Expand your knowledge of Precision Oncology: Genomics and Big Data through this program, where you will find the best teaching material with real practical cases. Learn here the latest advances in the specialty to be able to perform quality medical practice”

A fundamental objective of the program is to bring students closer to and disseminate computer knowledge, which is already applied in other fields of knowledge but has minimal implementation in the medical world, despite the fact that for genomic medicine to become a reality, it is necessary to accurately interpret the enormous volume of clinical information currently available and associate it with the biological data generated after a bioinformatic analysis. 

While this is a difficult challenge, it will allow the effects of genetic variation and potential therapies to be explored quickly, inexpensively and with greater precision than is currently possible. Humans are not naturally equipped to perceive and interpret genomic sequences, to understand all the mechanisms, pathways and interactions that take place within a living cell, nor to make medical decisions with tens or hundreds of variables. To move forward, a system with superhuman analytical capabilities is required to simplify the work environment and show the relationships and proximities between variables. 

In genomics and biology, it is now recognized that it is better to spend resources on new computational techniques than on pure data collection, something that is possibly the same in medicine and, of course, oncology. 

We have millions ofpublications and enormous amounts of data, but when analyzed by physicians or biologists, the conclusions are totally subjective and relative to the available publications or data which are prioritized arbitrarily. This generates partial knowledge, which is increasingly distanced from the genetic and biological knowledge available and supported by computing, so a giant step in the implementation of precision medicine is to reduce this distance through the massive analysis of available medical and pharmacological information.

Update your knowledge through the program in Precision Oncology: Genomics and Big Data”

This professional master’s degree in Precision Oncology: Genomics and Big Data contains the most complete and up-to-date scientific program on the market. The most important features include:

• More than 75 practical cases presented by experts in Precision Oncology: Genomics and Big Data The graphic, schematic, and eminently practical contents with which they are created provide scientific and practical information on the disciplines that are essential for professional
• Novelties in precision oncology, genomics and big data
• Contains practical exercises where the self-evaluation process can be carried out to improve learning
• An algorithm-based interactive learning system for decision-making in the clinical situations presented throughout the course
• With special emphasis on evidence-based medicine and research methodologies in Precision Oncology: Genomics and Big Data
• All of this will be complemented by 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 in the selection of a refresher program for two reasons: in addition to updating your knowledge of Precision Oncology: Genomics and Big Data, you will obtain a qualification from TECH”

The teaching staff includes professionals from the field of precision oncology, who bring their experience to this specialization program, as well as renowned specialists from leading scientific societies.

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

This program is designed around Problem Based Learning, whereby the physician must try to solve the different professional practice situations that arise during the course. For this purpose, the physician will be assisted by an innovative interactive video system created by renowned and experienced experts in the field of precision oncology with extensive teaching experience.

This professional master’s degree offers training in simulated environments, which provides an immersive learning experience designed to train for real-life situations"

It includes clinical cases to bring the program's degree as close as possible to the reality of medical care"

This professional master’s degree in Precision Oncology: Genomics and Big Data is aimed at facilitating the performance of the physician dedicated to the treatment of oncological pathologies which require an accurate interpretation of the huge volume of clinical information currently available and to associate it with the biological data generated after a bioinformatic analysis.

This professional master’s degree is designed to help you update your knowledge of Precision Oncology: Genomics and Big Data with the use of the latest educational technology to contribute quality and confidence in decision-making, diagnosis, treatment, and patient support”

General objective

• Be able to accurately interpret the volume of clinical information currently available and associated with the biological data generated after a bioinformatic analysis

Specific objectives 

Module 1. Molecular Biology

• Update knowledge of the molecular biology of cancer, especially in relation to the concept of genetic heterogeneity, reprogramming of the microenvironment, the role of the immune response in cancer control and molecular mechanisms involved in the process of invasion and metastasis
• Provide and expand knowledge of immunotherapy as an example of a clear scientific advance in translational research and as one of the most promising lines of research in cancer treatment
• Learn a new approach to classifying the most common tumors based on genomic data available from The Cancer Genome Atlas (TCGA) Research Network, which not only renews traditional ideas about how malignancies are diagnosed and treated, but may also have a profound impact on the future landscape of drug development

Module 2. Genomic or precision oncology

• Discuss the change in the current landscape with the introduction of genomic data in the biological knowledge of tumors that has allowed a shift in the research and treatment of tumors from the classical view, which defines cancer as a disease according to the tissue in which it originated; and consider the genomic signature to identify tumor subtypes with independent prognostic and predictive value
• Explain how genomic classification, although correlated with tissue of origin, provides independent information to predict clinical outcomes, and will provide the biological basis for an era of personalized cancer treatment
• Learn the new genomic technologies currently used in DNA and RNA sequencing, based on the human genome sequence and made possible since the completion of the Human Genome
• Project, which has represented an unprecedented expansion of the capabilities of molecular genetics in genetic and clinical diagnostic research
• Comment on the bioinformatic process followed to interpret and apply biological data, which is fundamental since the advent of modern sequencing techniques, and which enable the organization, analysis and interpretation of biological information at the molecular, cellular and genomic levels, which is essential today, since the identification of nucleic acid sequences has become a ubiquitous and essential tool in all areas of biological science

Module 3. Changes in Current Clinical Practice and New Applications with Genomic Oncology

• Discuss and know how to interpret tumor mutational burden (TMB) as a genomic biomarker that has a significant impact on the landscape of cancer immunotherapy. This emerging marker measures the number of mutations within the tumor genome and has already shown to be associated with improved response to immune checkpoint inhibitors
• Learning how a liquid biopsy of circulating DNA allows us to understand specifically what kind of molecular changes are happening in the tumor in real time, which is a big step beyond current clinical tumor response and follow-up endpoints
• Describe the current paradigm of incorporating genomic data into current clinical practice, where treatment selection should be dictated by the specific molecular aberrations found in each patient's tumor. As a result, the classical clinical trial paradigm of patient eligibility based on clinicopathological parameters is being abandoned in favor of current clinical trials that recruit patients on the basis of specific molecular aberrations

Module 4. Use of Unix and Linux in Bioinformatics

• Learn about the Linux operating system, which is currently essential in the scientific world both for the interpretation of biological data from sequencing and it also should be for medical text mining when handling large-scale data. There are many reasons, but one that justifies this section is that Unix is the most popular system in the world and is widely used, especially in the scientific world. Moreover, being an open source system, it clearly corresponds to the scientific approach of sharing results and methods to ensure the reproducibility of the results
• Provide the basics of accessing a Linux server and how to find and install packages to install software locally
• Describe basic Linux commands for: creating, renaming, moving and deleting directories; listing, reading, creating, editing, copying and deleting files; how permissions work and how to decipher the most cryptic Linux permissions with ease; methods for searching files and directories; how to compare the contents of files; what pipes are, why they are useful and how to use them; how to zip files to save space and ease data transfer, etc.

Module 5. Data Analysis in Big Data Projects: R Programming Language

• Discuss how the adoption of next-generation sequencing (NGS) in a diagnostic context raises numerous questions regarding the identification and reporting of variants in secondary genes for patient pathology, making it critical to define those genes considered actionable due to their efficient curation process and published data collection
• Getting started with the R programming language, which has the advantages of being an open-source programming language, multiple statistical analysis packages, a community that strives to develop the various aspects of this tool, and provides an effective language for managing and manipulating data
• Learn basic R programming concepts such as data types, vector arithmetic and indexing
• Performing operations in R, including sorting, creating or importing data
• Learn how problem solving begins with a modular decomposition and then further decompositions of each module in a process called successive refinement
• Learn the basics of statistical inference to understand and calculate p-values and confidence intervals while analyzing data with R
• Provide examples of R programming in a way that will help make the connection between concepts and their implementation

Module 6. The Graphical Environment in R

• Using visualization techniques to explore new datasets and determine the most appropriate approach
• Learn how to visualize data to extract information, better understand data and make more effective decisions
• Teach how to take data that at first glance has little meaning and visually present that data in a form that makes sense for analysis
• Learn how to use the three main graph sources in R: base, lattice and ggplot2
• Know what each graphics package is based on in order to define which one to use and the advantages offered by one or the other

Module 7. Statistical analysis in R

• Describe the most appropriate statistical techniques as an alternative when data do not conform to the assumptions required by the standard approach
• Learn the basics of conducting reproducible research by using R scripts to analyze data

Module 8. Machine Learning for Analyzing Big Data

• Rapidly and automatically process and analyze enormous volumes of complex structured, semi-structured and unstructured data in big data
• Understand what machine learning is and to use some of the techniques for data classification (decision tree, k-NN, Support Vector Machines, neural networks, etc.)
• Learn how to divide data into a test set and a training set and discover the concepts of bias and variance

Module 9. Data Mining Applied to Genomics

• Learn how data mining facilitates finding patterns and regularities in databases, which will be very useful for making predictions and prognoses, and in general improving and expanding knowledge through interaction with data, which is being crucial for the enrichment of genetic variants and will be essential for clinical enrichment and implementation of precision oncology
• Learn to apply the principles of data mining to the analysis of large complex datasets (Big Data), including those in very large databases or on web pages
• Explore, analyze and leverage data and convert it into useful and valuable information for clinical practice

Module 10. Techniques Genomic Data Extraction

• Understand how most scientific data appear in documents such as web pages and PDF files that are difficult to process for further analysis. However, we can make them usable by means of scraping techniques
• Access many data sources through the web has made scraping techniques an essential part of the toolkit for the implementation of precision medicine, by allowing the massive extraction of information, its subsequent processing and conversion into useful data for interpretation

Module 11. New Techniques in the Genomic Era

• Put into practice the knowledge acquired for the interpretation of a genomic study in several cancer cases by extracting useful information that will help in decision making
• Using several algorithms performed with the R language for the extraction of knowledge from Pubmed, DGIdb and Clinical Trials databases based on the search for genetic information in certain tumors

Module 12. Application of bioinformatics in genomic oncology

• Understanding the function of genes with little clinical information based on ontological proximity
• Discover genes involved in a disease based on a massive Pubmed search and graphical representation of the level of scientific evidence

Make the most of the opportunity and take the step to get up to date on the latest developments in Precision Oncology: Genomics and Big Data”