Calibration Curves in Quantitative Ligand Binding Assays

About
Privacy
Disclaimer

Graph Chatbot

In course

Proident aliquip ut aliquip irure magna anim sit esse do et ut ad nisi qui. Minim Lorem cillum officia dolor qui occaecat veniam laborum ipsum duis. Culpa nulla consequat officia eu.

Description

This lecture discusses the importance of calibration curves in ligand binding assays, focusing on preparation, design, and editing. It covers recommendations for curve preparation, regression models, weighting, and accuracy profiles. The presentation emphasizes the critical role of calibration curves in ensuring the accuracy of sample quantitation.

Instructor

reprehenderit aute ex nulla

Laborum eu amet sit cillum eu consectetur. Amet cillum eiusmod eu dolor officia magna eiusmod nostrud duis anim qui ipsum voluptate minim. Occaecat exercitation amet consequat deserunt officia adipisicing et est cupidatat voluptate nostrud. Elit qui nostrud culpa pariatur do. Do mollit duis irure minim ex consequat reprehenderit dolor reprehenderit. Cillum in veniam anim irure Lorem velit excepteur in et quis. Irure adipisicing proident non occaecat et anim veniam.

Official source

Related lectures (31)

https://mediaspace.epfl.ch/media/0_kijt2gxm

About this result

This page is automatically generated and may contain information that is not correct, complete, up-to-date, or relevant to your search query. The same applies to every other page on this website. Please make sure to verify the information with EPFL's official sources.

In course

DEMO: officia sit

Proident aliquip ut aliquip irure magna anim sit esse do et ut ad nisi qui. Minim Lorem cillum officia dolor qui occaecat veniam laborum ipsum duis. Culpa nulla consequat officia eu.

Instructor

reprehenderit aute ex nulla

Related lectures (31)

Probabilistic Linear Regression

Explores probabilistic linear regression, covering joint and conditional probability, ridge regression, and overfitting mitigation.

Nonlinear ML Algorithms

Introduces nonlinear ML algorithms, covering nearest neighbor, k-NN, polynomial curve fitting, model complexity, overfitting, and regularization.

Overfitting, Cross-validation, Regularization

Explores overfitting, cross-validation, and regularization in machine learning, emphasizing model complexity and the importance of regularization strength.

Supervised Learning in Financial Econometrics

Explores supervised learning in financial econometrics, covering linear regression, model fitting, potential problems, basis functions, subset selection, cross-validation, regularization, and random forests.

Linear Regression and Logistic Regression

Covers linear and logistic regression for regression and classification tasks, focusing on loss functions and model training.