This program will provide you with a sense of confidence in medical practice, which will help you grow personally and professionally”

The scale and complexity of genomic data dwarf the measurements traditionally used in laboratory testing. In recent years there has been an enormous development of informatics to analyze and interpret DNA sequencing, and it has created a gap between biological knowledge and its application to routine clinical practice. It is therefore necessary to educate, disseminate and incorporate these informatics techniques among the medical community in order to be able to interpret the massive analysis of data from publications, biological or medical databases and medical records, among others, and thus enrich the biological information available at the clinical level. 

This machine learning will enable the development of precision oncology, in order to interpret genomic characteristics and find targeted therapies, or to identify risks to certain diseases and establish more individualized preventive measures. 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 huge 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. 

Update your knowledge with the Postgraduate diploma Program in Machine Learning and Data Mining Techniques in Genomic Oncology"

This Postgraduate diploma in Machine Learning and Data Mining Techniques in Genomic Oncology contains the most complete and up-to-date scientific program on the market. The most important features include:

• Development of case studies presented by experts in Machine Learning and Data Mining Techniques in Genomic Oncology
• Its graphic, schematic and practical contents are designed to provide scientific and practical information on those disciplines that are essential for professional practice
• News on Machine Learning and Data Mining Techniques in Genomic Oncology
• It contains practical exercises where the self-assessment process can be carried out to improve learning
• With special emphasis on innovative methodologies in Machine Learning and Data Mining Techniques in Genomic Oncology
• 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 Postgraduate diploma may be the best investment you can make in the selection of a refresher program for two reasons: in addition to updating your knowledge in Machine Learning and Data Mining Techniques in Genomic Oncology, you will obtain a qualification from TECH Global University"

Its teaching staff includes professionals belonging to the field of Machine Learning and Data Mining Techniques in Genomic Oncology, who bring to this program the experience of their work, as well as recognized specialists belonging to 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 an immersive program to learn in real situations.

This program is designed around Problem-Based Learning, whereby the student must try to solve the different professional practice situations that arise during the Programming. For this, the student will be assisted by a novel interactive video system developed by recognized experts in the field of Machine Learning Techniques and Data Mining in Genomic Oncology with extensive teaching experience.

Increase your decision-making confidence by updating your knowledge with this Postgraduate diploma"

Take the opportunity to learn about the latest advances in Machine Learning and Data Mining Techniques in Genomic Oncology and improve the care of your patients"

The structure of the contents has been designed by a team of professionals from the best educational centers, universities, and companies in the national territory, aware of the relevance of current specialization in order to intervene in the training and support of students, and committed to quality teaching through new educational technologies

This Postgraduate diploma in Machine Learning and Data Mining Techniques in Genomic Oncology contains the most complete and up-to-date scientific program on the market”

Module 1. Machine learning for Analysing Big Data

1.1. Introduction to Machine Learning
1.2. Presentation of the Problem, Loading Data and Libraries
1.3. Data Cleaning (NAS, Categories,  Dummy Variables)
1.4. Exploratory Data Analysis (Ggplot) + Crossed Validation
1.5. Prediction Algorithms: Multiple Linear Regression, Support Vector Machine, Regression Trees, Random Forest, etc.
1.6. Classification Algorithms: Logistic Regression, Support Vector Regression, Classification Trees, Random Forest
1.7. Adjustment of the Algorithm's Hyperparameters
1.8. Predicting Data with Different Models
1.9. ROC Curves and Confusion Matrices for Assessing Model Quality

Module 2. Data Mining Applied to Genomics

2.1. Introduction
2.2. Initiation to Variables
2.3. Text Cleaning and Conditioning
2.4. Generating the Words Matrix

2.4.1. Creating the TDM Words Matrix
2.4.2. Visualizations on the TDM Word Matrix

2.5. Description of the Word Matrix

2.5.1. Graphic Representation of the Frequencies
2.5.2. Creating a Word Cloud

2.6. Creating a Data Frame  for K-NN
2.7. Creating a Classification Model
2.8. Validating a Classification Model
2.9. Guided Practical Exercise on Data Mining in Cancer Genomics

Module 3. Techniques for Extracting Genomic Data

3.1. Introduction to‘Scraping Data’
3.2. Importing Spreadsheet Data Files Stored Online
3.3. Scraping HTML Text
3.4. Scraping Data from an HTML Table
3.5. Using APIs for Data Scraping
3.6. Extracting Relevant Information
3.7. Using the Rvest Package of R
3.8. Obtaining Data Distributed Over Multiple Pages
3.9. Extracting Genomic Data from the “My Cancer Genome” Platform
3.10. Extracting Information on Genes from the “HGNC Hugo Gene Nomenclature Committee” Database
3.11. Extracting Pharmacological Data from the‘ONCOKG’ (Precision Oncology Knowledge Base) Database

Module 4. Application of Bioinformatics in Genomic Oncology

4.1. Clinical and Pharmacological Enrichment of Gene Variants
4.2. Mass Search in PubMed for Genomic Information
4.3. Mass Search in DGIdb for Genomic Information
4.4. Mass Search in Clinical Trials for Clinical Trials on Genomic Data
4.5. Gene Similarity Search for the Interpretation of a Gene Panel or Exome
4.6. Mass Search for Genes Connected to a Disease
4.7. Enrich-Gen: Platform for the Clinical and Pharmacological Enrichment of Genes
4.8. Procedure to Produce a Genomic Report in the Age of Precision Oncology

A unique, key and decisive training experience to boost your professional development"