Essentials of Genomics and Biomedical Informatics

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Three innovations are driving the data revolution in medicine.

• Next Generation Sequencing, and in particular, the ability to sequence individual genomes at diminishing costs.

• Electronic Medical Records, and our ability to mine, using machine learning techniques, huge datasets of medical records.

• Wearable devices, the Web, social networks and crowdsourcing - exemplifying the surprising capacity to collect medical data using non-conventional resources.

In order to take advantage of these technologies and participate in the revolution, physicians need a new toolbox that is generally lacking in the medical school curriculum.

This course is a product of a decade of a collaborative effort between researchers from the computational biology program at Bar-Ilan University, and clinicians from Sheba Medical Center to develop and deliver an extended curriculum in genomics and biomedical informatics. The program has been endorsed by the Israeli Medicine Association and Ministry of Health. Here, we present a condensed online course that includes selected topics chosen from the extended program.

This GaBI course on edX presents clinicians and digital health enthusiasts with an overview of the data revolution in medicine, and how to take advantage of it for research and in the clinic. In the scope of this single course, you will not become a bioinformatician, but you will be able to familiarize yourself with the main concepts, tools, algorithms, and databases used in this field, and understand the types of problems that these analysis techniques can help address.

The syllabus covers the main topics of this discipline in a logical order:

- Methods used to obtain medical data (genotypic and phenotypic)

- Analysis of biological molecules such as DNA, RNA, and proteins using various computational tools from the field of bioinformatics

- Use of machine learning and artificial intelligence tools to mine the huge databases of medical information accumulating in Electronic Medical Records (EMRs), the Web, and numerous data science projects in medicine

- Analysis of complex interaction networks between DNA, RNA and protein molecules to gain a more holistic and systematic view of biological systems and medical conditions

- Practical applications in the clinic and in personalized medicine research, and the use of cutting edge technology to improve health

What you will learn

● The scientific basis, concepts, and language needed to communicate effectively with bioinformaticians, statisticians and data scientists

● First-hand experience in a computational analysis in the practice sessions

● An opportunity to conduct your own basic research (utilizing existing tools, not programming new ones)

● Sufficient background to enable further studies, research, or training towards a career in this field

● And ultimately, the ability to provide better and more personalized medicine for your patients

Skills you learn

• Curriculum Development
• Machine Learning
• Bioinformatics
• Ribonucleic Acid Sequencing
• Biology
• Biological Systems
• Personalized Medicine
• Artificial Intelligence
• Electronic Medical Record
• Medical Records
• Data Science
• Computational Biology
• Algorithms
• Computational Tools
• Genomics
• Biomedical Informatics

Syllabus

Units:

Week 1: The Data Revolution in Medicine

Description of medicine as a data driven science, and discussion of new approaches to identify or design drugs optimized for specific patients and disease characteristics. We will discuss the medical revolution that combines genomic information and machine learning techniques.

Week 2:Biological Sequence Analysis

Overview of biological sequences, how we compare pairs of sequences, perform multiple sequence alignment, and how to construct phylogenetic trees.

Week 3:Biological Databases and How to Search Them

Description of sequence databases, and introduction to BLAST, which is the main tool used to search in such databases. Then, we will mention additional genomic databases with clinical applications, and describe the UCSC genomic browser that is used for viewing multiple layers of genomic information.

Week 4: Next Generation Sequencing

Description of the technology used in Next Generation Sequencing, the computational analysis involved, and medical applications of the technology.

Week 5: Medical Machine Learning

Analysis of non-genomic types of medical data, such as those stored in Electronic Medical Records (EMRs). Harnessing modern machine learning algorithms to make sense of this data “jungle”, enable early detection and diagnosis, predict treatment outcome, and more.

Week 6: Artificial Intelligence in Medicine

Overview of today’s cutting edge technologies that strive to simulate the capabilities of human experts for medical purposes. These technologies include natural language processing, interpretation of medical images, development of decision support systems, and more.

Week 7: Systems Biology

Understanding biological systems as a dynamic network of interacting molecules. Application of this concept to rational drug design and the identification of disease biomarkers.

Week 8: Human Genomic Variations
The source of variations in the human genome and how we can predict the clinical implications of such variations. Consequences of such variations in monogenic and complex disease.

Week 9: Cancer as a Genomic Disease

Viewing cancer as a disease of the genome of each patient, from genetic predisposition to somatic mutations acquired later in life. Utilization of these concepts in immunotherapy.

Auto Summary

"Essentials of Genomics and Biomedical Informatics" is an expert-level course offered by edX, targeting clinicians and digital health enthusiasts. The course provides an overview of the data revolution in medicine, focusing on key concepts, tools, algorithms, and databases in genomics and biomedical informatics. Designed to enhance research and clinical applications, it offers flexible subscription options: Starter and Professional. Ideal for those seeking to understand the integration of data in medical science.