Thanks to TECH Global University, you will learn the mininvasive development of clinical practices in only 6 months of academic instruction"

The incorporation of bioinformatics in the health field is an advance that acts in parallel with Big Data and, with COVID, it was fundamental for the knowledge and interpretation of data at a global level. These disciplines enable the management of the enormous volume of data generated by new omics technologies. Bioinformatics is a high-level approach to mutation in biology, which is why it has gained importance over the years and its scientific evidence.  

Currently, controlling the mutation of epidemiological diseases is the main reason for the increase in bioinformatics studies. If possible, the vaccine would have been unique and it would not be necessary to look for alternatives according to the variation of the disease. For this reason, TECH Global University offers a Postgraduate diploma inBioinformatics and Big Data in Medicine, aimed at graduates in Nursing to expand and update the knowledge of these professionals so that they are able to apply it in their daily work.  

This Postgraduate diploma is supported by an expert teaching team in biomedicine that will transmit not only theoretical knowledge to the students, but will also instruct them based on their own real experience through case simulations. Also, TECH Global University applies the Relearning methodology to offer dynamic instruction that does not require long hours of memorization.  Likewise, thanks to its 100% modality and its audiovisual content, students will be able to a dapt the pace of study to their personal and professional possibilities.   

Not proficient in Machine Learning algorithms? Enroll now in a program that will not only teach you to understand public health computing, but will also instruct you in bioinformatics"  

This Postgraduate diploma in Bioinformatics and Big Data in Medicine contains the most complete and up-to-date scientific 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 the self-assessment process can be carried out 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

Thanks to TECH Global University, you will be able to understand the ins and outs of bioinformatics and become a much more competent and competitive professional in the job market" 

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.  

The design of this program focuses on Problem-Based Learning, in which the professional will have to try to solve the different professional practice situations that will arise throughout the academic course. This will be done with the help of an innovative system of interactive videos made by renowned experts.    

Prevention and health diagnosis is in the hands of technology and how the professionals of the future know how to implement it. Update yourself withTECH Global University"

Enroll now in this Postgraduate diploma to deepen in data preprocessing techniques with Gene Ontology and KEGG"

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 bioinformatics and biomedicine, among other disciplines of Health Sciences. It is a 100% online program that streamlines the educational process around knowledge in computing, biomedical databases and Big Data in medicine. TECH Global University achieves this thanks to the innovative Relearning methodology. With it, students will not have to invest long hours of memorization, but will be able to assimilate the contents gradually and steadily. In this way, the study will be adapted with total flexibility to their availability, obtaining a personalized academic experience in line with their professional and personal obligations.  

Develop molecular biology and computation in parallel to witness its multiple advantages. This is the only way you will be part of the evolution in Health"

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. Errors in statistics: 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 

2.2.1. 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 
2.10.3. Access to Health Data. Legislation

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 Performance (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 Cromatine 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 

A program designed for you to discover new biomarkers and therapeutic targets, thanks to the application of Big Data"