This training program is the best option you can find to specialize in Genomic and Precision Medicine in Hematology: Thrombosis and establish more precise diagnoses”

Early detection of venous thrombosis is essential to treat the disease and reduce sequelae in patients. There are also preventive measures, such as physical or pharmacological ones. 

During this Professional master’s degree, students will focus on Genomic and Precision Medicine applied to the treatment of venous thrombosis. The program has been designed by specialists in the field, so students will receive a complete and specific training from experts in the matter. 

Thus, the aim is to establish the bases of genomic and precision medicine in the field, starting from the knowledge of hemostasis and venous thromboembolism, and addressing the key aspects of diagnosis, treatment and prevention. Professionals will also learn about special situations they may encounter in their daily practice, such as thrombosis in oncology patients or in women. 

After these more general aspects, the Professional master’s degree will fully delve into the field of genomics as applied to venous thrombosis, where students will learn about the main studies in the field that will allow them to offer more effective and accurate treatments to their patients suffering from this pathology. 

Therefore, after completing and passing the Professional master’s degree, students will have acquired the theoretical knowledge necessary to carry out an effective treatment of venous thrombosis in the main areas of action of the professional.

Don’t miss the opportunity to study this Professional master’s degree in Genomic and Precision Medicine in Hematology: Thrombosis at TECH. It's the perfect opportunity to advance your career”

This Professional master’s degree in Genomic and Precision Medicine in Hematology: Thrombosis contains the most complete and up-to-date scientific program on the market. Its most notable features are:

• Case studies presented by experts in Genomic and Precision Medicine in Hematology
• The graphic, schematic, and practical contents with which they are created, provide scientific and practical information on the disciplines that are essential for professional development
• The latest news on Genomic and Precision Medicine in Hematology
• Practical exercises where self-assessment can be used to improve learning
• Special emphasis on innovative methodologies in Genomics and Precision Medicine in Hematology
• Theoretical lessons, questions to the expert, debate forums on controversial topics, and individual reflection assignments
• Content that is accessible from any fixed or portable device with an Internet connection

This Professional master’s degree may be the best investment you can make when selecting a refresher program for two reasons: in addition to updating your knowledge of Genomic and Precision Medicine in Hematology: Thrombosis, you will obtain a qualification from TECH Global University”

The teaching staff is made up of professionals who belong to the field of Genomic and Precision Medicine in Hematology: Thrombosis, who bring to this program the experience of their work, as well as recognized specialists from reference societies and prestigious universities. 

The multimedia content, developed with the latest educational technology, will provide the professional with situated and contextual learning, i.e., a simulated environment that will provide immersive specialization programmed to learn in real situations. 

This program is designed around Problem-Based Learning, whereby the specialist must try to solve the different professional practice situations that arise throughout the program. To do so, the professional will be assisted by an innovative, interactive video system created by renowned and extensively experienced experts in Genomic and Precision Medicine in Hematology: Thrombosis. 

This program comes with the best educational material, providing you with a contextual approach that will facilitate your learning"

This 100% online Professional master’s degree will allow you to combine your studies with your professional work while increasing your knowledge in this field"

The contents have been structured and designed by the best professionals in the field of Genomic and Precision Medicine in Hematology: Thrombosis, who are extensively experienced and recognized prestige in the profession, backed by the volume of cases reviewed, studied and diagnosed, and with extensive knowledge of new technologies applied to genomic and precision medicine.    

This Professional master’s degree in Genomic and Precision Medicine in Hematology: Thrombosis contains the most complete and up-to-date scientific program on the market” 

Module 1. Introduction to Hemostasis

1.1. Introduction: History and Evolution

1.1.1. History
1.1.2. Evolution

1.2. Endothelium and Platelets in the Physiology of Hemostasis

1.2.1. The Role of Endothelium in Hemostasis
1.2.2. Platelets: Platelet Membrane Receptors
1.2.3. Platelet Plug Formation: Platelet Adhesion and Aggregation
1.2.4. Microparticles
1.2.5. Involvement of Other Cellular Elements in the Physiology of Hemostasis

1.3. Plasma Component of Coagulation: Fibrin Clots

1.3.1. Coagulation Cascade
1.3.2. Coagulation Factors
1.3.3. Coagulation System
1.3.4. Multicomponent Complexes

1.4. Coagulation Regulatory Mechanisms

1.4.1. Inhibitors of Activated Factors
1.4.2. Regulators of Cofactors

1.5. Fibrinolysis

1.5.1. Fibrinolytic System
1.5.2. Fibrinolysis Activation
1.5.3. Fibrinolysis Regulation
1.5.4. Cellular Receptors in Fibrinolysis

1.6. The Coagulation Laboratory: Pre-Analytical Phase

1.6.1. Patients and Sample Extraction
1.6.2. Sample Transportation and Processing

1.7. Platelet Study

1.7.1. Methods to Measure Platelet Function
1.7.2. Closure Time (PFA-100)
1.7.3. Flow Cytometry

1.8. Exploring Coagulation Plasma Phase

1.8.1. Classical Coagulation Techniques
1.8.2. Coagulation Factor Quantification
1.8.3. Study of Specific and Non-Specific Inhibitors
1.8.4. Fibrinolysis Laboratory Tests
1.8.5. Thrombophilia Study
1.8.6. Laboratory Tests to Monitor Anticoagulant Medication

1.9. Techniques for the Global Analysis of Hemostasis

1.9.1. Definition and Classification
1.9.2. Thrombin Generation Test
1.9.3. Viscoelastometric Techniques

1.10. Clinical Cases and Exercises

1.10.1. Clinical Cases 
1.10.2. Exercises

Module 2. Pathophysiology and Epidemiology in Venous Thromboembolism

2.1. General Introduction to the Complexity and Clinical Impact of VTE

2.1.1. General Introduction to Complexity
2.1.2. Clinical Impact of VTE

2.2. Generation of a Pathological Thrombus

2.2.1. Hemostasis Balance
2.2.2. Break in Balance (Classic Virchow's Triad) and Consequences
2.2.3. Normal and Pathological Venous Function
2.2.4. The Role of Venous Valve in Pathological Thrombi
2.2.5. The Role of the Vascular Endothelium
2.2.6. The Role of Platelets and Polyphosphates
2.2.7. The Role of Neutrophil Extracellular Traps (NETs)
2.2.8. The Role of Circulating Microparticles
2.2.9. Local inflammatory processes
2.2.10. Paraneoplastic Thrombosis (see Module 4)
2.2.11. Mechanism and Site in Thrombus Formation

2.3. Classification and Characteristics of VTE according to Anatomical Site

2.3.1. Lower Limbs
2.3.2. Upper Limbs
2.3.3. Pulmonary Thromboembolism
2.3.4. Atypical Sites

 2.3.4.1. Visceral
 2.3.4.2. Intracranial

2.4. Classification of Thrombosis according to Associated Circumstances

2.4.1. Spontaneous VTE vs. Secondary
2.4.2. Environmental Risk Factors (Table a)
2.4.3. The Role of Race, Age, and Sex
2.4.4. The Role of Intravascular Devices (Intravenous Catheters)

2.5. VTE Sequalae

2.5.1. Post-Thrombotic Syndrome and Residual Thrombosis: Relation to Recurrence
2.5.2. Chronic Pulmonary Hypertension
2.5.3. Short- and Long-Term Mortality
2.5.4. On Quality of Life

2.6. Impact of VTE on the Global Burden of Disease 

2.6.1. Contribution to the Global Burden of Disease
2.6.2. Impact on the Economy

2.7. VTE Epidemiology

2.7.1. Influencing Variables (Age, Race, Comorbidities, Medication, Seasonal Factors, etc)

2.8. Risk and Epidemiology of Thrombotic Recurrence

2.8.1. Differences between the Sexes
2.8.2. Differences according to the Circumstances associated with the First Episode

2.9. Thrombophilia

2.9.1. Classical Conception
2.9.2. Biological Biomarkers of Thrombophilia

 2.9.2.1. Genetic Biomarkers
 2.9.2.2. Plasmatic Biomarkers
 2.9.2.3. Cell Biomarkers

2.9.3. Thrombophilia Laboratory Study

 2.9.3.1. Debate on its Utility
 2.9.3.2. Classical Abnormalities
 2.9.3.3. Other Biomarkers or Intermediary Phenotypes (Table b)

2.10. Thrombophilia as a Complex and Chronic Pathology Concept

2.10.1. High Complexity (see 2.1)
2.10.2. Importance of the Genetic basis: Concept of Heritability
2.10.3. Known Genetic Risk Factors (Table c): Connection to Modules 7 and 8
2.10.4. Heritability to Be Discovered

2.11. Individual Risk Profile

2.11.1. Concept
2.11.2. Permanent Components (Genetic)
2.11.3. Changing Circumstances
2.11.4. New and Powerful Mathematical Models to Jointly Assess All Risk Variables (see Module 9)

Module 3. Diagnosis, Treatment and Prophylaxis in Venous Thromboembolism

3.1. VTE Diagnosis: 

3.1.1. Clinical Presentation and Diagnostic Probability Scales
3.1.2. Complementary Tests (D-Dimer, Imaging)
3.1.3. Prognostic Risk Stratification of Patients with Parkinson's Disease

3.2. VTE Treatment

3.2.1. Antithrombotic Medication
3.2.2. Treating the Initial Phase (Acute Phase and up to 3-6 Months)
3.2.3. Length of Treatment and Long-Term Treatment (> 6 Months)
3.2.4. Complications in Antithrombotic Treatment

3.3. VTE Prophylaxis: 

3.3.1. Medical Patient Prophylaxis
3.3.2. Surgical Patient Prophylaxis
3.3.3. Clinical Cases

Module 4. Special Situations I: Thrombosis in Oncology

4.1. Epidemiology and Risk Factors

4.1.1. Epidemiology
4.1.2. Patient-Related Risk Factors
4.1.3. Tumor-Related Risk Factors
4.1.4. Treatment-Related Risk Factors

4.2. Thromboprophylaxis in Admitted Medical Oncology Patients

4.2.1. Introduction
4.2.2. Thromboprophylaxis in Admitted Medical Oncology Patients

4.3. Surgical Patient Prophylaxis

4.3.1. Introduction
4.3.2. Surgical Patient Prophylaxis

4.4. Thromboprophylaxis in Oncology Patients Receiving Systemic Therapy in an Outpatient Setting

4.4.1. Introduction
4.4.2. Thromboprophylaxis in Oncology Patients Receiving Systemic Therapy in an Outpatient Setting

4.5. Predictive Risk Models for Thrombosis

4.5.1. Khorana Score
4.5.2. Others Predictive Risk Models
4.5.3. Other Potential Applications of Predictive Risk Models

4.6. Initial Treatment of Cancer-Related Thrombosis

4.6.1. Introduction
4.6.2. Initial Treatment of Cancer-Related Thrombosis

4.7. Long-Term Treatment of Cancer-Related Thrombosis

4.7.1. Introduction
4.7.2. Long Term Treatment of Cancer-Related Thrombosis

4.8. Predictive Models for Bleeding and Recurrence: Interactions of Direct Acting Oral Anticoagulants

4.8.1. Predictive Models for Bleeding and Recurrence
4.8.2. Interactions of Direct Acting Oral Anticoagulants

4.9. Antitumor Therapy and Risk of Thrombosis

4.9.1. Chemotherapy
4.9.2. Hormone Therapy
4.9.3. Biological Medication
4.9.4. Immunotherapy
4.9.5. Supportive therapy

Module 5. Special Situations II: Thrombosis in Women

5.1. Hemostasis Pathophysiology in the Different Development Stages of Women

5.1.1. Introduction
5.1.2. Physiological Risk Factors
5.1.3. Acquired Risk Factors

5.2. Thrombophilia and Women

5.2.1. Hereditary Thrombophilia
5.2.2. Acquired Thrombophilia
5.2.3. Study Indications

5.3. Contraception and Hormone Therapy and Venous Thromboembolism

5.3.1. Introduction
5.3.2. Contraception in Women with Thrombotic Risk Factors
5.3.3. Contraception in Women after a Thrombotic Event

5.4. Prevention Strategies for Venous Thromboembolism in Non-Pregnant Women in Childbearing Age

5.4.1. Non-Pregnant Women without a History of Thrombosis
5.4.2. Non-Pregnant Woman with a History of Thrombosis

5.5. Venous Thromboembolism during Gestation and Puerperium

5.5.1. Incidence and Epidemiology
5.5.2. Risk Factors: Risk Assessment Scales
5.5.3. Clinical Presentation
5.5.4. Diagnostic Strategy
5.5.5. Treatment
5.5.6. Prophylaxis
5.5.7. Managing Patients with Heart Valves

5.6. Venous Thromboembolism and Cesarean Section

5.6.1. Incidence and Epidemiology
5.6.2. Risk Factors: Risk Assessment Scales
5.6.3. Treatment and Prophylaxis

5.7. Assisted Reproductive Techniques and Venous Thromboembolism

5.7.1. Incidence and Risk Factors
5.7.2. Clinical Presentation
5.7.3. Treatment
5.7.4. Prophylaxis

5.8. Anticoagulant Medication used during Pregnancy, Postpartum and Lactation

5.8.1. Unfractionated Heparin
5.8.2. Low Molecular Weight Heparin
5.8.3. Vitamin K Antagonists
5.8.4. Peripartum Anticoagulant Therapy Management
5.8.5. Complications Arising from Anticoagulant Therapy

5.9. Obstetric Antiphospholipid Syndrome

5.9.1. Incidence and Epidemiology
5.9.2. Laboratory Diagnosis of Obstetric APS
5.9.3. Treatment of Obstetric APS
5.9.4. Approach to Women in Childbearing Age with Isolated Antiphospholipid Antibodies

5.10. Climacteric Age, Menopause and Thrombosis

5.10.1. Incidence and Epidemiology
5.10.2. Cardiovascular Risk
5.10.3. Hormone Replacement Therapy

Module 6. Omic Data: Introduction to the Programming Language R 

6.1. Basic Introduction to the UNIX/ Linux Operating System

6.1.1. History and Philosophy
6.1.2. Command Interpreter (Shell)
6.1.3. Basic Linux Commands
6.1.4. Word Processors

6.2. File Management in UNIX/Linux

6.2.1. File System
6.2.2. Users and Groups
6.2.3. Licences

6.3. System Management in UNIX/Linux

6.3.1. Tasks (Jobs)
6.3.2. Register (Logs)
6.3.3. Monitoring Tools
6.3.4. Networks

6.4. Introduction and Basic Features of R

6.4.1. What is R? 
6.4.2. First Steps

 6.4.2.1. Installation and Graphic Interface 
 6.4.2.2. Workspace 

6.4.3. Extension in R 

 6.4.3.1. Standard Packages
 6.4.3.2. Contributed Packages, CRAN and Bioconductor

6.5. Types of Data in R

6.5.1. Vectors
6.5.2. Lists
6.5.3. Arrays and Matrices
6.5.4. Factors
6.5.5. Data Frames
6.5.6. Text Strings 
6.5.7. Other Types of Data

6.6. Data Management in R

6.6.1. Import and Export Data
6.6.2. Data Manipulation

 6.6.2.1. Vectors
 6.6.2.2. Matrices
 6.6.2.3. Text Strings 
 6.6.2.4. Data Sheets

6.7. Control Functions and Loops in R

6.7.1. Conditional Execution: if
6.7.2. Cycles: For, Repeat, While
6.7.3. Apply Functions

6.8. Statistical Models in R

6.8.1. Univariate Data
6.8.2. Multivariate Data
6.8.3. Hypothesis Test

6.9. Graphic Representation in R

6.9.1. Basic Representations
6.9.2. Graphical Parameters and Elements
6.9.3. The ggplot2 Package

6.10. Defining Functions in R

6.10.1. Simple Examples
6.10.2. Default Arguments and Values 
6.10.3. Assignments within Functions 

Module 7. Thrombosis in the Genomic Era I: Genome-Wide Association Studies (GWAS)

7.1. Introduction to Genetics

7.1.1. Introduction and Basic Concepts

 7.1.1.1. Genes
 7.1.1.2. Polymorphisms, Alleles and Loci
 7.1.1.3. Haplotypes
 7.1.1.4. Concept of Linkage Disequilibrium
 7.1.1.5. Genotype
 7.1.1.6. Phenotype

7.1.2. Genetics to Study Complex Diseases

 7.1.2.1. Complex and Rare Diseases
 7.1.2.2. Study of Candidate Genes vs. Global Genome Studies

7.1.3. Types of Polymorphism, Nomenclature and Genome Versions
7.1.4. Genotyping Chips

7.2. Introduction to Global Genome-Wide Analysis Studies (GWAS)

7.2.1. What Is a GWAS?
7.2.2. GWAS Study Design

 7.2.2.1. Heritability
 7.2.2.2. Case-Control vs. Quantitative Trait Analysis
 7.2.2.3. Sample Size and Statistical Power
 7.2.2.4. Biases by Population Substructure
 7.2.2.5. Phenotypes: Standardization and Outliers

7.2.3. The Genetic Association Test
7.2.4. Useful Software for GWAS

7.3. Genetic Imputation

7.3.1. Concept of Imputation
7.3.2. Reference Panels

 7.3.1.1. Hap Map Project
 7.3.1.2. 1000 Genomes Project
 7.3.1.3. Haplotype Reference Consortium Project
 7.3.1.4. Other Population-Specific Projects

7.4. Quality Control and Filters

7.4.1. Pre-Imputation Filters

 7.4.1.1. Minor Allele Frequency
 7.4.1.2. Hardy-Weinberg Equilibrium
 7.4.1.3. Genotyping Errors (Call Rate)
 7.4.1.4. Excess Heterozygosity
 7.4.1.5. Mendelian Errors
 7.4.1.6. Sex Errors
 7.4.1.7. Chain Direction
 7.4.1.8. Family Relationships

7.4.2. Post-Imputation Filters

 7.4.2.1. Monomorphic Variants, Frequencies
 7.4.2.2. Imputation Quality

7.4.3. Post GWAS Filters
7.4.4. Quality Control Software

7.5. Analyzing and Interpreting GWAS Results

7.5.1. Manhattan Plot
7.5.2. Multiple Testing Correction and Genome-Wide Significant Results
7.5.3. Concept of Genetic Locus

7.6. Meta-Analysis and Replication

7.6.1. Common Workflow in GWAS Studies
7.6.2. Meta-Analysis

 7.6.2.1. Meta-Analysis Methods
 7.6.2.2. Required Information for Meta-Analyses
 7.6.2.3. Meta-Analysis Result
 7.6.2.4. Meta-Analysis Software Examples

7.6.3. The Most Relevant Consortia

7.7. Post GWAS Analysis

7.7.1. Fine-Mapping and Regional Graphic
7.7.2. Conditional Analysis
7.7.3. Selecting the Best Gene Candidate (from Locus to Gene)

 7.7.3.1. Exploiting Information on Expression
 7.7.3.2. Gene Set Enrichment Analyses
 7.7.3.3. Study of the Potential Functional Effect of Polymorphism

7.8. The Era of GWAS

7.8.1. GWAS Data Repositories
7.8.2. Taking Stock of the GWAS Era Results

7.9. Use of GWAS Results

7.9.1. Risk Estimation Models
7.9.2. Mendelian Randomization Studies

7.10. Genetic Analysis of Venous Thromboembolism (VTE)

7.10.1. Some History
7.10.2. The Most Relevant GWAS Studies on VTE
7.10.3. Latest Studies Results
7.10.4. Clinical Implications of Genetic Results: The Importance of Coagulation Cascades and New Metabolic Pathways 
7.10.5. Future Strategies

Module 8. Thrombosis in the Genomic Era II: Massive Sequencing Studies

8.1. Genetic Basis and Molecular Study in Thrombosis and Hemostasis

8.1.1. Molecular Epidemiology in Thrombosis and Hemostasis
8.1.2. Genetic Study of Congenital Diseases
8.1.3. Classical Approach to Molecular Diagnostics
8.1.4. Indirect Diagnosis or Genetic Linkage Techniques
8.1.5. Direct Diagnostic Techniques

 8.1.5.1. Mutation Screening
 8.1.5.2. Direct Mutation Identification

8.2. DNA Sequencing Techniques

8.2.1. Sanger’s Traditional Sequencing

 8.2.1.1. Characteristics of the Technique, Limitations and Application in Thrombosis and Hemostasis

8.2.2. Next-Generation Sequencing (NGS) 

 8.2.2.1. NGS Platforms in Molecular Diagnostics
 8.2.2.2. General Information on the Technology, Possibilities and Limitations of NGS vs. Traditional Sequencing

8.2.3. Third-Generation Sequencing (TGS)

8.3. Different Approaches to Genetic Studies Using NGS

8.3.1. Gene Panel Sequencing
8.3.2. Whole Exome Sequencing and Whole Genome Sequencing
8.3.3. Transcriptomics by RNA-Seq
8.3.4. MicroRNA Sequencing
8.3.5. Mapping Protein-DNA Interactions with ChIP-Seq
8.3.6. Epigenomics Analysis and DNA Methylation Using NGS

8.4. Bioinformatics Analysis of NGS Data

8.4.1. The Challenge of Bioinformatics Analysis of Massive NGS Generated Data
8.4.2. IT Requirements for NGS Data Management and Analysis

 8.4.2.1. NGS Data Storage, Transfer and Sharing
 8.4.2.2. Computing Power Required for NGS Data Analysis
 8.4.2.3. Software Requirements for NGS Data Analysis
 8.4.2.4. Bioinformatics Skills Required for NGS Data Analysis

8.4.3. Base Calling, FASTQ File Format and Base Quality Scoring 
8.4.4. NGS Data Quality Control and Pre-processing 
8.4.5. Read Mapping Bioinformatics Required for NGS Data Analysis 
8.4.6. Variant Calls 
8.4.7. Tertiary Analysis 
8.4.8. Structural Variation Analysis by NGS 
8.4.9. Methods to Estimate Copy Number Variation from NGS Data 

8.5. Concept and Types of Mutation Detectable by NGS 

8.5.1. Molecular Etiology of Thrombotic and Hemorrhagic Disorders 
8.5.2. Mutation Nomenclature 
8.5.3. Functional Implication of Identified Variants/Mutations 
8.5.4. Difference between Mutation and Polymorphism 

8.6. Fundamental Molecular Databases in NGS 

8.6.1. Locus Specific Databases (LSDB) 
8.6.2. Previous Mutation Descriptions in Databases 
8.6.3. Databases of Variants Detected in Healthy Population by NGS 
8.6.4. Molecular Databases with Clinical Annotations 

8.7. Analysis and Interpretation of NGS Results on Thrombosis and Hemostasis

8.7.1. Mutation Validation
8.7.2. Concept of Mutation Pathogenicity
8.7.3. Genotype-Phenotype Correlation

 8.7.3.1. In Silico Studies
 8.7.3.2. Expression Studies
 8.7.3.3. In Vitro Functional Studies

8.8. Role of NGS in Genetic Counseling and Prenatal Diagnosis

8.8.1. Genetic Counseling in the NGS Era
8.8.2. Ethical Issues Specific to NGS and Whole Genome Sequencing for Genetic Counseling and Clinical Diagnostics 
8.8.3. Conventional Prenatal Diagnosis and Methods
8.8.4. Pre-implant Genetic Diagnostic
8.8.5. Non-invasive Prenatal Diagnosis

 8.8.5.1. Use of Fetal DNA in Maternal Circulation for Prenatal Diagnosis
 8.8.5.2. Sequencing of SNPs from Circulating Fetal DNA
 8.8.5.3. Limitations and Challenges of NGS-Based Non-invasive Prenatal Testing
 8.8.5.4. Clinical Implementation of Non-Invasive Prenatal Testing for Aneuploidies

8.9. Future Perspectives in NGS Technologies and Data Analysis

8.9.1. Technological Development of Sequencing in the Mid-Term
8.9.2. Evolution of Bioinformatics Tools for High-Throughput Sequencing Data Analysis
8.9.3. Standardization and Rationalization of NGS Analytical Processes
8.9.4. Parallel Computation
8.9.5. Cloud Computing

Module 9. Thrombosis in the Genomic Era III: Regulation of Gene Expression Studies (RNA and miRNA)

9.1. Introduction to RNA-seq

9.1.1. Technique Description
9.1.2. Advantages over Expression Arrays
9.1.3. Limitations

9.2. Experimental Design for RNA-seq Studies

9.2.1. Concept of Randomization and Blocking
9.2.2. Biological Replicas vs. Technical Replicas
9.2.3. Number of Replicas
9.2.4. Sequencing Depth
9.2.5. Type of Library

9.3. Quality Control for RNA-seq

9.3.1. Quality Metrics for RNA-seq
9.3.2. Programs Designed for RNA-seq Quality Control

9.4. RNA Alignment and Quantification

9.4.1. Reference Genome (Genome-based)
9.4.2. Reference Genome (Transcriptomics-Based)

9.5. De Novo Assembly and RNA Annotation

9.5.1. Pipeline without Reference Transcriptome
9.5.2. Annotation of Coding and Non-Coding Transcripts

9.6. Differential Expression with RNA-seq

9.6.1. Standardization
9.6.2. Latent Variable Elimination
9.6.3. Programs and Statistics Methods
9.6.4. Functional Enrichment

9.7. Other Applications of RNA-seq Technology

9.7.1. Alternative Splicing Detection
9.7.2. Chimera Transcript Detection
9.7.3. Mutation Detection
9.7.4. Allele-specific Expression Detection

9.8. Small RNA-seq

9.8.1. Small RNA-seq Library Building

 9.9.8.1. Quality Control for Small RNA-seq

9.8.2. Alignment and Quantification for Small RNA-seq
9.8.3. miRNA Annotation
9.8.4. miRNA targets

9.9. Gene Co-expression Networks

9.9.1. Concept of Gene Co-expression Networks
9.9.2. Differential Co-expression vs. Differential Expression
9.9.3. Weighted Gene Co-expression Networks Analysis (WGCNA)
9.9.4. Gene Co-expression Networks Visualisation

9.10. Gene Expression Regulation Analysis in Venous Thromboembolism (VTE)

9.10.1. A Bit of History
9.10.2. Relevant Studies on VTE
9.10.3. Latest Studies Results
9.10.4. Clinical Implications in the Results
9.10.5. Practical Examples and Exercises

Module 10. Predictive Models

10.1. Statistical Learning

10.1.1. Estimating f
10.1.2. Supervised and Unsupervised Learning
10.1.3. Regression and Classification Problems
10.1.4. Linear and Non-Linear Models

10.2. Data Pre-Processing

10.2.1. Standardization
10.2.2. Imputability
10.2.3. Atypical Values (Outliers)

10.3. Linear Regression

10.3.1. Linear Models
10.3.2. Variance Analysis (ANOVA)
10.3.3. Mixed Effects Models

10.4. Classification

10.4.1. Logistic Regression
10.4.2. Linear Discriminant Analysis
10.4.3. K Nearest Neighbors (KNN)

10.5. Resampling Methods

10.5.1. Cross Validation

 10.5.1.1. Validation Set or Test
 10.5.1.2. Leave One Out Cross Validation
 10.5.1.3. Cross Validation of k Iterations (k-Fold)

10.5.2. Bootstrap

10.6. Linear Model Selection

10.6.1. Nested Model Comparison
10.6.2. Stepwise Algorithms
10.6.3. Linear Model Diagnosis

10.7. Regularization

10.7.1. The Curse of Dimensions
10.7.2. Principal Component Regression 
10.7.3. Partial Least Squares Regression 
10.7.4. Shrinkage Methods 

 10.7.4.1. Ridge Regression
 10.7.4.2. Lasso

10.8. Methods Based on Decision Trees

10.8.1. Introduction to Decision Trees
10.8.2. Types of Decision Trees

 10.8.2.1. Bagging
 10.8.2.2. Random Forests
 10.8.2.3. Boosting

10.9. Support Vector Machines

10.9.1. Maximum Margin Classifiers
10.9.2. Support Vector Machines
10.9.3. Hyperparameter Tuning

10.10. Unsupervised Learning

10.10.1. Main Component Analysis
10.10.2. Clustering Methods

 10.10.2.1. K-Means Clustering
 10.10.2.2. Hierarchical Clustering

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