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The rapid growth of high-throughput data, including -omics technologies, gave rise to significant demand for data science skills and experience with bioinformatics methods of analysis. This online training program will cover practical and conceptual aspects of data science, including data wrangling, statistical analysis, and machine learning in application to high-throughput biomedical omics data using big data analysis tools on the T-BioInfo Platform, R and Python. Throughout the course, students will get an understanding of opportunities and limitations of machine learning in the context of basic pre-clinical and clinical research.
The training is designed as a combination of online resources, practical assignments and live workshops that will be conducted via ZOOM. Throughout the course, we will review several project examples that demonstrate successes and limitations of conventional machine learning (ML) methods and deep learning (DL) using data from public repositories. To learn more, we welcome you to review the information on this page and register for an upcoming webinar where the program topics will be introduced and questions about he training will be answered by our team.
Key Topics Covered:
Many types of omics data require step-by-step preparation, exploration, annotation, and visualization to understand. The T-BioInfo platform was designed for big multi-omics data analysis hiding the complexities of data with a user-friendly and intuitive interface that eliminates the need for coding and advanced machine learning algorithms for data integration and mining.
A program that embeds data-driven concepts into biological projects, spanning the student learning journey from observer to participant in research. Project-based learning for big data bioinformatics is to go beyond the theory with real datasets, projects, and expert mentors. Work with curated datasets from publicly available repositories with easy-to-follow tutorials.
Data processing for Next Generation Sequencing, Mass-Spectroscopy, Structural and phenotypic data. Build and adapt pipelines using similar approaches to data mapping, quantification, and annotation that are used to prepare data for downstream statistical analysis, train machine learning models and annotate features.
Online bioinformatics coding exercises to learn and explore R and Python scripting and understand how to analyze and visualize -omics data to extract meaningful insights from large biological datasets. Learn, practice, and achieve bioinformatics greatness with concise exercises and interesting challenges right in the comfort of your browser!
A Complete Data Science Crash Course with Hands-on Training and curated Case Study Datasets | |||
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Data Wrangling and Exploratory Data Analysis |
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Statistical and Machine Learning Approaches |
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Coding Environments and Best Practices in R and Python |
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Introduction to Case Studies using Research Omics Data |
Program Syllabus : Data Science for Biomedical Data
Session Title | Description |
FEBRUARY 04, 2022 |
Introduction to the program
Associated Online Resources
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FEBRUARY 11, 2022
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Processing high throughput (BIG) data
Associated Online Resources
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FEBRUARY 18, 2022 |
Major Types of Machine Learning Methods
Associated Online Resources
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FEBRUARY 25, 2022 |
Machine Learning for Data visualization
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FEBRUARY 25, 2022 at 1 PM CST |
LIVE QnA Session |
MARCH 04, 2022 |
Unsupervised Learning: Clustering
Associated Online Resources
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MARCH 11, 2022 |
Supervised Learning: Clustering
Associated Online Resources |
MARCH 18, 2022 |
Feature selection and gene signature construction
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MARCH 21, 2022 |
Regression and generalized linear models (GLM)
Associated Online Resources - |
MARCH 21, 2022 at 1 PM CST |
LIVE QnA Session |
MARCH 25, 2022 |
Network analysis
Associated Online Resources |
MARCH 28, 2022 |
Combining/selecting Machine Learning methods
Associated Online Resources |
APRIL 01, 2022 |
Deep Learning: Types & Application
Associated Online Resources |
APRIL 08, 2022 |
Selecting a project for ML analysis
Associated Online Resources |
APRIL 08, 2022 at 1 PM CST |
LIVE QnA Session |
If you need help finalizing registration, contact Farhana Musarrat (email: fmusar1@lsu.edu). You have to register for the orientation using the form below. For LSU or LBRN members, you can complete your registration via BIOMMED iLab link below -
Register for the Online Mentor Guided Training Program

Training Certificate from the Louisiana Biomedical Research Network or LSU BioMMED:
- Certification of Training Requirement Completion
- Recognition within the Network and Other IDeA States
- Advancement for Research, Faculty and Student participants within the LBRN Network
- Training certification for LSU through the Center for Biotechnology and Biomolecular Medicine at the Louisiana State University
A Complete Overview
Browser-based, hands-on experience with big data
Learn, practice and gain experience independent of your technological limitations, including:
- R and Python browser-based data analysis and code review'
- Process and Analyze large-scale Omics data
- Create reproducible workflows for data processing, analysis and integration
- Apply methods to curated datasets from peer-reviewed journals


