This educational website is maintained by Pine Biotech, a company that specializes in solutions for data-driven life science research, STEM education and Bioinformatics. Pine Biotech is on a mission to simplify bioinformatics by enabling user-friendly multi-omics data processing, analysis, and integration. We strive to improve human health and well-being by innovating biomedical data science education, research and decision making. Pine-Biotech is a tech transfer company from the University of Haifa. Our major academic partner is the expert team at the Tauber Bioinformatics Research Center. After incorporating in 2014, our team grew and moved into the BioInnovation Center in the great city of New Orleans in 2016. We continue to grow: each day, dozens of people join our programs and we are regularly adding new staff to our team.

Big Data Bioinformatics For Everyone

Our vision is to enhance human health and well being by enabling Biological Research and Discovery with relevant data, solutions, and support. Our mission is to simplify bioinformatics and advance research through our modular and intuitive multi-omics analysis platform powered by Human Experience and Artificial Intelligence.

Program Vision and Leadership

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Dr. Alfred Tauber

Univeristy of Haifa, Israel
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Dr. Leonid Brodsky

Professor of Bioinformatics

“Your intuitive web-based platform is perfectly suited for the educational setting and I am excited at the prospect of it being more widely available in this capacity. It has been a great pleasure to work with the Pine Biotech team thus far, and I look forward to our continued collaborations.”

Chris McGowin, LSU HSC

Meet Our Instructors, Advisors and Co-Presenters

Advisors, project and curriculum developers:

JackLeBien

Jack LeBien

Data Scientist
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Dr. Saaket Varma

Biomedical Data Scientist
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Dr. Claudia Copeland

Educational Content Creator
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Jaclyn Williams

Microbiologist
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Julia Panov

Bioinformatics Ph.D. Candidate

Program organizers and contributors:

Meet the people making bioinformatics more accessible across the US and around the world.

Dr. Paul Kim

Dr. Paul Kim

Grambling University
Dr. Sona Vasudevan

Dr. Sona Vasudevan

Georgetown University Medical Center
Kousoulas Konstantin

Dr. Konstantin “Gus” Kousoulas

Louisiana Biomedical Research Network
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Dr. Lyndon Coghill

Louisiana State University Center for Computation and Technology
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Dr. Chris Taylor

Louisiana Biomedical Research Network
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Dr. Ramesh Subramanian

Louisiana Biomedical Research Network

T-Bio: Easy and Intuitive Data Analysis

By hiding complicated mathematical algorithms behind a user-friendly interface, T-BioInfo enables faster and easier analysis, integration, and visualization of different types of big data. As an answer to a multi-source heterogeneous dataset analysis need, T-BioInfo combines many data types as well as many industry-standard and novel algorithms into flexible, interactive, visual pipelines. The platform is designed to eliminate dependency on bioinformaticians and streamline the way big data is collected, analyzed and interpreted.

The approach to analysis and data mining of Big Biomedical Data, we can assist in drug and vaccine discovery in collaboration with medical and scientific research teams, by integrating heterogenous datasets and visualizing association networks. Learn more

Making Your Next Discovery

Pharmaceutical companies and healthcare organizations are turning to genomic, transcriptomic, proteomic, metabolomic and other “omics” data to diagnose and treat various diseases. From cancer to high blood pressure and dementia, networks of intra-cellular regulations can be monitored by looking at -omics data using technology that is becoming cheaper and more precise every year. Discoveries in this field are leading to new treatments and technologies, but they are just the start of a big data revolution in healthcare that will help cure disease, protect us from microorganisms and find new ways to enable healthier and longer lives.

You too can participate in this discovery by learning the logic of biomedical omics data analysis.