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Biobanking of human biospecimens : lessons from 25 years of biobanking experience / / editors, Pierre Hainaut [and three others]
| Biobanking of human biospecimens : lessons from 25 years of biobanking experience / / editors, Pierre Hainaut [and three others] |
| Edizione | [Second edition.] |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2021] |
| Descrizione fisica | 1 online resource (210 pages) |
| Disciplina | 570.752 |
| Soggetto topico |
Biobanks
Ciències de la salut Biologia computacional |
| ISBN | 3-030-55901-7 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910495217303321 |
| Cham, Switzerland : , : Springer, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Bioinformatics and computational biology : a primer for biologists / / Basant K. Tiwary
| Bioinformatics and computational biology : a primer for biologists / / Basant K. Tiwary |
| Autore | Tiwary Basant K. |
| Pubbl/distr/stampa | Singapore : , : Springer, , [2022] |
| Descrizione fisica | 1 online resource (239 pages) |
| Disciplina | 570.285 |
| Soggetto topico |
Bioinformatics
Computational biology Bioinformàtica Biologia computacional |
| Soggetto genere / forma | Llibres electrònics |
| ISBN |
981-16-4240-0
981-16-4241-9 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910743247203321 |
Tiwary Basant K.
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| Singapore : , : Springer, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Concise encyclopaedia of bioinformatics and computational biology / / edited by John M. Hancock (Department of Physiology, Development & Neuroscience University of Cambridge Cambridge, UK), Marketa J. Zvelebil (Breakthrough Breast Cancer Research Institute of Cancer Research London, UK)
| Concise encyclopaedia of bioinformatics and computational biology / / edited by John M. Hancock (Department of Physiology, Development & Neuroscience University of Cambridge Cambridge, UK), Marketa J. Zvelebil (Breakthrough Breast Cancer Research Institute of Cancer Research London, UK) |
| Edizione | [Second edition] |
| Pubbl/distr/stampa | Chichester, West Sussex, UK : , : John Wiley & Sons Ltd., , 2014 |
| Descrizione fisica | 1 online resource (832 pages) |
| Disciplina | 572.330285 |
| Soggetto topico |
Bioinformatics -- Dictionaries
Computational biology -- Dictionaries Systems biology -- Dictionaries Bioinformatics Computational biology Biology Health & Biological Sciences Biology - General Bioinformàtica Biologia computacional |
| ISBN |
9780470978726
0470978724 9781118598160 1118598164 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Concise Encyclopaedia of Bioinformatics and Computational Biology; Title Page; Copyright; Contents; List of Contributors; Preface; A; Ab Initio; Ab Initio Gene Prediction, see Gene Prediction, ab initio.; ABNR, see Energy Minimization.; Accuracy (of Protein Structure Prediction); David Jones; Accuracy Measures, see Error Measures.; Adjacent Group; Aidan Budd and Alexandros Stamatakis; Admixture Mapping (Mapping by Admixture Linkage Disequilibrium); Andrew Collins, Mark McCarthy and Steven Wiltshire; Adopted-basis Newton-Raphson Minimization (ABNR), see Energy Minimization.
Affine Gap Penalty, see Gap Penalty.Affinity Propagation-based Clustering; Pedro Larranaga and Concha Bielza; Affymetrix GeneChipTM Oligonucleotide Microarray; Stuart Brown and Dov Greenbaum; Affymetrix Probe Level Analysis; Stuart Brown; After Sphere, see After State.; After State (After Sphere); Thomas D. Schneider; AIC, see Akaike Information Criterion.; Akaike Information Criterion; Pedro Larranaga and Concha Bielza; Algorithm; Matthew He; Alignment (Domain Alignment, Repeats Alignment); Jaap Heringa; Alignment Score; Laszlo Patthy; Allele-Sharing Methods (Non-parametric Linkage Analysis) Mark McCarthy, Steven Wiltshire and Andrew CollinsAllelic Association; Mark McCarthy, Steven Wiltshire and Andrew Collins; Allen Brain Atlas; Dan Bolser; Allopatric Evolution (Allopatric Speciation); A.R. Hoelzel; Allopatric Speciation, see Allopatric Evolution.; AlogP; Bissan Al-Lazikani; Alpha carbon, see Cα (C-Alpha).; Alpha Helix; Roman Laskowski and Tjaart de Beer; Alternative Splicing; Enrique Blanco and Josep F. Abril; Alternative Splicing Gene Prediction, see Gene Prediction, alternative splicing.; Amide Bond (Peptide Bond); Roman Laskowski and Tjaart de Beer; Amino Acid (Residue) Roman Laskowski, Jeremy Baum and Tjaart de BeerAmino Acid Abbreviations, see IUPAC-IUB Codes.; Amino Acid Composition; Jeremy Baum; Amino Acid Exchange Matrix (Dayhoff Matrix, Log Odds Score, PAM (Matrix), BLOSUM Matrix); Jaap Heringa; AMINO Acid Substitution Matrix, see Amino Acid Exchange Matrix.; Amino-terminus, see N-terminus.; Amphipathic; Roman Laskowski and Tjaart de Beer; Analog (Analogue); Dov Greenbaum; Ancestral Lineage, see Offspring Lineage.; Ancestral State Reconstruction; Sudhir Kumar and Alan Filipski; Anchor Points; Roland Dunbrack Annotation Refinement Pipelines, see Gene Prediction.Annotation Transfer (Guilt by Association Annotation); Dov Greenbaum; APBIONET (Asia-Pacific Bioinformatics Network); Pedro Fernandes; Apomorphy; A.R. Hoelzel; APOLLO, see Gene Annotation, visualization tools.; Arc, see Branch (of a Phylogenetic Tree).; Are We There Yet?, see AWTY.; Aromatic; Roman Laskowski and Tjaart de Beer; Array, see Data Structure.; Artificial Neural Networks, see Neural Networks.; ASBCB (The African Society for Bioinformatics and Computational Biology); Pedro Fernandes Association Analysis (Linkage Disequilibrium Analysis) |
| Record Nr. | UNINA-9910970663603321 |
| Chichester, West Sussex, UK : , : John Wiley & Sons Ltd., , 2014 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Data-driven Modelling of Structured Populations : A Practical Guide to the Integral Projection Model / / by Stephen P. Ellner, Dylan Z. Childs, Mark Rees
| Data-driven Modelling of Structured Populations : A Practical Guide to the Integral Projection Model / / by Stephen P. Ellner, Dylan Z. Childs, Mark Rees |
| Autore | Ellner Stephen P |
| Edizione | [1st ed. 2016.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2016 |
| Descrizione fisica | 1 online resource (339 p.) |
| Disciplina | 333.95411072 |
| Collana | Lecture Notes on Mathematical Modelling in the Life Sciences |
| Soggetto topico |
Biomathematics
Bioinformatics Bioinformàtica Computational biology Biomatemàtica Biologia computacional Mathematical and Computational Biology Computer Appl. in Life Sciences |
| ISBN | 3-319-28893-8 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Introduction -- Simple Deterministic IPM -- Basic Analysis 1: Demographic Measures and Events in the Life Cycle -- Basic Analysis 2: Prospective Perturbation Analysis -- Density Dependence -- General Deterministic IPM -- Environmental Stochasticity -- Spatial Models -- Evolutionary Demography -- Future Directions and Advanced Topics. |
| Record Nr. | UNINA-9910254076603321 |
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Ellner Stephen P
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| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2016 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Evolutionary intelligence
| Evolutionary intelligence |
| Pubbl/distr/stampa | [Heidelberg] : , : Springer-Verlag, , ©2008- |
| Descrizione fisica | 1 online resource |
| Soggetto topico |
Artificial intelligence
Artificial intelligence - Mathematical models Artificial Intelligence Computational Biology Intelligence artificielle Bio-informatique Intel·ligència artificial Biologia computacional Bioinformàtica |
| Soggetto genere / forma |
Periodicals.
Revistes electròniques. |
| ISSN | 1864-5917 |
| Formato | Materiale a stampa |
| Livello bibliografico | Periodico |
| Lingua di pubblicazione | eng |
| Altri titoli varianti | Evol. intel |
| Record Nr. | UNINA-9910143844403321 |
| [Heidelberg] : , : Springer-Verlag, , ©2008- | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Handbook of statistical bioinformatics / / Henry Horng-Shing Lu [and three others]
| Handbook of statistical bioinformatics / / Henry Horng-Shing Lu [and three others] |
| Edizione | [2nd ed.] |
| Pubbl/distr/stampa | Berlin : , : Springer, , [2022] |
| Descrizione fisica | 1 online resource (406 pages) |
| Disciplina | 570.285 |
| Collana | Springer Handbooks of Computational Statistics |
| Soggetto topico |
Bioinformatics - Statistical methods
Bioinformatics Bioinformàtica Biologia computacional Informàtica mèdica Estadística matemàtica |
| Soggetto genere / forma | Llibres electrònics |
| ISBN | 3-662-65902-6 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Contents -- Part I Single-Cell Analysis -- Computational and Statistical Methods for Single-Cell RNA Sequencing Data -- 1 Introduction -- 2 Data Preprocessing -- 2.1 Reads Mapping -- 2.2 Cell Barcodes Demultiplexing -- 2.3 UMI Collapsing -- 2.4 Cell Barcodes Selection -- 2.5 Summary -- 3 Data Normalization and Visualization -- 3.1 Background -- 3.2 Global Scaling Normalization for UMI Data -- 3.3 Probabilistic Model-Based Normalization for UMI Data -- 3.4 Dimension Reduction and Cell Clustering -- 4 Dropout Imputation -- 4.1 Background -- 4.2 Cell-Cell Similarity-Based Imputation -- 4.3 Gene-Gene Similarity-Based Imputation -- 4.4 Gene-Gene and Cell-Cell Similarity-Based Imputation -- 4.5 Deep Neural Network-Based Imputation -- 4.6 G2S3 -- 4.7 Methods Evaluation and Comparison -- 5 Differential Expression Analysis -- 5.1 Background -- 5.2 DE Methods Ignoring Subject Effects -- 5.3 DE Methods Considering Subject Effects -- 5.4 iDESC -- 5.5 DE Methods Evaluation and Comparison -- 5.5.1 Type I Error Comparison -- 5.5.2 Statistical Power Comparison -- 6 Concluding Remarks -- References -- Pre-processing, Dimension Reduction, and Clustering for Single-Cell RNA-seq Data -- 1 Introduction -- 2 Pre-processing of scRNA-seq Data -- 2.1 Removal of Batch Effects -- 2.2 Quality Control and Feature Selection -- 3 Dimension Reduction and Clustering -- 3.1 Dimension Reduction -- 3.2 Clustering -- 4 Conclusion -- References -- Integrative Analyses of Single-Cell Multi-Omics Data: A Review from a Statistical Perspective -- 1 Multi-Omics Data Profiled on Different Cells -- 2 Multi-Omics Data Profiled on the Same Single Cells -- 3 Challenges and Future Perspectives -- References -- Approaches to Marker Gene Identification from Single-Cell RNA-Sequencing Data -- 1 Introduction.
2 Marker Gene Selection Relies on Identifying Differentially Expressed Genes -- 3 Methods for Marker Gene Selection -- 3.1 Highest Expressed, Highest Variable -- 4 Supervised Methods -- 4.1 COMET -- 4.2 scGeneFit -- 5 Unsupervised Methods -- 5.1 Seurat -- 5.2 SC3 -- 5.3 SCMarker -- 5.4 scTIM -- 5.5 RankCorr -- 6 Discussion -- References -- Model-Based Clustering of Single-Cell Omics Data -- 1 Introduction -- 2 Single-Cell Transcriptomic Data Clustering -- 2.1 Single-Cell Transcriptomic Data Structure -- 2.2 DIMM-SC -- 2.3 Real Data Example -- 3 Population-Scale Single-Cell Transcriptomic Data Clustering -- 3.1 Population-Scale Single-Cell Transcriptomic Data Structure -- 3.2 BAMM-SC -- 3.3 Real Data Example -- 4 Single-Cell Multi-omics Data Clustering -- 4.1 CITE-seq Data Structure -- 4.2 BREM-SC -- 4.3 Real Data Example -- 5 Concluding Remarks -- References -- Deep Learning Methods for Single-Cell Omics Data -- 1 Introduction -- 2 Factor-Model-Based Deep Learning Approaches -- 2.1 Regularization and Priors on the Latent Factors -- 2.1.1 Gaussian Prior and Variational Inference -- 2.1.2 Adjust for Batch Effects and Confounding Covariates: Identifiability -- 2.1.3 Adjust for Batch Effects and Confounding Covariates: Implementation -- 2.1.4 Model Cell Population Structure in the Latent Space -- 2.2 Distributional Assumptions on Observed Data -- 2.2.1 Model Observed Data from scRNA-seq -- 2.2.2 Model Observed Data from scATAC-seq -- 2.2.3 Model Observed Data from Single-Cell Multiomics Technologies -- 2.3 Post-training Statistical Analyses -- 2.3.1 Denoising -- 2.3.2 Visualization, Clustering, and Trajectory Analysis -- 2.3.3 Prediction -- 3 Deep Learning Methods for Dimension Reduction -- 3.1 Construct the Loss Function -- 3.2 Extra Penalties and Regularization -- 4 Discussion -- References -- Part II Network Analysis. Probabilistic Graphical Models for Gene Regulatory Networks -- 1 Introduction -- 2 Probabilistic Graphical Models -- 2.1 Graphical Model Basics -- 2.2 Markov Networks -- 2.3 Bayesian Networks -- 3 Classic Graphical Models for Reconstructing GRNs -- 3.1 Frequentist Approach -- 3.2 Bayesian Approach -- 3.3 Graphical Models Incorporating Prior Knowledge -- 4 Testing in Graphical Models -- 4.1 Parametric Test -- 4.2 Non-parametric Test for Global Graph Structure -- 5 Conclusion -- References -- Additive Conditional Independence for Large and Complex Biological Structures -- 1 Additive Conditional Independence (ACI) -- 1.1 Additive Reproducing Kernel Hilbert Spaces and Relevant Linear Operators -- 2 Variable Selection via ACI -- 2.1 Nonparametric Variable Selection -- 2.2 Penalized Least-Square Estimation with RKHS Operators -- 2.3 Matrix Representation of Operators and Algorithm -- 2.4 Data Example -- 3 Graphical Modeling Through ACI -- 3.1 Nonparametric Graphical Models -- 3.2 The Additive Conditional Covariance and Partial Correlation Operators -- 3.3 Operator-Level Estimation and the Algorithm -- 3.4 Data Examples -- References -- Integration of Boolean and Bayesian Networks -- 1 Introduction -- 2 Methods -- 2.1 s-p-scores Associated with Networks, SPAN -- 2.2 Network Learning -- 3 Results -- 3.1 An Example -- 3.2 Real Example -- 3.3 Complex Example -- 4 Discussion -- References -- Computational Methods for Identifying MicroRNA-Gene Regulatory Modules -- 1 Introduction -- 2 Identifying MiRNA-Gene Modules by Integrating Heterogeneous Data Sources -- 2.1 Bipartite Graph-Based Methods -- 2.2 Nonnegative Matrix Factorization Methods -- 2.3 Statistical Modeling Approaches -- 3 Evaluating the Performance of MiRNA-Gene Module Identification Methods -- 4 Discussion -- 5 Conclusions -- References -- Causal Inference in Biostatistics -- 1 Introduction. 1.1 Causation and Association -- 1.2 Two Conceptual Frameworks: Causal Effect and Causal Discovery -- 2 Causal Effect -- 2.1 Approaches to Causal Inference -- 2.2 Randomized Clinical Trials -- 2.2.1 Perfect Randomized Trials -- 2.2.2 Randomized Trials with Missing Data -- 2.2.3 Randomized Trials with Post-treatment Variables -- 2.3 Observational Studies -- 2.3.1 Unconfounded Treatment Assignment Conditional on Measured Covariates -- 2.3.2 Unmeasured Cofounding -- 3 Some Current Research Topics -- 3.1 Heterogenous Treatment Effect and Precision Medicine -- 3.2 Integrating Data from Randomized Controlled Trials and Observational Studies -- 3.3 Multiple Treatments -- 4 Software Appendix -- References -- Bayesian Balance Mediation Analysis in Microbiome Studies -- 1 Introduction -- 2 Bayesian Balance Mediation Model -- 2.1 Bayesian Balance Mediation Model with a Binary Treatment -- 2.2 Direct and Mediation Effect and Estimation Based on Predictive Posterior Distribution -- 3 MCMC Sampling -- 3.1 MCMC Sampling -- 3.2 Conditional Distributions -- 4 Applications to Real Data -- 4.1 Mediation Analysis at the Phylum Level -- 4.2 Analysis at the Order Level -- 5 Simulation Studies -- 5.1 Data Generation -- 5.2 Simulation Result -- 6 Discussion -- References -- Part III Systems Biology -- Identifying Genetic Loci Associated with Complex Trait Variability -- 1 Introduction -- 2 The Concept of vQTL -- 3 Statistical Methods for vQTL Mapping -- 3.1 Classical Nonparametric Tests -- 3.2 Regression-Based Methods -- 3.3 Two-Stage Methods -- 3.4 Quantile Integral Linear Model (QUAIL) -- 3.5 Dispersion Effects -- 4 Applications of vQTL -- 4.1 Examples of vQTL -- 4.2 Screening vQTL for Candidate Loci Involved in GxE Interaction -- 4.3 Variance Polygenic Score -- 4.4 Other Applications and Future Directions -- References. Cell Type-Specific Analysis for High-throughput Data -- 1 Introduction -- 2 Cell Type Composition Estimation -- 3 Cell Type-Specific Differential Analysis -- 4 Step-by-step Tutorial -- References -- Recent Development of Computational Methods in the Field of Epitranscriptomics -- 1 Introduction -- 2 MeRIP-seq and Other Technologies for RNA Modification Profiling -- 3 Methods to Analyze MeRIP-seq Data -- 3.1 Count-Based Methods for Simple Study Designs -- 3.2 Methods Compatible with Confounding Factors -- 3.3 A Guide for RNA Differential Methylation Analysis Using RADAR -- 4 Web Resources on m6A Epitranscriptome -- 4.1 Web Servers with m6A Site Prediction -- 4.2 m6A Epitranscriptome Database -- 5 Discussion -- References -- Estimation of Tumor Immune Signatures from Transcriptomics Data -- 1 Introduction -- 2 Regression-Based Deconvolution Algorithms -- 2.1 Linear Least Squares Regression -- 2.2 Support Vector Regression -- 2.3 Other Deconvolution Methods -- 3 Gene Set Enrichment-Based Methods and Other Gene-Based Algorithms -- 3.1 Gene Set Enrichment Analysis (GSEA) -- 3.2 Single-Sample GSEA (ssGSEA) -- 4 Benchmark Studies -- 5 Discussions -- References -- Cross-Linking Mass Spectrometry Data Analysis -- 1 Introduction -- 1.1 Peptide Identification Based on Mass Spectrometry -- 1.2 Cross-Linked Peptides Identification -- 1.2.1 Cross-Linker Selection -- 1.2.2 Chemical Reaction -- 1.2.3 Enzyme Digestion -- 1.2.4 Enrichment of Cross-Linked Peptides -- 1.2.5 LC-MS and MS2 Acquisition -- 1.2.6 Data Interpretation -- 1.2.7 Quality Control -- 1.2.8 Downstream Applications -- 2 Non-cleavable Cross-Linking Methods -- 3 Cleavable Cross-Linking Methods -- 4 Time-Complexity Comparison Between Non-cleavable Methods and Cleavable Methods -- 5 False Discovery Rate in CL-MS -- 5.1 Target-Decoy Approach in Linear Peptides Identification. 5.2 TDA in Cross-Linked Peptides Identification. |
| Record Nr. | UNINA-9910634054103321 |
| Berlin : , : Springer, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Handbook of statistical bioinformatics / / Henry Horng-Shing Lu [and three others]
| Handbook of statistical bioinformatics / / Henry Horng-Shing Lu [and three others] |
| Edizione | [2nd ed.] |
| Pubbl/distr/stampa | Berlin : , : Springer, , [2022] |
| Descrizione fisica | 1 online resource (406 pages) |
| Disciplina | 570.285 |
| Collana | Springer Handbooks of Computational Statistics |
| Soggetto topico |
Bioinformatics - Statistical methods
Bioinformatics Bioinformàtica Biologia computacional Informàtica mèdica Estadística matemàtica |
| Soggetto genere / forma | Llibres electrònics |
| ISBN | 3-662-65902-6 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Contents -- Part I Single-Cell Analysis -- Computational and Statistical Methods for Single-Cell RNA Sequencing Data -- 1 Introduction -- 2 Data Preprocessing -- 2.1 Reads Mapping -- 2.2 Cell Barcodes Demultiplexing -- 2.3 UMI Collapsing -- 2.4 Cell Barcodes Selection -- 2.5 Summary -- 3 Data Normalization and Visualization -- 3.1 Background -- 3.2 Global Scaling Normalization for UMI Data -- 3.3 Probabilistic Model-Based Normalization for UMI Data -- 3.4 Dimension Reduction and Cell Clustering -- 4 Dropout Imputation -- 4.1 Background -- 4.2 Cell-Cell Similarity-Based Imputation -- 4.3 Gene-Gene Similarity-Based Imputation -- 4.4 Gene-Gene and Cell-Cell Similarity-Based Imputation -- 4.5 Deep Neural Network-Based Imputation -- 4.6 G2S3 -- 4.7 Methods Evaluation and Comparison -- 5 Differential Expression Analysis -- 5.1 Background -- 5.2 DE Methods Ignoring Subject Effects -- 5.3 DE Methods Considering Subject Effects -- 5.4 iDESC -- 5.5 DE Methods Evaluation and Comparison -- 5.5.1 Type I Error Comparison -- 5.5.2 Statistical Power Comparison -- 6 Concluding Remarks -- References -- Pre-processing, Dimension Reduction, and Clustering for Single-Cell RNA-seq Data -- 1 Introduction -- 2 Pre-processing of scRNA-seq Data -- 2.1 Removal of Batch Effects -- 2.2 Quality Control and Feature Selection -- 3 Dimension Reduction and Clustering -- 3.1 Dimension Reduction -- 3.2 Clustering -- 4 Conclusion -- References -- Integrative Analyses of Single-Cell Multi-Omics Data: A Review from a Statistical Perspective -- 1 Multi-Omics Data Profiled on Different Cells -- 2 Multi-Omics Data Profiled on the Same Single Cells -- 3 Challenges and Future Perspectives -- References -- Approaches to Marker Gene Identification from Single-Cell RNA-Sequencing Data -- 1 Introduction.
2 Marker Gene Selection Relies on Identifying Differentially Expressed Genes -- 3 Methods for Marker Gene Selection -- 3.1 Highest Expressed, Highest Variable -- 4 Supervised Methods -- 4.1 COMET -- 4.2 scGeneFit -- 5 Unsupervised Methods -- 5.1 Seurat -- 5.2 SC3 -- 5.3 SCMarker -- 5.4 scTIM -- 5.5 RankCorr -- 6 Discussion -- References -- Model-Based Clustering of Single-Cell Omics Data -- 1 Introduction -- 2 Single-Cell Transcriptomic Data Clustering -- 2.1 Single-Cell Transcriptomic Data Structure -- 2.2 DIMM-SC -- 2.3 Real Data Example -- 3 Population-Scale Single-Cell Transcriptomic Data Clustering -- 3.1 Population-Scale Single-Cell Transcriptomic Data Structure -- 3.2 BAMM-SC -- 3.3 Real Data Example -- 4 Single-Cell Multi-omics Data Clustering -- 4.1 CITE-seq Data Structure -- 4.2 BREM-SC -- 4.3 Real Data Example -- 5 Concluding Remarks -- References -- Deep Learning Methods for Single-Cell Omics Data -- 1 Introduction -- 2 Factor-Model-Based Deep Learning Approaches -- 2.1 Regularization and Priors on the Latent Factors -- 2.1.1 Gaussian Prior and Variational Inference -- 2.1.2 Adjust for Batch Effects and Confounding Covariates: Identifiability -- 2.1.3 Adjust for Batch Effects and Confounding Covariates: Implementation -- 2.1.4 Model Cell Population Structure in the Latent Space -- 2.2 Distributional Assumptions on Observed Data -- 2.2.1 Model Observed Data from scRNA-seq -- 2.2.2 Model Observed Data from scATAC-seq -- 2.2.3 Model Observed Data from Single-Cell Multiomics Technologies -- 2.3 Post-training Statistical Analyses -- 2.3.1 Denoising -- 2.3.2 Visualization, Clustering, and Trajectory Analysis -- 2.3.3 Prediction -- 3 Deep Learning Methods for Dimension Reduction -- 3.1 Construct the Loss Function -- 3.2 Extra Penalties and Regularization -- 4 Discussion -- References -- Part II Network Analysis. Probabilistic Graphical Models for Gene Regulatory Networks -- 1 Introduction -- 2 Probabilistic Graphical Models -- 2.1 Graphical Model Basics -- 2.2 Markov Networks -- 2.3 Bayesian Networks -- 3 Classic Graphical Models for Reconstructing GRNs -- 3.1 Frequentist Approach -- 3.2 Bayesian Approach -- 3.3 Graphical Models Incorporating Prior Knowledge -- 4 Testing in Graphical Models -- 4.1 Parametric Test -- 4.2 Non-parametric Test for Global Graph Structure -- 5 Conclusion -- References -- Additive Conditional Independence for Large and Complex Biological Structures -- 1 Additive Conditional Independence (ACI) -- 1.1 Additive Reproducing Kernel Hilbert Spaces and Relevant Linear Operators -- 2 Variable Selection via ACI -- 2.1 Nonparametric Variable Selection -- 2.2 Penalized Least-Square Estimation with RKHS Operators -- 2.3 Matrix Representation of Operators and Algorithm -- 2.4 Data Example -- 3 Graphical Modeling Through ACI -- 3.1 Nonparametric Graphical Models -- 3.2 The Additive Conditional Covariance and Partial Correlation Operators -- 3.3 Operator-Level Estimation and the Algorithm -- 3.4 Data Examples -- References -- Integration of Boolean and Bayesian Networks -- 1 Introduction -- 2 Methods -- 2.1 s-p-scores Associated with Networks, SPAN -- 2.2 Network Learning -- 3 Results -- 3.1 An Example -- 3.2 Real Example -- 3.3 Complex Example -- 4 Discussion -- References -- Computational Methods for Identifying MicroRNA-Gene Regulatory Modules -- 1 Introduction -- 2 Identifying MiRNA-Gene Modules by Integrating Heterogeneous Data Sources -- 2.1 Bipartite Graph-Based Methods -- 2.2 Nonnegative Matrix Factorization Methods -- 2.3 Statistical Modeling Approaches -- 3 Evaluating the Performance of MiRNA-Gene Module Identification Methods -- 4 Discussion -- 5 Conclusions -- References -- Causal Inference in Biostatistics -- 1 Introduction. 1.1 Causation and Association -- 1.2 Two Conceptual Frameworks: Causal Effect and Causal Discovery -- 2 Causal Effect -- 2.1 Approaches to Causal Inference -- 2.2 Randomized Clinical Trials -- 2.2.1 Perfect Randomized Trials -- 2.2.2 Randomized Trials with Missing Data -- 2.2.3 Randomized Trials with Post-treatment Variables -- 2.3 Observational Studies -- 2.3.1 Unconfounded Treatment Assignment Conditional on Measured Covariates -- 2.3.2 Unmeasured Cofounding -- 3 Some Current Research Topics -- 3.1 Heterogenous Treatment Effect and Precision Medicine -- 3.2 Integrating Data from Randomized Controlled Trials and Observational Studies -- 3.3 Multiple Treatments -- 4 Software Appendix -- References -- Bayesian Balance Mediation Analysis in Microbiome Studies -- 1 Introduction -- 2 Bayesian Balance Mediation Model -- 2.1 Bayesian Balance Mediation Model with a Binary Treatment -- 2.2 Direct and Mediation Effect and Estimation Based on Predictive Posterior Distribution -- 3 MCMC Sampling -- 3.1 MCMC Sampling -- 3.2 Conditional Distributions -- 4 Applications to Real Data -- 4.1 Mediation Analysis at the Phylum Level -- 4.2 Analysis at the Order Level -- 5 Simulation Studies -- 5.1 Data Generation -- 5.2 Simulation Result -- 6 Discussion -- References -- Part III Systems Biology -- Identifying Genetic Loci Associated with Complex Trait Variability -- 1 Introduction -- 2 The Concept of vQTL -- 3 Statistical Methods for vQTL Mapping -- 3.1 Classical Nonparametric Tests -- 3.2 Regression-Based Methods -- 3.3 Two-Stage Methods -- 3.4 Quantile Integral Linear Model (QUAIL) -- 3.5 Dispersion Effects -- 4 Applications of vQTL -- 4.1 Examples of vQTL -- 4.2 Screening vQTL for Candidate Loci Involved in GxE Interaction -- 4.3 Variance Polygenic Score -- 4.4 Other Applications and Future Directions -- References. Cell Type-Specific Analysis for High-throughput Data -- 1 Introduction -- 2 Cell Type Composition Estimation -- 3 Cell Type-Specific Differential Analysis -- 4 Step-by-step Tutorial -- References -- Recent Development of Computational Methods in the Field of Epitranscriptomics -- 1 Introduction -- 2 MeRIP-seq and Other Technologies for RNA Modification Profiling -- 3 Methods to Analyze MeRIP-seq Data -- 3.1 Count-Based Methods for Simple Study Designs -- 3.2 Methods Compatible with Confounding Factors -- 3.3 A Guide for RNA Differential Methylation Analysis Using RADAR -- 4 Web Resources on m6A Epitranscriptome -- 4.1 Web Servers with m6A Site Prediction -- 4.2 m6A Epitranscriptome Database -- 5 Discussion -- References -- Estimation of Tumor Immune Signatures from Transcriptomics Data -- 1 Introduction -- 2 Regression-Based Deconvolution Algorithms -- 2.1 Linear Least Squares Regression -- 2.2 Support Vector Regression -- 2.3 Other Deconvolution Methods -- 3 Gene Set Enrichment-Based Methods and Other Gene-Based Algorithms -- 3.1 Gene Set Enrichment Analysis (GSEA) -- 3.2 Single-Sample GSEA (ssGSEA) -- 4 Benchmark Studies -- 5 Discussions -- References -- Cross-Linking Mass Spectrometry Data Analysis -- 1 Introduction -- 1.1 Peptide Identification Based on Mass Spectrometry -- 1.2 Cross-Linked Peptides Identification -- 1.2.1 Cross-Linker Selection -- 1.2.2 Chemical Reaction -- 1.2.3 Enzyme Digestion -- 1.2.4 Enrichment of Cross-Linked Peptides -- 1.2.5 LC-MS and MS2 Acquisition -- 1.2.6 Data Interpretation -- 1.2.7 Quality Control -- 1.2.8 Downstream Applications -- 2 Non-cleavable Cross-Linking Methods -- 3 Cleavable Cross-Linking Methods -- 4 Time-Complexity Comparison Between Non-cleavable Methods and Cleavable Methods -- 5 False Discovery Rate in CL-MS -- 5.1 Target-Decoy Approach in Linear Peptides Identification. 5.2 TDA in Cross-Linked Peptides Identification. |
| Record Nr. | UNISA-996503551803316 |
| Berlin : , : Springer, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
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IEEE/ACM transactions on computational biology and bioinformatics
| IEEE/ACM transactions on computational biology and bioinformatics |
| Pubbl/distr/stampa | New York, NY, : IEEE Computer Society, ©2004- |
| Disciplina | 572 |
| Soggetto topico |
Computational biology
Bioinformatics Computational Biology Bio-informatique Biologia computacional Bioinformàtica |
| Soggetto genere / forma |
Periodicals.
Revistes electròniques. |
| ISSN | 1557-9964 |
| Formato | Materiale a stampa |
| Livello bibliografico | Periodico |
| Lingua di pubblicazione | eng |
| Altri titoli varianti |
Computational biology and bioinformatics
Computational biology and bioinformatics, IEEE/ACM transactions on |
| Record Nr. | UNINA-9910143539403321 |
| New York, NY, : IEEE Computer Society, ©2004- | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Molecular informatics
| Molecular informatics |
| Pubbl/distr/stampa | [Weinheim, Germany], : Wiley-VCH Verlag |
| Descrizione fisica | 1 online resource |
| Soggetto topico |
Cheminformatics
Bioinformatics Drugs - Structure-activity relationships Structure-activity relationships (Biochemistry) QSAR (Biochemistry) Computational Biology Computer-Aided Design Biochemical Phenomena Chimio-informatique Relations structure-activité (Biochimie) Médicaments - Relations structure-activité Bio-informatique Conception assistée par ordinateur computer-aided designs (visual works) computer-aided design (process) Structuur-activiteit-relatie Bioinformàtica Biologia computacional |
| Soggetto genere / forma |
Periodical
periodicals. Periodicals. Périodiques. Revistes electròniques. |
| ISSN | 1868-1751 |
| Formato | Materiale a stampa |
| Livello bibliografico | Periodico |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910143076903321 |
| [Weinheim, Germany], : Wiley-VCH Verlag | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Network modeling and analysis in health informatics and bioinformatics
| Network modeling and analysis in health informatics and bioinformatics |
| Pubbl/distr/stampa | [Wien], : Springer-Verlag, 2012- |
| Descrizione fisica | 1 online resource |
| Soggetto topico |
Bioinformatics
Medical informatics Medical Informatics Computational Biology Bioinformàtica Informàtica mèdica Biologia computacional |
| Soggetto genere / forma |
Periodical
Periodicals. Revistes electròniques. |
| ISSN | 2192-6670 |
| Formato | Materiale a stampa |
| Livello bibliografico | Periodico |
| Lingua di pubblicazione | eng |
| Altri titoli varianti |
NetMAHIB
Netw model anal health inform bioinforma |
| Record Nr. | UNINA-9910307953403321 |
| [Wien], : Springer-Verlag, 2012- | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
