Genomic Data Analysis: Microarray Technology

Related lectures (30)

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Explores the Drop Seq method for single-cell mRNA sequencing and compares it with RNA Microarray.

Explains transcriptomics, RNA-seq workflow, and gene expression analysis for disease biomarkers.

Introduces statistics basics, including data analysis and probability theory, emphasizing central tendency, dispersion, and distribution shapes.

Introduces the basics of supervised machine learning, covering types, techniques, bias-variance tradeoff, and model evaluation.

Explores generalization theory in machine learning, addressing challenges in higher-dimensional spaces and the bias-variance tradeoff.

Explores the Bias-Variance tradeoff in machine learning, emphasizing the balance between bias and variance in model predictions.

Discusses estimation methods in probability and statistics, focusing on maximum likelihood estimation and confidence intervals.

Explores nonparametric statistics, covering estimation methods and the bias-variance tradeoff.

Explores bias, variance, and confidence intervals in parameter estimation using examples and distributions.

Covers nonparametric regression, scatterplot smoothing, kernel methods, and bias-variance tradeoff.