A study option that will qualify you in the field of computation and the use of Machine Learning algorithms in public health, to become an industrial expert in Bioinformatics" 

The explosion of Bioinformatics as an indispensable discipline in many fields, such .professionals. The speed with which new diseases adapt to the environment in order to remain is what makes expert qualification necessary for data analytical engineers to respond to emerging changes.

Currently, the COVID-19 viral genome mutation has been one of the reasons why the bioinformatics discipline has come to the forefront of medicine. For this reason, TECH has created a digital learning pathway, which is aimed at instructing engineers in the field of computation and Big Data. The program is proposed with the support of a professional team that is dedicated to the sector and that will be available to carry out a thorough follow-up to the student and solve all their questions about the subject.

This Postgraduate diploma inBioinformatics and Big Data in Medicine corresponds to a program with a design adapted to the new media that facilitates student learning, thanks to its 100% online mode and its audiovisual content. In addition, as it has a downloadable syllabus, the user will be able to access the materials without an Internet connection and even when they have already completed the Postgraduate diploma

Still not familiar with the evolution of Big Data in medicine? Enroll in a program that will not only teach you to understand the importance of databases, but also how to apply them in healthcare centers" 

This  Postgraduate diploma inBioinformatics and Big Data in Medicine contains the most complete and up-to-date program on the market. The most important features include: 

• The development of practical cases presented by experts in bioinformatics and databases
• The graphic, schematic, and practical contents with which they are created, provide practical information on the disciplines that are essential for professional practice
• Practical exercises where self-assessment can be used to improve learning
• Its special emphasis on innovative methodologies
• 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

Information is power, enroll in this Postgraduate diploma to explore data preprocessing techniques using tools such as Gene Ontology and KEGG"

The program’s teaching staff includes professionals from the sector who contribute their work experience to this educational program, as well as renowned specialists from leading societies and prestigious universities.

Its multimedia content, developed with the latest educational technology, will allow the professional a situated and contextual learning; that is, a simulated environment that will provide an immersive training programmed to train in real situations.

This program is designed around Problem-Based Learning, whereby the professional must try to solve the different professional practice situations that arise during the course. For this purpose, students will be assisted by an innovative interactive video system created by renowned and experienced experts. 

Thanks to TECH, you can interpret extensive data collections and cooperate in research lines and trials"

Develop more effective therapies with fewer side effects on the human body, thanks to the Adverse Drug Reaction (ADR) databases"

The syllabus of this Postgraduate diploma in Bioinformatics and Big Data in Medicine has been designed in detail by professionals working in the field of biological engineering and bioinformatics. Thanks to their contribution and the incorporation of technological tools, students will have at their disposal audiovisual contents in different formats that will help them in their education addition, the Relearning methodology applied by TECH makes students forget about long hours of memorization and assimilate the contents in a gradual and simple way. In this way, the 100% online study will be adapted to your availability, through theoretical and practical exercises that will prepare you for real cases. 

Informatics is part of us, now you can make information flows optimal to enhance clinical outreach" 

Module 1. Computation in Bioinformatics

1.1. Central Tenet in Bioinformatics and Computing. Current State

1.1.1. The Ideal Application in Bioinformatics
1.1.2. Parallel Developments in Molecular Biology and Computing
1.1.3. Dogma in Biology and Information Theory
1.1.4. Information Flows

1.2. Databases for Bioinformatics Computing

1.2.1. Database
1.2.2. Data Management
1.2.3. Data Life Cycle in Bioinformatics

1.2.3.1. Use
1.2.3.2. Modifications
1.2.3.3. Archive
1.2.3.4. Reuse
1.2.3.5. Discarded

1.2.4. Database Technology in Bioinformatics

1.2.4.1. Architecture
1.2.4.2. Database Management

1.2.5. Interfaces for Bioinformatics Databases

1.3. Networks for Bioinformatics Computing

1.3.1. Communication Models. LAN, WAN, MAN and PAN Networks
1.3.2. Protocols and Data Transmission
1.3.3. Network Topologies
1.3.4. Datacenter Hardware for Computing
1.3.5. Security, Management and Implementation

1.4. Search Engines in Bioinformatics

1.4.1. Search Engines in Bioinformatics
1.4.2. Search Engine Processes and Technologies in Bioinformatics
1.4.3. Computational Models: Search and Approximation Algorithms

1.5. Data Display in Bioinformatics

1.5.1. Displaying Biological Sequences
1.5.2. Displaying Biological Structures

1.5.2.1. Visualization Tools
1.5.2.2. Rendering Tools

1.5.3. User Interface in Bioinformatics Applications
1.5.4. Information Architectures for Displays in Bioinformatics

1.6. Statistics for Computing

1.6.1. Statistical Concepts for Computing in Bioinformatics
1.6.2. Use Case: MARN Microarrays
1.6.3. Imperfect Data. Statistical Errors: Randomness, Approximation, Noise and Assumptions
1.6.4. Error Quantification: Precision and Sensitivity
1.6.5. Clustering and Classification

1.7. Data Mining

1.7.1. Mining and Data Computing Methods
1.7.2. Infrastructure for Data Mining and Computing
1.7.3. Pattern Discovery and Recognition
1.7.4. Machine Learning and New Tools

1.8. Genetic Pattern Matching

1.8.1. Genetic Pattern Matching
1.8.2. Computational Methods for Sequence Alignments
1.8.3. Pattern Matching Tools

1.9. Modelling and Simulation

1.9.1. Use in the Pharmaceutical Field: Drug Discovery
1.9.2. Protein Structure and Systems Biology
1.9.3. Available Tools and Future

1.10. Collaboration and Online Computing Projects

1.10.1. Grid Computing
1.10.2. Standards and Rules Uniformity, Consistency and Interoperability
1.10.3. Collaborative Computing Projects

Module 2. Biomedical Databases

2.1. Biomedical Databases

2.1.1. Biomedical Databases
2.1.2. Primary and Secondary Databases
2.1.3. Major Databases

2.2. DNA Databases

2.2.1. Genome Databases
2.2.2. Gene Databases
2.2.3. Mutations and Polymorphisms Databases

2.3. Protein Databases

2.3.1. Primary Sequence Databases
2.3.2. Secondary Sequence and Domain Databases
2.3.3. Macromolecular Structure Databases

2.4. Omics Projects Databases

2.4.1. Genomics Studies Databases
2.4.2. Transcriptomics Studies Databases
2.4.3. Proteomics Studies Databases

2.5. Genetic Diseases Databases. Personalized and Precision Medicine

2.5.1. Genetic Diseases Databases
2.5.2. Precision Medicine. The Need to Integrate Genetic Data
2.5.3. Extracting Data from OMIM

2.6. Self-Reported Patient Repositories

2.6.1. Secondary Data Use
2.6.2. Patients' Role in Deposited Data Management
2.6.3. Repositories of Self-Reported Questionnaires. Examples:

2.7. Elixir Open Databases

2.7.1. Elixir Open Databases
2.7.2. Databases Collected on the Elixir Platform
2.7.3. Criteria for Choosing between Databases

2.8. Adverse Drug Reactions (ADRs) Databases

2.8.1. Pharmacological Development Processes
2.8.2. Adverse Drug Reaction Reporting
2.8.3. Adverse Reaction Repositories at European and International Levels

2.9. Research Data Management Plans. Data to be Deposited in Public Databases

2.9.1. Data Management Plans
2.9.2. Data Custody in Research
2.9.3. Data Entry in Public Databases

2.10. Clinical Databases. Problems with Secondary Use of Health Data

2.10.1. Medical Record Repositories
2.10.2. Data Encryption

Module 3. Big Data in Medicine: Massive Medical Data Processing 

3.1. Big Data in Biomedical Research

3.1.1. Data Generation in Biomedicine
3.1.2. High-Throughput Technology
3.1.3. Uses of High-Throughput Data. Hypotheses in the Age of Big Data

3.2. Data Pre-Processing in Big Data

3.2.1. Data Pre-Processing
3.2.2. Methods and Approaches
3.2.3. Problems with Data Pre-Processing in Big Data

3.3. Structural Genomics

3.3.1. Sequencing the Human Genome
3.3.2. Sequencing vs. Chips
3.3.3. Variant Discovery

3.4. Functional Genomics

3.4.1. Functional Notation
3.4.2. Mutation Risk Predictors
3.4.3. Association Studies in Genomics

3.5. Transcriptomics

3.5.1. Techniques to Obtain Massive Data in Transcriptomics: RNA-seq
3.5.2. Data Normalization in Transcriptomics
3.5.3. Differential Expression Studies

3.6. Interactomics and Epigenomics

3.6.1. The Role of Chromatin in Gene Expression
3.6.2. High-Throughput Studies in Interactomics
3.6.3. High-Throughput Studies in Epigenetics

3.7. Proteomics

3.7.1. Analysis of Mass Spectrometry Data
3.7.2. Post-Translational Modifications Study
3.7.3. Quantitative Proteomics

3.8. Enrichment and Clustering Techniques

3.8.1. Contextualizing Results
3.8.2. Clustering Algorithms in Omics Techniques
3.8.3. Repositories for Enrichment: Gene Ontology and KEGG

3.9. Applying Big Data to Public Health

3.9.1. Discovery of New Biomarkers and Therapeutic Targets
3.9.2. Risk Predictors
3.9.3. Personalized Medicine

3.10. Big Data Applied to Medicine

3.10.1. Potential for Diagnostic and Preventive Assistance
3.10.2. Use of Machine Learning Algorithms in Public Health
3.10.3. The Problem of Privacy

Take the step to get up to date on the latest developments in Bioinformatics and Big Data in Medicine”