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.

To learn more, we welcome you to explore the topics on this page and join the orientation session on

January 20, 2022 at 5 PM CST

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 and Python
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!

Program Syllabus : Data Science for Biomedical Data

Session Title	Description
	Introduction to the program Overview of key topics that will be covered, including: Data loading and preparation Data visualization in R and Python Statistical Concepts and Tests Supervised and Unsupervised Machine Learning Network Analysis and Deep Learning Associated Online Resources Loading data in R Getting Started BioML1: Introduction to Machine Learning in Biomedical Data
	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 Need for interpretability and reproducibility Limitations of statistical analysis Associated Online Resources BioML1: High Throughput Data & Technology BioML1: Data Preparation for ML
	Major Types of Machine Learning Methods Unsupervised and supervised types of analysis: Dimensionality Reduction, clustering and classification Hierarchical and K-means clustering Big Data Clustering (CLARA, PAM, fuzzy) Conventional Regression-based methods Random Forest and SVM Deep Learning Associated Online Resources BioML1: Data Descriptive Statistics & Normalization BioML1: Data for Machine learning
	Machine Learning for Data visualization Dimensionality Reduction Objectives Continuous and Ordinal Data Multi-variate statistical analysis approaches Variance and co-variance Singular Value Decomposition (SVD) Principal Component Analysis, Coordinate Analysis and NMDS tSNE and UMAP Associated Online Resources Data Processing & visualization Advanced Data Visualization
	Unsupervised Learning: Clustering Patterns and Learning Clustering for data mining PCA and clustering K-means and Hierarchical clustering Big Data Clustering (CLARA, PAM, fuzzy) Clustering objects and features Feature Engineering Associated Online Resources BioML2: Unsupervised Machine Learning Overview Dimensionality reduction: PCA & visualization (Using R) Data visualization using t-SNE (Using R) Dimensionality reduction: PCA & visualization (Using Python)
	Supervised Learning: Clustering Overview of supervised learning Preparing Training and test datasets Binary Decision Trees Random Forest (RF) Support Vector Machine (SVM) Discriminant Analysis: LDA and QDA Model Accuracy and Specificity Associated Online Resources BioML3: Introduction to Supervised Machine Learning BioML3: Supervised Machine Learning methods
	Feature selection and gene signature construction Need for feature selection Feature selection for tSNE Feature selection for biomarker discovery Methods for feature significance Algorithms and approaches (greedy) Technical accuracy (ROC curve and AUC) Logical or biological relevance Automating the process Associated Online Resources BioML2: Dimensionality Reduction Methods BioML2: Clustering Methods BioML3: Data Partition and Feature Selection
	Regression and generalized linear models (GLM) Factor Regression Analysis and interpretation Using regression for missing data Data distributions (non-normal) Generalized Linear Models (GLM) Logistic Regression interpretation Associated Online Resources - Statistical Analysis Tests Regression & Factor Regression Analysis
	Network analysis Objectives of Network Analysis Signaling and Metabolic Pathways Gene Expression Networks Time-series data Overview of Bayesian Network Examples of Network analysis Associated Online Resources BioML2: Bayesian Network Method Differential Gene Expression & Gene Enrichment Analysis
	Combining/selecting Machine Learning methods Objectives of real-world ML projects: Exploratory Analysis Data Visualization Statistical/Data Mining Predictive Analysis Feature Selection Associated Online Resources Machine Learning: Classification BioML3: Data Partition and Feature Selection
	Deep Learning: Types & Application Introduction to Deep Learning Models and Approaches Multi-Layer Perceptrons (MLP) Convolutional Neural Networks (CNN) Recurrent Neural Networks (RNN) Dimensionality Reduction with Deep Learning Model Optimization Associated Online Resources Dimensionality Reduction with Deep Learning Predictive Models with Deep Learning
	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 Introduction to Metagenomics
	Use-cases in clinical applications Patient Derived Xenografts (PDX) Modeling Precision Medicine for Breast Cancer Biomarker Selection in Alzheimer's Disease Relationship between Geography and Genomic Variation Associated Online Resources Patient Derived Xenograft Models Modeling Cancer Precision Medicine
	Use-cases in industry applications TCGA Liver Cancer Project Microbiome of Skin and gut Treatment Selection Target identification Associated Online Resources TCGA Liver Cancer Project Precision Oncology Spodoptera frugiperda (fall armyworm)