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Analysis of Variance for High-Dimensional Data : Applications in Life, Food, and Chemical Sciences
| Analysis of Variance for High-Dimensional Data : Applications in Life, Food, and Chemical Sciences |
| Autore | Smilde Age K |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Newark : , : John Wiley & Sons, Incorporated, , 2025 |
| Descrizione fisica | 1 online resource (339 pages) |
| Disciplina | 570.285 |
| Altri autori (Persone) |
MariniFederico
WesterhuisJohan A LilandKristian Hovde |
| Soggetto topico |
Life sciences - Data processing
Food science - Data processing Chemistry - Data processing Experimental design |
| ISBN |
1-394-21124-4
1-394-21122-8 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover -- Title Page -- Copyright -- Contents -- About the Authors -- Foreword -- Preface -- Chapter 1 Introduction -- 1.1 Types of Data -- 1.2 Statistical Design of Experiments -- 1.3 High‐Dimensional Data -- 1.4 Examples -- 1.4.1 Metabolomics -- 1.4.2 Genomics -- 1.4.3 Microbiome -- 1.4.4 Proteomics -- 1.4.5 Food Science -- 1.4.6 Sensory Science -- 1.4.7 Chemistry -- 1.5 Complexities -- 1.5.1 Normalization -- 1.5.2 Different Measurement Scales -- 1.5.3 Different Distributions -- 1.5.4 Heteroscedastic Error -- 1.5.5 Comparability -- 1.5.6 Sparseness, Non‐detects, and Missing Values -- 1.5.7 Unbalancedness -- 1.6 Direct Versus Indirect Methods -- 1.7 Some History -- 1.A.1 Types of Measurements -- 1.A.2 Notation and Terminology -- 1.A.3 Some Definitions -- 1.A.4 Abbreviations -- Chapter 2 Basic Theory and Concepts -- 2.1 Mathematical Background -- 2.1.1 Vector Spaces and Subspaces -- 2.1.2 Matrix Decompositions -- 2.1.3 Inverses and Generalized Inverses -- 2.1.4 Distances and Projections -- 2.1.4.1 Formal Description of Distances -- 2.1.4.2 Projections -- 2.1.5 Principal Component Analysis -- 2.2 Statistical Background -- 2.2.1 Estimation Methods -- 2.2.1.1 Least Squares -- 2.2.1.2 Maximum Likelihood -- 2.2.2 Regression Methods -- 2.2.2.1 Multiple Linear Regression: Full Rank Case -- 2.2.2.2 Multiple Linear Regression Using Dummy Variables -- 2.2.2.3 Multiple Linear Regression: Rank Deficient Case -- 2.2.2.4 Penalized Regression -- 2.2.2.5 Principal Component Regression -- 2.2.2.6 Partial Least Squares -- 2.2.2.7 Redundancy Analysis -- 2.2.3 Significance Tests -- 2.2.3.1 Classical Tests -- 2.2.3.2 Permutation Tests -- 2.2.3.3 Likelihood Ratio Tests -- 2.3 Association Measures -- 2.3.1 Pearson and Spearman Correlation Coefficients -- 2.3.2 Problems with Correlations -- Chapter 3 Linear Models -- 3.1 Introduction -- 3.2 Simple ANOVA Models.
3.2.1 One‐Way ANOVA -- 3.2.2 Two‐Way ANOVA -- 3.2.2.1 Crossed Designs -- 3.2.2.2 Nested Designs -- 3.2.3 Unbalanced Designs -- 3.2.3.1 One‐Way ANOVA -- 3.2.3.2 Two‐Way ANOVA for Crossed Designs -- 3.2.3.3 Nested ANOVA -- 3.3 Regression Formulation, Estimability, and Contrasts -- 3.4 Coding Schemes -- 3.4.1 Codings for Balanced Designs -- 3.4.1.1 One‐Way Layout -- 3.4.1.2 Two‐Way Crossed Designs -- 3.4.1.3 Two‐Way Nested Designs -- 3.4.2 Codings for Unbalanced Designs -- 3.5 Advanced Models -- 3.5.1 Variance Component Models -- 3.5.2 Linear Mixed Models -- 3.5.2.1 General Idea -- 3.5.2.2 Estimation of Model Parameters -- 3.5.2.3 Repeated Measures ANOVA -- 3.5.2.4 Cross‐over Designs and Models -- 3.5.2.5 Longitudinal LMMs -- 3.6 Hasse Diagrams -- 3.6.1 Building a Hasse Diagram -- 3.7 Validation -- 3.7.1 Classical Tests Revisited -- 3.7.2 Expected Mean Squares from Hasse Diagrams -- 3.7.3 Permutation Tests -- 3.7.3.1 Exact Tests -- 3.7.3.2 Approximate Tests -- 3.8 Miscellaneous Models -- 3.8.1 Multivariate Analysis of Variance -- 3.8.1.1 Traditional Multivariate Analysis of Variance -- 3.8.1.2 Significance Testing in MANOVA -- 3.8.1.3 Regression Formulation of MANOVA -- 3.8.2 Multivariate LMMs -- 3.A.1 Proof -- 3.A.2 Relationships Between Codings -- 3.A.3 Practical Aspects of Codings -- Chapter 4 ASCA and Related Methods -- 4.1 ASCA -- 4.1.1 Basic Idea of ASCA -- 4.1.2 Properties of ASCA -- 4.1.3 Permutation Tests for ASCA -- 4.1.4 Back‐Projection -- 4.1.5 Scaling in ASCA -- 4.1.6 Group‐wise ASCA -- 4.1.7 Variable‐Selection ASCA -- 4.1.8 REP‐ASCA -- 4.1.9 ASCA as a Multivariate Multiple Regression Model -- 4.1.10 Geometry of ASCA -- 4.1.10.1 Geometry of ASCA in Row‐Space -- 4.1.10.2 Geometry of ASCA in Column‐Space -- 4.2 APCA -- 4.2.1 Basic Idea of APCA -- 4.2.2 Comparing APCA with ASCA -- 4.3 ASCA+ -- 4.3.1 Confidence Ellipsoids for ASCA. 4.3.2 ASCA and ASCA+ as RDA Models -- 4.4 Principal Response Curves -- 4.5 SMART -- 4.6 ASCA, PRC, and SMART Compared -- 4.7 MSCA -- 4.A.1 Proof of Equation -- 4.A.2 Proof of Equation -- Chapter 5 Alternative Methods -- 5.1 General Introduction -- 5.2 PLSR‐Based Methods -- 5.2.1 ANOVA‐TP -- 5.2.2 ANOVA Multiblock Orthogonal Partial Least Squares (AMOPLS) -- 5.3 LMM‐Based Methods -- 5.3.1 RM‐ASCA+ with Qualitative Time Models -- 5.3.2 Validation of the RM‐ASCA+ Model -- 5.3.2.1 Validation of RM‐ASCA+ Models with Nonparametric Bootstrap -- 5.3.2.2 Validation of RM‐ASCA+ Models with Permutation Testing -- 5.3.2.3 Visualization -- 5.3.2.4 RM‐ASCA+ with Quantitative Time Models -- 5.3.3 LiMM‐PCA -- 5.3.3.1 Validation -- 5.3.3.2 Visualization of Effects in LiMM‐PCA -- 5.4 Miscellaneous Methods -- 5.4.1 PC‐ANOVA -- 5.4.1.1 Basic Idea of PC‐ANOVA -- 5.4.1.2 Comparing PC‐ANOVA with ASCA -- 5.4.2 PARAFASCA -- 5.4.3 PE‐ASCA -- 5.4.4 rMANOVA -- 5.4.5 Fifty-Fifty MANOVA -- 5.4.6 AComDim -- 5.4.7 General Effect Modeling (GEM) -- Chapter 6 Distance‐based Methods -- 6.1 Introduction -- 6.1.1 Double Zeros -- 6.1.2 Horseshoe Effect -- 6.1.3 Compositionality -- 6.2 Methods -- 6.2.1 Principal Coordinate Analysis -- 6.2.2 PERMANOVA -- 6.2.2.1 PERMANOVA Calculated from the Gower Matrix -- 6.2.2.2 PERMANOVA of Non‐Euclidean Dissimilarity Matrices -- 6.2.3 Effect Sizes in PERMANOVA -- 6.2.4 Permutations in PERMANOVA -- 6.2.5 Assumptions for PERMANOVA -- 6.3 ANOSIM -- Chapter 7 Reviews and Reflections -- 7.1 Reviews -- 7.1.1 Metabolomics -- 7.1.1.1 Plant Science -- 7.1.1.2 Microbiology and Biotechnology -- 7.1.1.3 Animal Science -- 7.1.1.4 Human Science -- 7.1.2 Microbiome -- 7.1.3 Genomics -- 7.1.4 Proteomics -- 7.1.5 Food Science -- 7.1.6 Sensory Science -- 7.1.7 Chemistry -- 7.2 Reflections -- 7.2.1 Summary of Reviews -- 7.2.2 Overview of Methods. 7.2.3 Remaining Challenges -- 7.2.3.1 ASCA+ and Partial RDA -- 7.2.3.2 Permutations: Correlations and Unbalancedness -- 7.2.3.3 PERMANOVA and Effect Sizes -- 7.2.3.4 Back‐Projection Approach -- 7.2.3.5 Inferential Statistics -- 7.2.3.6 Advanced HD‐ANOVA Methods -- Chapter 8 Software -- 8.1 HD‐ANOVA Software -- 8.2 R Package HDANOVA -- 8.3 Installing and Starting the Package -- 8.4 Data Handling -- 8.4.1 Read from File -- 8.4.2 Data Pre‐processing -- 8.4.2.1 Re‐coding Categorical data -- 8.4.3 Data Structures for Analysis Including Blocks -- 8.4.3.1 Create List of Blocks -- 8.4.3.2 Create data.frame of Blocks -- 8.5 Analysis of Variance (ANOVA) -- 8.5.1 Simulated Data -- 8.5.2 Fixed Effect Models -- 8.5.2.1 One‐Way ANOVA -- 8.5.2.2 Two‐Way Crossed Effects ANOVA -- 8.5.2.3 Types of Sums of Squares -- 8.5.2.4 Coding Schemes -- 8.5.2.5 Fixed Effect Nested ANOVA -- 8.5.3 Linear Mixed Models -- 8.5.3.1 Least Squares − mixlm -- 8.5.3.2 Restrictions -- 8.5.3.3 Repeated Measures -- 8.5.3.4 REML -- 8.5.4 Multivariate ANOVA (MANOVA) -- 8.6 Basic ASCA Family -- 8.6.1 Fit ASCA Model -- 8.6.1.1 Permutation Testing -- 8.6.1.2 Random Effects -- 8.6.1.3 Scores and Loadings -- 8.6.1.4 Data Ellipsoids and Confidence Ellipsoids -- 8.6.1.5 Combined Effects -- 8.6.1.6 Quantitative Effects -- 8.6.2 ANOVA‐PCA (APCA) -- 8.6.3 PC‐ANOVA -- 8.6.4 MSCA -- 8.6.5 LiMM‐PCA -- 8.6.6 Repeated Measures ASCA -- 8.7 Alternative Methods -- 8.7.1 Principal Response Curves (PRC) -- 8.7.2 Permutation‐Based MANOVA (PERMANOVA) -- 8.8 Software Packages -- 8.8.1 R Packages -- 8.8.2 MATLAB Toolboxes -- 8.8.3 Python -- References -- Index -- EULA. |
| Record Nr. | UNINA-9911020059203321 |
Smilde Age K
|
||
| Newark : , : John Wiley & Sons, Incorporated, , 2025 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Bioinformatics
| Bioinformatics |
| Pubbl/distr/stampa | Oxford : , : Oxford University Press, , 1998- |
| Descrizione fisica | 1 online resource (volumes) |
| Disciplina | 570.285 |
| Soggetto topico |
Life sciences - Data processing
Genomes - Data processing Computational Biology Genome Sciences de la vie - Informatique Génomes - Informatique Bio-informatique Génomes Biologie Informatietechnologie |
| Soggetto genere / forma |
Periodical
Periodicals. |
| Soggetto non controllato | BIOINFORMATICS |
| ISSN | 1367-4811 |
| Formato | Materiale a stampa |
| Livello bibliografico | Periodico |
| Lingua di pubblicazione | eng |
| Altri titoli varianti | Bioinformatics online |
| Record Nr. | UNISA-996217543703316 |
| Oxford : , : Oxford University Press, , 1998- | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Bioinformatics
| Bioinformatics |
| Pubbl/distr/stampa | Oxford : , : Oxford University Press, , 1998- |
| Descrizione fisica | 1 online resource (volumes) |
| Disciplina | 570.285 |
| Soggetto topico |
Bioinformatics
Life sciences - Data processing Genomes - Data processing Computational Biology Genome Sciences de la vie - Informatique Génomes - Informatique Bio-informatique Génomes Biologie Informatietechnologie |
| Soggetto genere / forma |
Periodical
Periodicals. |
| ISSN | 1367-4811 |
| Formato | Materiale a stampa |
| Livello bibliografico | Periodico |
| Lingua di pubblicazione | eng |
| Altri titoli varianti | Bioinformatics online |
| Record Nr. | UNINA-9910145557903321 |
| Oxford : , : Oxford University Press, , 1998- | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Blockchain in life sciences / / Wendy Charles
| Blockchain in life sciences / / Wendy Charles |
| Autore | Charles Wendy |
| Pubbl/distr/stampa | Singapore : , : Springer, , [2022] |
| Descrizione fisica | 1 online resource (349 pages) |
| Disciplina | 005.74 |
| Collana | Blockchain Technologies |
| Soggetto topico |
Blockchains (Databases)
Life sciences - Data processing |
| ISBN |
9789811929762
9789811929755 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Foreword -- Preface -- Acknowledgments -- Contents -- About the Editor -- Abbreviations -- List of Figures -- List of Tables -- Blockchain Uses and Real World Evidence -- Introduction to Blockchain -- 1 Introduction -- 2 Blockchain Core Characteristics -- 2.1 Ledgers -- 2.2 Cryptography -- 2.3 Immutability (Tamper Evidence and Tamper Resistance) -- 2.4 Distribution -- 3 Blockchain Features -- 3.1 Permissionless Versus Permissioned -- 3.2 Permissionless -- 3.3 Off-Chain Versus On-Chain Storage -- 3.4 Smart Contracts -- 4 Blockchain Benefits for Life Sciences -- 4.1 Trust -- 4.2 Audit Trails-Provenance -- 4.3 Data Transparency Versus Privacy -- 4.4 Security -- 4.5 Performance -- 5 Conclusions -- 6 Key Terminology and Definitions -- References -- Blockchain in Pharmaceutical Research and the Pharmaceutical Value Chain -- 1 Brief Overview of Pharmaceutical Research -- 1.1 Drug Delivery and Discovery -- 1.2 Challenges Associated with Drug Delivery and Discovery -- 1.3 Challenges Associated with Preclinical (i.e., In Vitro, In Vivo) and Phase 0/I-IV Studies -- 2 Introduction of the End-To-End Pharmaceutical Value Chain -- 2.1 Five Main Categories: (1) Research and Discovery, (2) Clinical Development, (3) Manufacturing and Supply Chain, (4) Launch and Commercial Considerations, and (5) Monitoring and Health Records -- 2.2 Differentiating Pharmaceutical Value Chain from Pharmaceutical Supply Chain -- 3 Blockchain Efforts Within Pharmaceutical Industry -- 3.1 Pharmaceutical Users Software Exchange (PhUSE) Blockchain Project -- 3.2 Innovative Medicines Initiative (IMI) Blockchain-Enabled Healthcare -- 3.3 The MELLODDY Project and Millions of Molecules Blockchain + Smart Contracts for Human Participant Regulations and Consent Management -- 3.4 Information Exchange and Data Transformation (INFORMED) Initiative -- 3.5 Moneyball Medicine.
4 Mapping Blockchain Characteristics to Pain Points in the Pharmaceutical Value Chain -- 4.1 Adapted Fit-For-Purpose Framework and Design Elements -- 4.2 Matching Characteristics (e.g., Decentralized, Distributed, Conditionally Immutable, Scalable, Cryptographically Secured) to Identified Pain Points in Each of the 5 Categories -- 5 Blockchain-But Not in a Vacuum -- 5.1 Blockchain-Complementary Established and Emerging (e.g., Machine Learning, Artificial Intelligence) Technologies for the Pharmaceutical Value Chain -- 6 Debunking Myths Around Challenges with Blockchain -- 6.1 The Myth of the Technical Challenge -- 6.2 The Reality of Challenges Tied to Change Management, Resource Allocation, Paradigm Shift, and Reaching Consensus -- 7 Blockchain and The Idea Pipeline -- 7.1 Pharmacogenomics -- 7.2 Collaborative Pharmaceutical Development -- 7.3 Patient Access, Medication Reclamation, and Prescription Waste Reduction -- 7.4 The Evolution of the Traditional Retail Pharmacy -- 8 Future Directions -- 9 Conclusions -- References -- Blockchain-Based Scalable Network for Bioinformatics and Internet of Medical Things (IoMT) -- 1 Introduction -- 1.1 Data Ownership -- 1.2 Data in Blockchain-Based Network -- 2 Case Implementation of Internet of Medical Things (IoMT) with Real Ownership -- 2.1 The Synsal Network -- 2.2 Sensors, Device Engineering, and Scaling in the Synsal Network -- 3 Tokenization and Value Scaling in the Blockchain-Based Network of Hardware Devices -- 3.1 Tokenization and Value Scaling -- 3.2 Basic Stabilization Tokenomics -- 4 Future Directions -- 5 Conclusions -- References -- Blockchains and Genomics: Promises and Limits of Technology -- 1 Introduction: A Brief History of Capitalization on Genes -- 2 The Scientific and Market Value of Genomic Data -- 2.1 On the Nature of Data, and the Data of Nature -- 2.2 Fair and Sustainable Data Use. 3 Democratize, Decentralize, and Disintermediate Data (The Three Ds) -- 3.1 Blockchain Genomics: The Current Slate -- 4 Why Genomes Cannot Be Owned -- 5 How Shall We Treat Genes? -- 6 What About Non-Fungible Tokens, NFTs? -- 6.1 The Need for Regulation -- 7 Future Directions -- 8 Conclusions -- References -- Convergence of Blockchain and AI for IoT in Connected Life Sciences -- 1 Introduction -- 1.1 Fueling the Digital Transformation in Health and Life Sciences -- 1.2 Technology Unification -- 2 Harnessing the Power of Data-Driven Technologies in Life Sciences -- 2.1 Data-Driven Technologies in Life Sciences -- 3 Innovating in a Highly Regulated Industry -- 4 Essential Elements for Data Strategy in Life Sciences -- 4.1 Data Building Blocks -- 5 Prioritizing Risk Management in Life Sciences -- 6 Opportunities and Challenges for Emerging Digital Technologies in Life Sciences -- 6.1 Major Milestones in Life Sciences Product Development -- 7 Strategic Planning Frameworks -- 7.1 Blockchain and AI to Mitigate Risks of IoT/BYOD -- 7.2 Blockchain-AI Platforms and Infrastructures -- 8 Future Directions -- 8.1 Human as a Platform -- 8.2 Thinking Beyond the Adoption of Technology -- 9 Conclusions -- References -- A Blockchain-Empowered Federated Learning System and the Promising Use in Drug Discovery -- 1 Introduction -- 2 Overview of Federated Learning and Blockchain -- 2.1 Federated Learning -- 2.2 Barriers and Challenges in Drug Discovery -- 2.3 Challenges in Federated Learning -- 2.4 Blockchain Benefit for Federated Learning -- 2.5 The Benefits of Blockchain-Empowered Federated Learning for Drug Discovery -- 3 The Rahasak-ML Platform -- 3.1 Overview -- 3.2 Key Components -- 4 Rahasak-ML Federated Learning Process -- 4.1 Overview -- 4.2 Incremental Training Flow -- 4.3 Finalizing Model. 4.4 The Use Case of Blockchain-Empowered Federated Learning in the Medical Field -- 5 Future Directions -- 5.1 Data Heterogeneity -- 5.2 Efficiency and Effectiveness -- 5.3 Model Interpretation -- 6 Conclusions -- References -- Considerations for Ensuring Success of Blockchain in Life Sciences Research -- Valuing Research Data: Blockchain-Based Management Methods -- 1 Introduction -- 1.1 Nature of Health Data -- 1.2 Health Data Management -- 2 Data as an Asset -- 2.1 How to Value Data Assets -- 3 Data Sales Methods -- 3.1 Data Brokers -- 3.2 Centralized Data Marketplaces -- 3.3 Decentralized Data Marketplaces -- 3.4 Non-Fungible Tokens -- 4 Considerations -- 4.1 Ethical Considerations -- 4.2 Ownership -- 4.3 Data Considerations -- 5 Recommendations -- 5.1 Ethical Recommendations -- 5.2 Data Recommendations -- 5.3 Legal Recommendations -- 6 Future Directions -- 6.1 Regulations -- 6.2 Future Research -- 7 Conclusions -- 7.1 Key Terminology and Definitions -- References -- Blockchain Adoption in Life Sciences Organizations: Socio-organizational Barriers and Adoption Strategies -- 1 Introduction -- 2 Background Literature -- 3 Research Methods -- 4 Findings -- 4.1 The State of the Blockchain + Life Sciences Ecosystem -- 4.2 Socio-organizational Barriers for Blockchain Adoption in Life Sciences -- 4.3 Barrier 4: The Lack of an "Ecosystem" Mindset -- 4.4 Adoption Strategies -- 5 Discussion -- 5.1 Limitations and Future Directions -- 6 Conclusion -- References -- Blockchain Governance Strategies -- 1 Introduction -- 2 Defining Governance -- 3 A Deeper Dive: Blockchain Governance -- 3.1 On-Chain Governance -- 3.2 Off-Chain Governance -- 4 Types of Ecosystem Governance Decisions -- 5 Common Blockchain Governance Strategies -- 5.1 Founder Led/Benevolent Dictator -- 5.2 Core Development Team -- 5.3 Federations or Alliances -- 6 Ecosystem Roles. 7 Typical Ecosystem for Life Sciences Blockchain -- 7.1 Special Considerations for Life Sciences -- 8 Recommendations -- 9 Future Directions -- 10 Conclusions -- References -- Life Sciences Intellectual Property Through the Blockchain Lens -- 1 Introduction -- 2 The Emergence of Blockchain in Life Sciences -- 3 The Intersection of Blockchain and Life Sciences IP Rights in the United States -- 3.1 Patents -- 3.2 Trademarks and Trade Dress -- 3.3 Trade Secrets -- 3.4 Copyrights -- 4 Transferring IP Rights Through Blockchains -- 5 Managing IP Rights Through Blockchain -- 6 Blockchain in Adversarial Proceedings Involving IP Rights -- 6.1 Anticounterfeiting -- 7 Future Directions -- 8 Conclusions -- References -- Regulatory Compliance Considerations for Blockchain in Life Sciences Research -- 1 Introduction -- 1.1 Regulatory Agency Uses of Blockchain -- 1.2 Regulatory Applicability -- 2 Regulatory Review and Documentation -- 2.1 System Design and Documentation -- 2.2 System Protection Features -- 2.3 Record and Signature Integrity -- 2.4 Verification and Validation -- 2.5 Training -- 3 Outsourcing -- 4 Future Directions -- 4.1 Standards -- 4.2 Blockchain Education -- 4.3 Research -- 5 Conclusions -- 5.1 Key Terminology and Definitions -- References -- The Art of Ethics in Blockchain for Life Sciences -- 1 Introduction -- 2 Digital Ethics Programs Design for Blockchain in Life Sciences -- 2.1 General Application of Digital Ethics Across the Life Sciences Continuum -- 2.2 Research -- 2.3 Genomics and Precision Medicine -- 2.4 Digital Identity -- 3 Cultural, Legal, and Socioeconomic Influences -- 4 Blockchain Ethics and Purpose in Life Sciences -- 5 Future Directions: Disruption, Innovation, Evolution -- 6 Conclusions -- References -- Cybersecurity Considerations in Blockchain-Based Solutions -- 1 Introduction -- 2 Blockchain Solution Architecture. 2.1 Network and Architecture Types. |
| Record Nr. | UNISA-996485665203316 |
|
Charles Wendy
|
||
| Singapore : , : Springer, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Blockchain in life sciences / / Wendy Charles
| Blockchain in life sciences / / Wendy Charles |
| Autore | Charles Wendy |
| Pubbl/distr/stampa | Singapore : , : Springer, , [2022] |
| Descrizione fisica | 1 online resource (349 pages) |
| Disciplina | 005.74 |
| Collana | Blockchain Technologies |
| Soggetto topico |
Blockchains (Databases)
Life sciences - Data processing |
| ISBN |
9789811929762
9789811929755 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Foreword -- Preface -- Acknowledgments -- Contents -- About the Editor -- Abbreviations -- List of Figures -- List of Tables -- Blockchain Uses and Real World Evidence -- Introduction to Blockchain -- 1 Introduction -- 2 Blockchain Core Characteristics -- 2.1 Ledgers -- 2.2 Cryptography -- 2.3 Immutability (Tamper Evidence and Tamper Resistance) -- 2.4 Distribution -- 3 Blockchain Features -- 3.1 Permissionless Versus Permissioned -- 3.2 Permissionless -- 3.3 Off-Chain Versus On-Chain Storage -- 3.4 Smart Contracts -- 4 Blockchain Benefits for Life Sciences -- 4.1 Trust -- 4.2 Audit Trails-Provenance -- 4.3 Data Transparency Versus Privacy -- 4.4 Security -- 4.5 Performance -- 5 Conclusions -- 6 Key Terminology and Definitions -- References -- Blockchain in Pharmaceutical Research and the Pharmaceutical Value Chain -- 1 Brief Overview of Pharmaceutical Research -- 1.1 Drug Delivery and Discovery -- 1.2 Challenges Associated with Drug Delivery and Discovery -- 1.3 Challenges Associated with Preclinical (i.e., In Vitro, In Vivo) and Phase 0/I-IV Studies -- 2 Introduction of the End-To-End Pharmaceutical Value Chain -- 2.1 Five Main Categories: (1) Research and Discovery, (2) Clinical Development, (3) Manufacturing and Supply Chain, (4) Launch and Commercial Considerations, and (5) Monitoring and Health Records -- 2.2 Differentiating Pharmaceutical Value Chain from Pharmaceutical Supply Chain -- 3 Blockchain Efforts Within Pharmaceutical Industry -- 3.1 Pharmaceutical Users Software Exchange (PhUSE) Blockchain Project -- 3.2 Innovative Medicines Initiative (IMI) Blockchain-Enabled Healthcare -- 3.3 The MELLODDY Project and Millions of Molecules Blockchain + Smart Contracts for Human Participant Regulations and Consent Management -- 3.4 Information Exchange and Data Transformation (INFORMED) Initiative -- 3.5 Moneyball Medicine.
4 Mapping Blockchain Characteristics to Pain Points in the Pharmaceutical Value Chain -- 4.1 Adapted Fit-For-Purpose Framework and Design Elements -- 4.2 Matching Characteristics (e.g., Decentralized, Distributed, Conditionally Immutable, Scalable, Cryptographically Secured) to Identified Pain Points in Each of the 5 Categories -- 5 Blockchain-But Not in a Vacuum -- 5.1 Blockchain-Complementary Established and Emerging (e.g., Machine Learning, Artificial Intelligence) Technologies for the Pharmaceutical Value Chain -- 6 Debunking Myths Around Challenges with Blockchain -- 6.1 The Myth of the Technical Challenge -- 6.2 The Reality of Challenges Tied to Change Management, Resource Allocation, Paradigm Shift, and Reaching Consensus -- 7 Blockchain and The Idea Pipeline -- 7.1 Pharmacogenomics -- 7.2 Collaborative Pharmaceutical Development -- 7.3 Patient Access, Medication Reclamation, and Prescription Waste Reduction -- 7.4 The Evolution of the Traditional Retail Pharmacy -- 8 Future Directions -- 9 Conclusions -- References -- Blockchain-Based Scalable Network for Bioinformatics and Internet of Medical Things (IoMT) -- 1 Introduction -- 1.1 Data Ownership -- 1.2 Data in Blockchain-Based Network -- 2 Case Implementation of Internet of Medical Things (IoMT) with Real Ownership -- 2.1 The Synsal Network -- 2.2 Sensors, Device Engineering, and Scaling in the Synsal Network -- 3 Tokenization and Value Scaling in the Blockchain-Based Network of Hardware Devices -- 3.1 Tokenization and Value Scaling -- 3.2 Basic Stabilization Tokenomics -- 4 Future Directions -- 5 Conclusions -- References -- Blockchains and Genomics: Promises and Limits of Technology -- 1 Introduction: A Brief History of Capitalization on Genes -- 2 The Scientific and Market Value of Genomic Data -- 2.1 On the Nature of Data, and the Data of Nature -- 2.2 Fair and Sustainable Data Use. 3 Democratize, Decentralize, and Disintermediate Data (The Three Ds) -- 3.1 Blockchain Genomics: The Current Slate -- 4 Why Genomes Cannot Be Owned -- 5 How Shall We Treat Genes? -- 6 What About Non-Fungible Tokens, NFTs? -- 6.1 The Need for Regulation -- 7 Future Directions -- 8 Conclusions -- References -- Convergence of Blockchain and AI for IoT in Connected Life Sciences -- 1 Introduction -- 1.1 Fueling the Digital Transformation in Health and Life Sciences -- 1.2 Technology Unification -- 2 Harnessing the Power of Data-Driven Technologies in Life Sciences -- 2.1 Data-Driven Technologies in Life Sciences -- 3 Innovating in a Highly Regulated Industry -- 4 Essential Elements for Data Strategy in Life Sciences -- 4.1 Data Building Blocks -- 5 Prioritizing Risk Management in Life Sciences -- 6 Opportunities and Challenges for Emerging Digital Technologies in Life Sciences -- 6.1 Major Milestones in Life Sciences Product Development -- 7 Strategic Planning Frameworks -- 7.1 Blockchain and AI to Mitigate Risks of IoT/BYOD -- 7.2 Blockchain-AI Platforms and Infrastructures -- 8 Future Directions -- 8.1 Human as a Platform -- 8.2 Thinking Beyond the Adoption of Technology -- 9 Conclusions -- References -- A Blockchain-Empowered Federated Learning System and the Promising Use in Drug Discovery -- 1 Introduction -- 2 Overview of Federated Learning and Blockchain -- 2.1 Federated Learning -- 2.2 Barriers and Challenges in Drug Discovery -- 2.3 Challenges in Federated Learning -- 2.4 Blockchain Benefit for Federated Learning -- 2.5 The Benefits of Blockchain-Empowered Federated Learning for Drug Discovery -- 3 The Rahasak-ML Platform -- 3.1 Overview -- 3.2 Key Components -- 4 Rahasak-ML Federated Learning Process -- 4.1 Overview -- 4.2 Incremental Training Flow -- 4.3 Finalizing Model. 4.4 The Use Case of Blockchain-Empowered Federated Learning in the Medical Field -- 5 Future Directions -- 5.1 Data Heterogeneity -- 5.2 Efficiency and Effectiveness -- 5.3 Model Interpretation -- 6 Conclusions -- References -- Considerations for Ensuring Success of Blockchain in Life Sciences Research -- Valuing Research Data: Blockchain-Based Management Methods -- 1 Introduction -- 1.1 Nature of Health Data -- 1.2 Health Data Management -- 2 Data as an Asset -- 2.1 How to Value Data Assets -- 3 Data Sales Methods -- 3.1 Data Brokers -- 3.2 Centralized Data Marketplaces -- 3.3 Decentralized Data Marketplaces -- 3.4 Non-Fungible Tokens -- 4 Considerations -- 4.1 Ethical Considerations -- 4.2 Ownership -- 4.3 Data Considerations -- 5 Recommendations -- 5.1 Ethical Recommendations -- 5.2 Data Recommendations -- 5.3 Legal Recommendations -- 6 Future Directions -- 6.1 Regulations -- 6.2 Future Research -- 7 Conclusions -- 7.1 Key Terminology and Definitions -- References -- Blockchain Adoption in Life Sciences Organizations: Socio-organizational Barriers and Adoption Strategies -- 1 Introduction -- 2 Background Literature -- 3 Research Methods -- 4 Findings -- 4.1 The State of the Blockchain + Life Sciences Ecosystem -- 4.2 Socio-organizational Barriers for Blockchain Adoption in Life Sciences -- 4.3 Barrier 4: The Lack of an "Ecosystem" Mindset -- 4.4 Adoption Strategies -- 5 Discussion -- 5.1 Limitations and Future Directions -- 6 Conclusion -- References -- Blockchain Governance Strategies -- 1 Introduction -- 2 Defining Governance -- 3 A Deeper Dive: Blockchain Governance -- 3.1 On-Chain Governance -- 3.2 Off-Chain Governance -- 4 Types of Ecosystem Governance Decisions -- 5 Common Blockchain Governance Strategies -- 5.1 Founder Led/Benevolent Dictator -- 5.2 Core Development Team -- 5.3 Federations or Alliances -- 6 Ecosystem Roles. 7 Typical Ecosystem for Life Sciences Blockchain -- 7.1 Special Considerations for Life Sciences -- 8 Recommendations -- 9 Future Directions -- 10 Conclusions -- References -- Life Sciences Intellectual Property Through the Blockchain Lens -- 1 Introduction -- 2 The Emergence of Blockchain in Life Sciences -- 3 The Intersection of Blockchain and Life Sciences IP Rights in the United States -- 3.1 Patents -- 3.2 Trademarks and Trade Dress -- 3.3 Trade Secrets -- 3.4 Copyrights -- 4 Transferring IP Rights Through Blockchains -- 5 Managing IP Rights Through Blockchain -- 6 Blockchain in Adversarial Proceedings Involving IP Rights -- 6.1 Anticounterfeiting -- 7 Future Directions -- 8 Conclusions -- References -- Regulatory Compliance Considerations for Blockchain in Life Sciences Research -- 1 Introduction -- 1.1 Regulatory Agency Uses of Blockchain -- 1.2 Regulatory Applicability -- 2 Regulatory Review and Documentation -- 2.1 System Design and Documentation -- 2.2 System Protection Features -- 2.3 Record and Signature Integrity -- 2.4 Verification and Validation -- 2.5 Training -- 3 Outsourcing -- 4 Future Directions -- 4.1 Standards -- 4.2 Blockchain Education -- 4.3 Research -- 5 Conclusions -- 5.1 Key Terminology and Definitions -- References -- The Art of Ethics in Blockchain for Life Sciences -- 1 Introduction -- 2 Digital Ethics Programs Design for Blockchain in Life Sciences -- 2.1 General Application of Digital Ethics Across the Life Sciences Continuum -- 2.2 Research -- 2.3 Genomics and Precision Medicine -- 2.4 Digital Identity -- 3 Cultural, Legal, and Socioeconomic Influences -- 4 Blockchain Ethics and Purpose in Life Sciences -- 5 Future Directions: Disruption, Innovation, Evolution -- 6 Conclusions -- References -- Cybersecurity Considerations in Blockchain-Based Solutions -- 1 Introduction -- 2 Blockchain Solution Architecture. 2.1 Network and Architecture Types. |
| Record Nr. | UNINA-9910590074503321 |
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Charles Wendy
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| Singapore : , : Springer, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Computational life sciences : first international symposium, CompLife 2005, Konstanz, Germany, September 25-27, 2005 : proceedings / / Michael R. Berthold ... [et al.] (eds.)
| Computational life sciences : first international symposium, CompLife 2005, Konstanz, Germany, September 25-27, 2005 : proceedings / / Michael R. Berthold ... [et al.] (eds.) |
| Edizione | [1st ed. 2005.] |
| Pubbl/distr/stampa | Berlin ; ; New York, : Springer, c2005 |
| Descrizione fisica | 1 online resource (XII, 280 p.) |
| Disciplina | 572.80285 |
| Altri autori (Persone) | BertholdM (Michael) |
| Collana | Lecture notes in computer science,Lecture notes in bioinformatics |
| Soggetto topico |
Life sciences - Data processing
Bioinformatics Computational biology Molecular biology |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Systems Biology -- Structural Protein Interactions Predict Kinase-Inhibitor Interactions in Upregulated Pancreas Tumour Genes Expression Data -- Biochemical Pathway Analysis via Signature Mining -- Recurrent Neuro-fuzzy Network Models for Reverse Engineering Gene Regulatory Interactions -- Data Analysis and Integration -- Some Applications of Dummy Point Scatterers for Phasing in Macromolecular X-Ray Crystallography -- BioRegistry: A Structured Metadata Repository for Bioinformatic Databases -- Robust Perron Cluster Analysis for Various Applications in Computational Life Science -- Structural Biology -- Multiple Alignment of Protein Structures in Three Dimensions -- Protein Annotation by Secondary Structure Based Alignments (PASSTA) -- MAPPIS: Multiple 3D Alignment of Protein-Protein Interfaces -- Genomics -- Frequent Itemsets for Genomic Profiling -- Gene Selection Through Sensitivity Analysis of Support Vector Machines -- The Breakpoint Graph in Ciliates -- Computational Proteomics -- ProSpect: An R Package for Analyzing SELDI Measurements Identifying Protein Biomarkers -- Algorithms for the Automated Absolute Quantification of Diagnostic Markers in Complex Proteomics Samples -- Detection of Protein Assemblies in Crystals -- Molecular Informatics -- Molecular Similarity Searching Using COSMO Screening Charges (COSMO/3PP) -- Increasing Diversity in In-silico Screening with Target Flexibility -- Multiple Semi-flexible 3D Superposition of Drug-Sized Molecules -- Molecular Structure Determination and Simulation -- Efficiency Considerations in Solving Smoluchowski Equations for Rough Potentials -- Fast and Accurate Structural RNA Alignment by Progressive Lagrangian Optimization -- Visual Analysis of Molecular Conformations by Means of a Dynamic Density Mixture Model -- Distributed Data Mining -- Distributed BLAST in a Grid Computing Context -- Parallel Tuning of Support Vector Machine Learning Parameters for Large and Unbalanced Data Sets -- The Architecture of a Proteomic Network in the Yeast. |
| Altri titoli varianti | CompLife 2005 |
| Record Nr. | UNINA-9910484709503321 |
| Berlin ; ; New York, : Springer, c2005 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Computational life sciences II : Second International Symposium, CompLife 2006, Cambridge, UK, September 27-29, 2006 : proceedings / / Michael R. Berthold, Robert Glen, Ingrid Fischer (eds.)
| Computational life sciences II : Second International Symposium, CompLife 2006, Cambridge, UK, September 27-29, 2006 : proceedings / / Michael R. Berthold, Robert Glen, Ingrid Fischer (eds.) |
| Edizione | [1st ed. 2006.] |
| Pubbl/distr/stampa | Berlin, : Springer, 2006 |
| Descrizione fisica | 1 online resource (XIII, 269 p.) |
| Disciplina | 572.80285 |
| Altri autori (Persone) |
BertholdM (Michael)
GlenRobert (Robert Charles) FischerIngrid |
| Collana |
LNCS sublibrary. SL 8, Bioinformatics
Lecture notes in computer science,Lecture notes in bioinformatics |
| Soggetto topico |
Life sciences - Data processing
Bioinformatics Computational biology Molecular biology |
| ISBN | 3-540-45768-2 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Genomics -- Improved Robustness in Time Series Analysis of Gene Expression Data by Polynomial Model Based Clustering -- A Hybrid Grid and Its Application to Orthologous Groups Clustering -- Promoter Prediction Using Physico-Chemical Properties of DNA -- Parametric Spectral Analysis of Malaria Gene Expression Time Series Data -- An Efficient Algorithm for Finding Long Conserved Regions Between Genes -- The Reversal Median Problem, Common Intervals, and Mitochondrial Gene Orders -- Data Mining -- Building Structure-Property Predictive Models Using Data Assimilation -- Set-Oriented Dimension Reduction: Localizing Principal Component Analysis Via Hidden Markov Models -- Relational Subgroup Discovery for Descriptive Analysis of Microarray Data -- Applicability of Loop Recombination in Ciliates Using the Breakpoint Graph -- High-Throughput Identification of Chemistry in Life Science Texts -- Beating the Noise: New Statistical Methods for Detecting Signals in MALDI-TOF Spectra Below Noise Level -- Molecular Simulation -- Dynamic Complexity of Chaotic Transitions in High-Dimensional Classical Dynamics: Leu-Enkephalin Folding -- Solvent Effects and Conformational Stability of a Tripeptide -- Grid Assisted Ensemble Molecular Dynamics Simulations of HIV-1 Proteases Reveal Novel Conformations of the Inhibitor Saquinavir -- Molecular Informatics -- A Structure-Based Analysis of Single Molecule Force Spectroscopy (SMFS) Data for Bacteriorhodopsin and Four Mutants -- Classifying the World Anti-Doping Agency’s 2005 Prohibited List Using the Chemistry Development Kit Fingerprint -- A Point-Matching Based Algorithm for 3D Surface Alignment of Drug-Sized Molecules -- Systems Biology -- Adaptive Approach for Modelling Variability in Pharmacokinetics -- A New Approach to Flux Coupling Analysis of Metabolic Networks -- Biological Networks / Metabolism -- Software Supported Modelling in Pharmacokinetics -- On the Interpretation of High Throughput MS Based Metabolomics Fingerprints with Random Forest -- Construction of Correlation Networks with Explicit Time-Slices Using Time-Lagged, Variable Interval Standard and Partial Correlation Coefficients -- Computational Neuroscience -- The Language of Cortical Dynamics -- A Simple Method to Simultaneously Track the Numbers of Expressed Channel Proteins in a Neuron. |
| Altri titoli varianti |
CompLife 2006
Computational life sciences 2 Computational life sciences two |
| Record Nr. | UNINA-9910767506003321 |
| Berlin, : Springer, 2006 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Computational science and techniques
| Computational science and techniques |
| Pubbl/distr/stampa | Klaipeda, Lithuania : , : Klaipeda University, , 2013- |
| Descrizione fisica | 1 online resource |
| Soggetto topico |
Computer science
Computational complexity Mathematical models Life sciences - Data processing |
| Soggetto genere / forma | Periodicals. |
| Soggetto non controllato | Computer Science |
| ISSN | 2029-9966 |
| Formato | Materiale a stampa |
| Livello bibliografico | Periodico |
| Lingua di pubblicazione | eng |
| Record Nr. | UNISA-996321072303316 |
| Klaipeda, Lithuania : , : Klaipeda University, , 2013- | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Computational science and techniques
| Computational science and techniques |
| Pubbl/distr/stampa | Klaipeda, Lithuania : , : Klaipeda University, , 2013- |
| Descrizione fisica | 1 online resource |
| Soggetto topico |
Computer science
Computational complexity Mathematical models Life sciences - Data processing |
| Soggetto genere / forma | Periodicals. |
| ISSN | 2029-9966 |
| Formato | Materiale a stampa |
| Livello bibliografico | Periodico |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910132346603321 |
| Klaipeda, Lithuania : , : Klaipeda University, , 2013- | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Computer applications in the biosciences : CABIOS
| Computer applications in the biosciences : CABIOS |
| Pubbl/distr/stampa | Oxford, : Oxford University Press |
| Soggetto topico |
Life sciences - Data processing
Life sciences - Computer programs Biology Computers |
| Soggetto genere / forma |
Periodical
Periodicals. |
| ISSN | 1460-2059 |
| Formato | Materiale a stampa |
| Livello bibliografico | Periodico |
| Lingua di pubblicazione | eng |
| Altri titoli varianti | CABIOS |
| Record Nr. | UNISA-996221339803316 |
| Oxford, : Oxford University Press | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
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