The rapid growth of high-throughput data, including -omics technologies, gave rise to data-driven discovery in life sciences. As a result, there is a significant demand for data science skills and experience with bioinformatics methods of analysis. Once a peripheral discipline you could easily outsource, bioinformatics is quickly becoming mainstream in life science research. Many high impact journals will only publish if a rigorous bioinformatics analysis is included in the results and methods portions of the publication. In this course, we will explain the logic of bioinformatics analysis and allow for user-friendly applications of bio-statistical and machine learning techniques to a variety of biomedical challenges.
Overview of the entire course, Overview of the tools and resources for this program (edu.t-bio.info courses, projects and datasets, and the T-BioInfo Analytics Platform),...
Overview of Processing NGS Data (from raw data to structured data), Standard tools and concepts for genomics, transcriptomics, epigenomics, metagenomics. What kinds of challenges...
Role of pre-processing in standard RNA-seq pipelines (Trimmomatic and PCR-clean) Mapping techniques: mapping on the transcriptome, mapping on the genome and combined strategies (Bowtie,...
Filtering, removing noise and Normalization Techniques Least error Predictive model, Fitting a regression line Regression, factors, and features – Factor Regression Analysis
Review of the first Five-Session, Hands-on training, and support, One-on-one and group meetings & project presentation by Students.
Review of Factor Regression Analysis Unsupervised machine learning (PCA, H-Clust, K-means,) Case study & Hands-on using Cell line project
Review of unsupervised machine learning methods Decision Trees, Random Forest Challenges associated with different kinds of machine learning
Review Supervised ML Discriminant Analysis (LDA, SwLDA, QDA) and Support Vector Machines Data Visualization and classification
Annotation using Gene Ontology Human GAGE: Gene Set Enrichment Analysis Statistical Significance and Reproducibility
Review of the last Five-Session, Hands-on training, and support, One-on-one and group meetings & Project Presentation by Students.
Planning your project, Projects on the Educational Platform. One-on-One interactions: Data access and project abstract Case Studies & Publications Datasets Independent Projects* (Requires Research...