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

What is a Hypothesis?

What is a Hypothesis?

A hypothesis can be a simple sentence or statement about a property or any phenomenon observed or predicted for a population. It is usually a claim about a property of the population. It can be stated for any field observations or experiments. A hypothesis statement cannot be said to be right or wrong as it is merely a statement. It needs to be tested through an elaborate data collection process and an appropriate statistical test. A hypothesis should be a general but not a vague...

Hypothesis Test for Test of Independence

Hypothesis Test for Test of Independence

The test of independence is a chi-square-based test used to determine whether two variables or factors are independent or dependent. This hypothesis test is used to examine the independence of the variables. One can construct two qualitative survey questions or experiments based on the variables in a contingency table. The goal is to see if the two variables are unrelated (independent) or related (dependent). The null and alternative hypotheses for this test are:
H0: The two variables (factors)...

Types of Hypothesis Testing

Types of Hypothesis Testing

There are three types of hypothesis tests: right-tailed, left-tailed, and two-tailed.
When the null and alternative hypotheses are stated, it is observed that the null hypothesis is a neutral statement against which the alternative hypothesis is tested. The alternative hypothesis is a claim that instead has a certain direction. If the null hypothesis claims that p = 0.5, the alternative hypothesis would be an opposing statement to this and can be put either p > 0.5, p < 0.5, or p...

Errors In Hypothesis Tests

Errors In Hypothesis Tests

When performing a hypothesis test, there are four possible outcomes depending on the actual truth (or falseness) of the null hypothesis and the decision to reject or not.

Velocity and Position by Integral Method

Velocity and Position by Integral Method

If acceleration as a function of time is known, then velocity and position functions can be derived using integral calculus. For constant acceleration, the integral equations refer to the first and second kinematic equations for velocity and position functions, respectively.
Consider an example to calculate the velocity and position from the acceleration function. A motorboat is traveling at a constant velocity of 5.0 m/s when it starts to decelerate to arrive at the dock. Its acceleration is...

Statistical Hypothesis Testing

Statistical Hypothesis Testing

Hypothesis testing is a critical statistical procedure facilitating informed, evidence-based decisions. It begins with a hypothesis, which is a tentative explanation, or a prediction about a population parameter. This hypothesis can be either a null hypothesis (H0), indicating no effect or difference, or an alternative hypothesis (Ha), suggesting an effect or difference.
Statistical significance measures the probability that an observed result occurred by chance. If this probability, known as...

You might also read

Related Articles

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

Sort by

Same author

MUFOLD-DB: a processed protein structure database for protein structure prediction and analysis.

BMC genomics·2015

Same author

The I-TASSER Suite: protein structure and function prediction.

Nature methods·2014

Same author

Genome-wide expression analysis of soybean NF-Y genes reveals potential function in development and drought response.

Molecular genetics and genomics : MGG·2014

Same author

Classification of lung cancer using ensemble-based feature selection and machine learning methods.

Molecular bioSystems·2014

Same author

Resveratrol possesses protective effects in a pristane-induced lupus mouse model.

PloS one·2014

Same author

Protein-losing enteropathy in systemic lupus erythematosus: 12 years experience from a Chinese academic center.

PloS one·2014

Search research articles

Related Experiment Video

Updated: Jan 23, 2026

Drug Repurposing Hypothesis Generation Using the "RE:fine Drugs" System

Drug Repurposing Hypothesis Generation Using the "RE:fine Drugs" System

Published on: December 11, 2016

IMPRes-Pro: A high dimensional multiomics integration method for in silico hypothesis generation.

Yuexu Jiang¹, Duolin Wang¹, Dong Xu²

¹Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO 65211, USA; Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, MO 65211, USA.

Methods (San Diego, Calif.)

|June 21, 2019

Summary

We enhanced the IMPRes algorithm to IMPRes-Pro, integrating multi-omics data for better pathway analysis. This improves in silico hypothesis generation for drug design and personalized cancer therapy strategies.

Keywords:

Data integrating Dynamic programming Graph theory Multiomics Pathway analysis Shortest path

More Related Videos

Analysis of Group IV Viral SSHHPS Using In Vitro and In Silico Methods

Analysis of Group IV Viral SSHHPS Using In Vitro and In Silico Methods

Published on: December 21, 2019

In Silico Clinical Trials for Cardiovascular Disease

In Silico Clinical Trials for Cardiovascular Disease

Published on: May 27, 2022

Related Experiment Videos

Last Updated: Jan 23, 2026

Drug Repurposing Hypothesis Generation Using the "RE:fine Drugs" System

Drug Repurposing Hypothesis Generation Using the "RE:fine Drugs" System

Published on: December 11, 2016

Analysis of Group IV Viral SSHHPS Using In Vitro and In Silico Methods

Analysis of Group IV Viral SSHHPS Using In Vitro and In Silico Methods

Published on: December 21, 2019

In Silico Clinical Trials for Cardiovascular Disease

In Silico Clinical Trials for Cardiovascular Disease

Published on: May 27, 2022

Area of Science:

Bioinformatics
Computational Biology
Systems Biology

Background:

Generating large omics datasets presents challenges in identifying active biological pathways and extracting meaningful insights due to noise and background information.
Existing informatics tools can detect biologically relevant modules but struggle with interpretation for in silico hypothesis generation and testing.
The Integrative MultiOmics Pathway Resolution (IMPRes) v1.0 algorithm was previously developed to address these limitations using a dynamic programming approach for step-wise active pathway detection.

Purpose of the Study:

To present IMPRes-Pro, an enhanced version of the IMPRes algorithm.
To integrate proteomics and transcriptomics data using a heterogeneous background network for improved pathway analysis.
To demonstrate the advantages of IMPRes-Pro over IMPRes v1.0 and provide insights for optimal therapy strategies.

Main Methods:

Integration of transcriptomics and proteomics data.
Construction of a heterogeneous background network.
Application of a dynamic programming approach for step-wise active pathway detection.

Main Results:

The enhanced IMPRes-Pro method demonstrated advantages over the original IMPRes v1.0.
Evaluation on a human primary breast cancer dataset confirmed the improved performance.
A case study on human metastatic breast cancer dataset yielded insights for selecting optimal therapy strategies.

Conclusions:

IMPRes-Pro offers an advanced approach for analyzing multi-omics data to identify active pathways.
The algorithm facilitates in silico hypothesis generation, aiding in more accurate drug design and effective treatment strategies.
The IMPRes-Pro algorithm and visualization tool are available as a web service for broader accessibility.