research - Current and Emerging Methodologies
| Metabolomics in Crop Research - Current and Emerging Methodologies |
| Autore | Sousa Silva Marta |
| Pubbl/distr/stampa | Frontiers Media SA, 2019 |
| Descrizione fisica | 1 online resource (181 p.) |
| Soggetto topico |
Botany & plant sciences
Science: general issues |
| Soggetto non controllato |
Crop development
Mass Spectrometry metabolic profiling Metabolomics Nuclear Magnetic Resonance |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910557434603321 |
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Sousa Silva Marta
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| Frontiers Media SA, 2019 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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