Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Statistical Analysis System (SAS)

Statistical Analysis System (SAS)

SAS, short for Statistical Analysis System, is a powerful data analysis, management, and visualization tool. Developed by the SAS Institute in the early 1970s, SAS has evolved into a comprehensive software suite used across various industries for statistical analysis, business intelligence, and predictive modeling.
Applications: SAS finds applications in numerous fields, including healthcare for clinical trial analysis, finance for risk assessment, marketing for customer data analysis, and...

Structure-Activity Relationships and Drug Design

Structure-Activity Relationships and Drug Design

Drug design is a dynamic field that involves discovering and developing new medications based on specific biological targets. This process heavily relies on structure-activity relationships (SAR) and quantitative structure-activity relationships (QSAR) to guide the design and optimization of efficient drugs.
SAR studies the intricate relationship between a drug's chemical structure and biological activity. It focuses on understanding how modifications to a drug's structure can influence its...

Response Surface Methodology

Response Surface Methodology

Response Surface Methodology (RSM) is a collection of statistical and mathematical techniques used to develop, improve, and optimize processes. It is particularly valuable when many input variables or factors potentially influence a response variable.
The process of RSM involves several key steps:

Statistical Software for Data Analysis and Clinical Trials

Statistical Software for Data Analysis and Clinical Trials

Statistical software is pivotal in data analysis and clinical trials by providing tools to analyze data, draw conclusions, and make predictions. These software packages range from simple data management applications to complex analytical platforms, supporting various statistical tests, models, and simulation techniques. Their significance lies in their ability to handle vast amounts of data with precision and efficiency, enabling researchers to validate hypotheses, identify trends, and make...

Cancer Survival Analysis

Cancer Survival Analysis

Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...

Introduction To Survival Analysis

Introduction To Survival Analysis

Survival analysis is a statistical method used to study time-to-event data, where the "event" might represent outcomes like death, disease relapse, system failure, or recovery. A unique feature of survival data is censoring, which occurs when the event of interest has not been observed for some individuals during the study period. This requires specialized techniques to handle incomplete data effectively.
The primary goal of survival analysis is to estimate survival time—the time until a...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

HFB301001, an OX40-based immunotherapy, drives Treg clearance and CTL activation through optimized OX40 receptor clustering.

Journal for immunotherapy of cancer·2026

Same author

Population pharmacokinetics and pharmacodynamics of HFB30132A, a monoclonal antibody against SARS-CoV-2, in healthy Chinese and US subjects.

International journal of antimicrobial agents·2025

Same author

Exploration of macrocyclic peptide binders to the extracellular CRD domain of human receptor tyrosine kinase-like orphan receptor 1 (ROR1).

Bioorganic & medicinal chemistry letters·2023

Same author

Drug Intelligence Science (DIS®): Pioneering a high-resolution translational platform to enhance the probability of success for drug discovery and development.

Drug discovery today·2023

Same author

Safety, tolerability, pharmacokinetics, and immunogenicity of an anti-SARS-CoV-2 monoclonal antibody HFB30132A after single dose intravenous administration in healthy Chinese subjects: a phase 1, randomized, double-blind, placebo-controlled study.

Frontiers in pharmacology·2023

Same author

Global or local: The future of biotech.

Drug discovery today·2023

Search research articles

Related Experiment Video

Updated: May 29, 2026

Expedited Radiation Biodosimetry by Automated Dicentric Chromosome Identification (ADCI) and Dose Estimation

Expedited Radiation Biodosimetry by Automated Dicentric Chromosome Identification (ADCI) and Dose Estimation

Published on: September 4, 2017

SDRS--an algorithm for analyzing large-scale dose-response data.

Rui-Ru Ji¹, Nathan O Siemers, Ming Lei

¹Department of Applied Genomics, Bristol-Myers Squibb, Princeton, NJ 08543, USA. ruiruji@gmail.com

Bioinformatics (Oxford, England)

|August 26, 2011

Summary

We developed the Sigmoidal Dose Response Search (SDRS) algorithm to analyze large-scale dose-response data, overcoming limitations in current methods. This tool enables detailed pharmacological analysis and transcriptome-level dose-response characterization.

More Related Videos

Efficient Sampling of Genetically Encoded Biosensor Design Space Enabled with a Design of Experiments and Automation Workflow

Efficient Sampling of Genetically Encoded Biosensor Design Space Enabled with a Design of Experiments and Automation Workflow

Published on: October 17, 2025

Related Experiment Videos

Last Updated: May 29, 2026

Expedited Radiation Biodosimetry by Automated Dicentric Chromosome Identification (ADCI) and Dose Estimation

Expedited Radiation Biodosimetry by Automated Dicentric Chromosome Identification (ADCI) and Dose Estimation

Published on: September 4, 2017

Efficient Sampling of Genetically Encoded Biosensor Design Space Enabled with a Design of Experiments and Automation Workflow

Efficient Sampling of Genetically Encoded Biosensor Design Space Enabled with a Design of Experiments and Automation Workflow

Published on: October 17, 2025

Area of Science:

Pharmacology
Bioinformatics
Computational Biology

Background:

Dose-response data is crucial for understanding drug effects.
Existing analytical methods struggle with the high dimensionality of large-scale screening data, like microarray data.

Purpose of the Study:

To develop a novel algorithm for analyzing large-scale dose-response data.
To enable comprehensive pharmacological parameter calculation and statistical analysis.

Main Methods:

Implementation of the Sigmoidal Dose Response Search (SDRS) algorithm.
A grid search-based approach designed for high-throughput dose-response assays.
Calculation of pharmacological parameters for individual assays.

Main Results:

The SDRS algorithm effectively handles large-scale dose-response data.
Provides built-in statistics for downstream systematic analyses.
Enables characterization of dose response at the transcriptome level.

Conclusions:

The SDRS algorithm addresses the limitations of current methods for analyzing large-scale dose-response data.
Facilitates a deeper understanding of drug effects and molecular responses.
Offers a robust tool for pharmacological research and transcriptomic analysis.