Biomedical Data Science in R (1)

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. Throughout the course, students will get an understanding of the 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 the 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 the training will be answered by our team.

Register for Biomedical Data Science in R (Webinar and 1-month Mentor Guided Program)

Data Science methods to integrate multi omics data in the area of oncology & various disorders

Machine Learning View of Multi-Omics Data Integration

R & Python - Genomics & Next Generation Sequencing (NGS) Data Analysis - Dr. Harpreet Kaur

Biomedical Data Science in R
What's included?

Choose a tenure that supports your needs and join a community of our program experts!

 

45 Days

Coding Sessions
Hands-on Assignments
Group Sessions
Session Recordings
Omicslogic Program Access
Example Cloud Pipelines
Online Mentor Support
Online Technical Support

OUTCOME:

⭐️ Certification of Completion

⭐️ Job Opportunities

60 Days

Coding Sessions
Hands-on Assignments
Group Sessions
Online Mentor Support
Omicslogic Program Access
Education Cloud Pipelines
One-on-One Mentor Sessions

OUTCOME:

⭐️ Certificate of Completion

⭐️ Job Opportunity

⭐️ Research Poster

90 Days

Coding Sessions
Hands-on Assignments
Group Sessions
Online Mentor Support
Omicslogic Program Access
Education Cloud Pipelines
Research Project

OUTCOME:

⭐️ Certificate of Excellence

⭐️ Job Opportunities

⭐️ Research Publication

Payment checkout links (Biomedical Data Science in R)

Note: For Low-Income Countries scholarship opportunities are available, please reach out to us and our team at: marketing@omicslogic.com and we will find the best possible way for you to participate and get the best outcomes from the training program.

We also have the option for financial assistance with weekly/monthly installment options and payment options according to your country (e.g., INR, etc.)

WhatsApp.svg+91-9876134120

Key Topics Covered:

  Big data, HPC and cloud computing

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.

 NGS: omics data types and use cases

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.

 Computational pipelines for data processing

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.

 Introduction to Programming: R 
Online bioinformatics coding exercises to learn and explore R 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
Principal Component Analysis - Exploratory Data Analysis

 

Data Wrangling and Exploratory Data Analysis

Machine Learning and Statistical Analysis

 

Statistical and Machine Learning Approaches

R studio - Coding Environment for Code Development

 

Coding Environments and Best Practices in R

Research Projects and Case Studies with curated Data Sets

 

Introduction to Case Studies using Research Omics Data

 

Program Syllabus: Biomedical Data Science in R

Session Title Description

   

Introduction to the program

  • Introduction to Bioinformatics & Data Science
  • Publicly Available Data Repositories
  • Overview of key topics that will be covered, including:
  • Data loading and preparation
  • Data visualization in R 
  • Statistical Concepts and Tests
  • Supervised and Unsupervised Machine Learning

Associated Online Resources: 


 

Processing high throughput (BIG) data

  • Data complexity and need for preparation
  • Availability and variability of data
  • Unprecedented detail and volume
  • Data heterogeneity, complexity, and noise
  • Publicly available data repositories
  • Omics Data: High Throughput Automated Technologies
  • Types of Omics Data: Genomic Data, Transcriptomic Data, Metagenomic Data
  • Need for interpretability and reproducibility/ Limitations of statistical analysis

Associated Online Resources:

Major Types of Machine Learning Methods

  • Data Exploration & Visualization
  • Statistical Analysis/ Types of Machine Learning
  • Data Mining & Classification
  • Unsupervised Machine Learning: Data Mining, Dimensionality Reduction, clustering , 
  • Hierarchical and K-means clustering/ Supervised Machine Learning: Classification

Associated Online Resources:

Machine Learning for Data visualization


Unsupervised Learning: Clustering

  • Patterns and Learning/ Clustering for data mining
  • Exploratory analysis: PCA samples, features, components & Noise
  • Clustering Algorithms & Linkage/ K-means and Hierarchical clustering
  • Big Data Clustering (CLARA, PAM, fuzzy)/ Clustering samples vs features
  • Practical Hands-on with google colab notebook

Associated Online Resources:

 

Supervised Learning: Classification

  • Overview of supervised learning/ Preparing Training and test datasets
  • Binary Decision Trees/ Random Forest (RF)
  • Discriminant Analysis: LDA and QDA/ Support Vector Machine (SVM)
  • Validation of Model Accuracy 
  • Working with R Google Colab Notebook 

Associated Online Resources

Combining/selecting ML methods

  • Objectives of real-world ML projects:/ Exploratory Analysis
  • Data Visualization/ Statistical/ Data Mining
  • Predictive Analysis/ Feature Selection
  • Working with Python Google Colab Notebook
  • Feature Selection & Regression - Classification Model Comparison
  • GLM Classification Assignment

Associated Online Resources


Session 8 (1)

Selecting a project for ML analysis

  • Designing a Bioinformatics Research Project
  • Gene Expression (Transcriptomics)
  • Variant Analysis (Genomics)
  • Microbiome Diversity (Metagenomics)
  • Basic Research, Clinical

Associated Online Resources:

Designing a Bioinformatics Research Project

 

 

Register for the Online Mentor Guided Training Program

Payment checkout links (Biomedical Data Science in R)

Note: For Low-Income Countries scholarship opportunities are available, please reach out to us and our team at: marketing@omicslogic.com and we will find the best possible way for you to participate and get the best outcomes from the training program.

We also have the option for financial assistance with weekly/monthly installment options and payment options according to your country (e.g., INR, etc.)

WhatsApp.svg+91-9876134120

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
Abubkar
"It was very informative and easy to learn. The content was concise and the provided articles can be used for deeper understanding. Overall this lesson did a great job at introducing bioinformatics".
- Abubakar Abdulkadir, Postgraduate Student
LSU student
"I enjoyed the lessons and look forward to learning more.It is a great documentation for beginners. For anyone starting afresh, I’d highly recommend these courses. Examples and resources are really useful".
- Wellesley Dittmar, Graduate Student
chinmay
"The modules were quiet a good opportunity to work with different supervised ML models. As I am not from the statistics or machine learning background but was able to grasp an overview of the same.'.
- Chinmay Dalvi, Senior Research Associate