Bioinformatics is a discipline that combines biology, statistics, and computer science. Data analysis skills are essential for modern biology and are at the core of scientific discovery. In this bioinformatics training program, we will explore bioinformatics concepts in a project setting, offering insights into the typical problems addressed by a bioinformatician and explaining the analysis logic. The program does not require any prerequisites, apart from basic biology concepts. The program will span for three months, comprising eleven workshops and one Team Project.
Day 1 Introduction to OmicsLogic at Amity University, Kolkata: Mohit Mazumder & Pratim Chakraborty, July 19th, 2019 Introduction and commencement of the program by...
Day 1 Transcriptomics 2, Pratim Chakraborty, August 5th, 2019 Filtering, removing noise and Normalization Techniques Exploring multi-dimensional data using PCA visualization Principal Components...
Introduction to multi-omics (Definitions), Knowledge-Driven vs. Data-Driven Discovery and Examples, Applications of Bioinformatics in Agriculture, Neuroscience, Cancer Biology, Immunology, Defense and Healthcare
Cell, Nucleus and Chromosomes The DNA molecule and its structure Genome variations: A detailed understanding Targeted Sequencing, Whole Exome Sequencing and Whole Genome Sequencing...
Day 1 Introduction to Machine Learning, Mohit Mazumder, September 2nd, 2019 What is machine learning, categories of methods Regression, factors, and features – Factor...
Filtering, removing noise and Normalization Techniques, Exploring multi-dimensional data using PCA visualization, Principal Components and variance – outliers, filtering, normalization
Role of Genomics and Transcriptomics in Modern Biology.
What is Metagenomics and why study Metagenomics Microbiome and taxonomy The Gut Microbiota and Human Health NGS of the Metagenome and 16S metagenomic data...
What is machine learning, categories of methods Regression, factors, and features – Factor Regression Analysis Challenges associated with different kinds of machine learning
Decision Trees, Discriminant Analysis and Support Vector Machines, Feature Selection and expanding the list of features, Data Visualization
Identifying the correct dataset(s) for your bioinformatics project Input and output of the typical processing and analysis steps Team Project selection...
Multi-Omics Data Analysis in Cancer Research
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