Oncology Specialization Track

Cancer is an important challenge for which personalized molecular medicine shows great promise. Recent advances in immunotherapy and genetic testing have been proposed to help transform care from one-size-fits-all to a highly specialized range of options that could be adapted to fit an individual’s molecular features. However, we are still far from understanding and navigating cancer. In fact, many promising research projects that reveal new targets and promising strategies that work on animal models fail in clinical trials. While generating data has been increasingly accessible, the challenge is now in integration and extracting insights from this data. A multi-omics approach based on big data analyses could lead to substantial advances in cancer treatment.

Curated Datasets

Project-based courses utilize datasets from high impact and up-to-date publications. Learning from these projects students get exposure to great research and curated data they can learn from.

Cutting Edge Research

Studying important research challenges, students get the opportunity to learn from the scientific process and interact with top researchers that hold regular sessions on these topics.

From Theory to Application

Each concept gets introduced with theoretical content that is simple to digest. The same concepts are shown in practical hands-on projects to explain the data aspects and challenges.

 Oncology Specialization Track

The Onclogy Specialization track is a series of lessons that focus on cancer, with practical, hands-on projects that allow students to practice analyses with data adapted from research papers from top impact journals.

1. Introduction to molecular indicators of cancer: deregulation of cellular checks and balances, uncontrollable growth, alternate signaling, altered immune responses, etc. 

2. Factors that contribute to cancer heterogeneity – levels of biological regulation and cell populations. 

3. Response to treatment studied with multi-omics data from cancer cell lines 

4. Patient Derived Xenograft mouse models: the role of microenvironment and the use of animal models 

5. The Cancer Genome Atlas (TCGA data) – real patients: miRNA-seq, RNA-seq, Exome-seq and clinical data 

6. Deeper look into clinical data: combinations of treatments, many ways to diagnose cancer, why molecular data is critical for targeted treatments. 

7. Future of Cancer: New treatments and ndings that could change current cancer treatment options for patients. 

Projects: Cell lines to study cancer subgroups, Macrophages and the role of the Immune System, PDX study of tumor-stroma interaction in Patient-derived Xenograft Models, TCGA triple negative breast cancer study in patients.

Pilot and Advisors: Dr. Kasthuri Kannan (NYU), Dr. Lucio Miele (LSU), Dr. Dario Ghesi (UNO)

Project Team: Dr. Ronald Ferucci, Julia Panov, Sahil Sethi, Jack LeBien

Created by:

In collaboration with:

 

Courses

This course will be made available in December 2018
This course will be made available in 2019

Machine Learning for Biomedical Data

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165
students
0
$125.00 $45.00

This course will cover conceptual aspects of machine learning in application to high-throughput biomedical data. Throughout the course, students will get an understanding of...

$125.00 $45.00

Projects

Certificate of Completion

After completing the specialization track, you will receive a certificate of completion.

The certificate of completion can be from our company or from one of the participating institutions we are collaborating with. You can find out more about certification by reaching out to our education counselors (email to: info@pine-biotech.com)

 

Specialization Track Contributors and Advisors

FAQ’s

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Fusce et pretium purus. Sed eu tincidunt ipsum, et ullamcorper erat. Proin congue consect sdfetur dui et convallis. Mauris accu msan lectus in diam pretium, ac aliquet tellus Suspendisse vel lectus eu magna commodo aliquam. Nunc nec lorem orci. Pelle ntesque facilisis lacus ac velit imperdiet, sit amet accumsan ex pellentesque. Nullam malesuada ligula vel magna vulputate cons equat. Fusce nec rhoncus sem, a varius risus. Donec tristique rhoncus metus, ut venenatis lacus porta sit amet. Nulla facilisi. Sed et magna mauris. Aliquam interdum eu dolor quis ultricies.