This program is the best option you can find to specialize in Bioinformatics Applied to Venous Thromboembolism and to establish more accurate diagnoses” 

Thrombosis is an often undiagnosed pathology that can affect anyone, regardless of age, and that can become a serious disease. 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.

Throughout this postgraduate diploma, students will focus on Bioinformatics Applied to Venous Thromboembolism , with a program designed by specialists in the field, so students will receive a complete and specific training from area experts.

The training program aims to establish the basis of knowledge in the field, starting with studies of the pathophysiology and epidemiology of venous thromboembolic disease. Omics data will also be studied, which will enable specialists to learn the programming language R and predictive models.

Therefore, after completing and passing the postgraduate diploma, students will have acquired the theoretical knowledge necessary to carry out effective treatment of venous thrombosis in the main areas of professional practice. 

Don't miss the opportunity to study this postgraduate diploma in Bioinformatics Applied to Venous Thromboembolism with us. It's the perfect opportunity to advance your career” 

This postgraduate diploma in Bioinformatics Applied to Venous Thromboembolism contains the most complete and up-to-date scientific program on the market. Its most notable features are:

• Case studies presented by experts in Bioinformatics Applied to Venous Thromboembolism
• 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 developments in Bioinformatics Applied to Venous Thromboembolism
• Practical exercises where self-assessment can be used to improve learning
• Special emphasis is placed on innovative methodologies in Bioinformatics Applied to Venous Thromboembolism
• 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 postgraduate diploma is the best investment you can make when selecting a refresher program, for two reasons: in addition to updating your knowledge of Bioinformatics Applied to Venous Thromboembolism , you will obtain a qualification endorsed by TECH Technological University”

The teaching staff includes professionals in the field of Bioinformatics Applied to Venous Thromboembolism , who bring their experience to this specialization program, as well as renowned specialists from leading 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 that end, the professional will have the help of an innovative, interactive video system made by recognized and extensively experienced experts in Bioinformatics Applied to Venous Thromboembolism .  

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

This 100% online postgraduate diploma will allow you to combine your studies with your professional work while expanding your knowledge in this field"

The postgraduate diploma in Bioinformatics Applied to Venous Thromboembolism is designed to facilitate the performance of biomedicine professionals with the latest advances and most innovative procedures in the sector.

This is the best option to learn about the latest advances in Bioinformatics Applied to Venous Thromboembolism ” 

General Objectives

• Delve into the knowledge of venous thromboembolism as a complex disease
• Train in the field of omics data and bioinformatic methods applied to precision medicine
• Keep up with the latest updates on the disease

Specific Objectives 

Module 1. Pathophysiology and Epidemiology in Venous Thromboembolism

• Demonstrate the enormous biological and clinical complexity underlying venous thromboembolism
• Explain the pathological mechanisms by which a thrombus develops in the veins and the short- and long-term consequences it may have
• Analyze the relations of thrombus and recurrence with determinant variables such as age, sex or race
• Highlight the importance of the circumstances associated with thromboembolic events and how these circumstances largely determine the risk of recurrence
• Describe the environmental risk factors that are associated with the disease and the genetic basis known today
• Review the global impact on the worldwide burden of disease and the economic impact of thrombosis, its sequelae and the complications in treatment
• Delve into the concept of biomarkers or intermediate phenotypes with the risk of the disease, which can be studied in diagnosing the cause and estimating risk of recurrence, and that can be used as a starting point to discover the genes involved in phenotype variability and, therefore, in venous thromboembolisms
• Understand the concept of individual risk profile

Module 2. Omic Data: Introduction to the Programming Language R 

• Learn about the Unix/Linux operating system and its importance
• Obtain notions of basic Unix/Linux administration
• Learn how to manage files and directories using the Unix/Linux command interpreter
• Learn the programming language R and how to manage its packages
• Recognize the different types of data in R and know which to use in each context
• Learn how to correctly manipulate each data type in R
• Know what control functions and loops are and how they are implemented in R
• Perform graphical representations of data and results in R
• Apply basic statistics in R depending on the characteristics of the data
• Learn how to implement proprietary functions in R to perform specific tasks 

Module 3. Predictive Models

• Identify the different types of statistical learning problems
• Understand and implement the steps involved in preprocessing a new dataset
• Understand the fundamentals of linear regression models and their scope of application
• Optimize linear regression models with the lowest possible number of variables
• List the different types of classification models and know when to use each of them
• Learn different ways to validate the performance of a predictive model
• Become familiar with decision trees and their extensions
• Adjust support vector machines to clinical data and assess their performance
• Learn different unsupervised learning methods for exploratory data analysis

Take advantage of the opportunity and take the step to get up to date on the latest developments in Diabetes”