IsraelX: Essentials of Genomics and Biomedical Informatics
This course presents clinicians and digital health enthusiasts with an overview of the data revolution in medicine and how to exploit it for research and in the clinic. The course will not make you a bioinformatician but will introduce the main concepts, tools, algorithms, and databases in this field.
There is one session available:
Essentials of Genomics and Biomedical Informatics
About this courseSkip About this course
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
At a glance
- Language: English
- Video Transcripts: اَلْعَرَبِيَّةُ, English, עברית
- Associated skills: Electronic Medical Record, Curriculum Development, Medical Records, Personalized Medicine, Artificial Intelligence, Computational Biology, Ribonucleic Acid Sequencing, Machine Learning, Computational Tools, Biology, Data Science, Bioinformatics, Biological Systems, Genomics, Algorithms, Biomedical Informatics
What you'll learnSkip What you'll 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
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.
Learner testimonialsSkip Learner testimonials
"Being introduced to the field of medical data mining through the GaBI program has had a major impact on my career. It allowed me to utilize computational tools developed for complex financial and commercial scenarios to solve clinical dilemmas."
- Roni Shouval, M.D.
"I never expected this program to be so interesting. I used to often send my boss articles and ask him to collaborate with me. Now, after I attended GaBI, my boss sends me articles! I highly recommend this course."
- Guy Zehavi, M.D.
"I came from the industry. I am grateful for every minute I spent attending the GaBI program. It changed my life and I recommend it to everyone I know. Even when I negotiate deals with international companies, I now have a better understanding of what to look for."
- Avivit Raz, M.Sc.
"The GaBI program is great! The content was fascinating, the technology was relevant, and the staff was amazing!"
- Eran Ashwal, M.D.
"This was a wonderful surprise. Needless to say, the material was challenging, but I succeeded. GaBI really made me activate more neurons than ever!"
- Maisam Shehab, M.D.
"The program is very intense but very interesting. A few months ago I thought about going into research, and now, thanks to GaBI, I am thinking of pursuing a PhD.
- Ofer Raz, M.D.
Frequently Asked QuestionsSkip Frequently Asked Questions
Who is the audience of this course?
This course is mainly intended for physicians, but it is also suitable for advanced medical students, digital health enthusiasts and for the general audience, provided they have a strong background in biology or health sciences.
Do I need to know programming in order to take this course?
Programming skills are not required.
Do you teach programming in the course?
No, we teach how to use computational tools, but this course does not teach programming.
Will I be able to do my own research in bioinformatics or machine learning after I finish this course?
The aim of the course is not to transform the students into bioinformaticians. The course does provide hands-on experience that enables students to perform basic computational tasks. Our main goal is for the students to be able to communicate better with bioinformaticians, to read and understand medical papers that use computational methods, and understand the sorts of research and clinical problems that are amenable to a computational approach.
Why do you have so many lecturers involved in this course?
Our disciple is broad and encompasses a wide range of specialties. We wanted each of the units to be prepared and delivered by actual scientists who are experts in the subject matter.