Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Inductive Reasoning00:59

Inductive Reasoning

60.5K
Inductive reasoning is a form of logical thinking that uses related observations to arrive at a general conclusion. It is uncertain and operates in degrees to which the conclusions are credible. As such, inductive arguments can be weak or strong, rather than valid or invalid, and conclusions can be used to formulate testable, falsifiable hypotheses.
Inductive reasoning is common in descriptive science. A life scientist makes observations and records them. This data can be qualitative or...
60.5K
Observational Learning01:12

Observational Learning

193
Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning...
193
Quantitative Aspects of Drug-Receptor Interaction01:30

Quantitative Aspects of Drug-Receptor Interaction

1.0K
The receptor occupancy theory connects a drug's response to the number of occupied receptors. With higher drug concentrations, more receptors are occupied, leading to increased responses. The formation of drug-receptor complexes involves association and dissociation rates, which reach equilibrium when the forward and backward reactions are equal. The equilibrium association constant (Ka) and its inverse, the equilibrium dissociation constant (Kd), indicate drug affinity. Higher Ka and lower...
1.0K
Deductive Reasoning01:16

Deductive Reasoning

55.3K
Deductive reasoning, or deduction, is the type of logic used in hypothesis-based science. In deductive reasoning, the pattern of thinking moves in the opposite direction as compared to inductive reasoning, which means that it uses a general principle or law to predict specific results. From those general principles, a scientist can deduce and predict the specific results that would be valid as long as the general principles are valid.
For example, a researcher can deduce specific predictions...
55.3K
Natural and Artificial Concepts01:24

Natural and Artificial Concepts

174
In psychology, concepts can be divided into two categories: natural and artificial. Natural concepts are formed through direct or indirect experiences. For example, consider the concept of snow. If you live in a place with regular snowfall, such as Essex Junction, Vermont, you know snow through direct experiences. You’ve seen it fall, touched it, shoveled it, and played in it. You recognize its texture, appearance, and even its smell. In contrast, if you live on an island like Saint...
174
Quantitative Analysis01:12

Quantitative Analysis

317
Quantitative analysis is a technique for measuring the amount of specific constituents in a sample. When the sample's composition is unknown, qualitative analysis is performed first to identify its components, which ensures that the correct substances are measured during the quantitative phase.
In quantitative analysis, two key measurements are made: the sample quantity and a property proportional to the amount of the analyte (the substance being analyzed). This forms the basis of the...
317

You might also read

Related Articles

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

Sort by
Same author

Recovering Reward Functions From Distributed Expert Demonstrations via Bi-Level Maximum-Likelihood Optimization.

IEEE transactions on neural networks and learning systems·2026
Same author

The crossroads between osteosarcopenia and intrinsic capacity-a narrative review.

The journals of gerontology. Series A, Biological sciences and medical sciences·2026
Same author

The interplay between osteosarcopenia and intrinsic capacity: insights and associations with all-cause mortality in the Toledo Study for Healthy Aging.

The journals of gerontology. Series A, Biological sciences and medical sciences·2026
Same author

Bayesian Topology Inference of Regulatory Networks under Partial Observability.

Results in control and optimization·2026
Same author

Pareto-Optimal Interventions in Gene Regulatory Networks using Signal Temporal Logic.

Proceedings of the ... American Control Conference. American Control Conference·2026
Same author

Deep Reinforcement Learning for Intervention of Partially Observable Regulatory Networks.

Proceedings of the ... American Control Conference. American Control Conference·2026
Same journal

From Patient Consultations to Graphs: Leveraging LLMs for Patient Journey Knowledge Graph Construction.

... IEEE Conference on Artificial Intelligence·2025
Same journal

Learning to Fight Against Cell Stimuli: A Game Theoretic Perspective.

... IEEE Conference on Artificial Intelligence·2023
See all related articles

Related Experiment Video

Updated: Jul 14, 2025

Quantifying Learning in Young Infants: Tracking Leg Actions During a Discovery-learning Task
11:18

Quantifying Learning in Young Infants: Tracking Leg Actions During a Discovery-learning Task

Published on: June 1, 2015

10.7K

Structure-Based Inverse Reinforcement Learning for Quantification of Biological Knowledge.

Amirhossein Ravari1, Seyede Fatemeh Ghoreishi2, Mahdi Imani1

  • 1Department of Electrical and Computer Engineering at Northeastern University.

... IEEE Conference on Artificial Intelligence
|October 6, 2023
PubMed
Summary
This summary is machine-generated.

This study quantifies biologist policies in gene regulatory networks (GRNs) using a novel machine learning approach. The method effectively handles biological data uncertainty, improving understanding of complex cellular processes and diseases.

More Related Videos

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

255
Automating Aggregate Quantification in Caenorhabditis elegans
07:50

Automating Aggregate Quantification in Caenorhabditis elegans

Published on: October 14, 2021

2.8K

Related Experiment Videos

Last Updated: Jul 14, 2025

Quantifying Learning in Young Infants: Tracking Leg Actions During a Discovery-learning Task
11:18

Quantifying Learning in Young Infants: Tracking Leg Actions During a Discovery-learning Task

Published on: June 1, 2015

10.7K
Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

255
Automating Aggregate Quantification in Caenorhabditis elegans
07:50

Automating Aggregate Quantification in Caenorhabditis elegans

Published on: October 14, 2021

2.8K

Area of Science:

  • Computational Biology
  • Systems Biology
  • Bioinformatics

Background:

  • Gene regulatory networks (GRNs) are fundamental to cellular processes like stress response, DNA repair, and disease mechanisms.
  • Understanding biologist policies within GRNs is crucial for deciphering complex biological systems.
  • Current machine learning, especially inverse reinforcement learning, faces challenges due to biological data limitations and uncertainty.

Purpose of the Study:

  • To develop a method for quantifying biologist policies from biological data.
  • To address the limitations and uncertainty inherent in biological data for machine learning applications.
  • To leverage the network structure of GRNs for efficient policy quantification.

Main Methods:

  • Utilized the network-like structure of gene regulatory networks (GRNs).
  • Defined expert reward functions with significantly fewer parameters than traditional models.
  • Applied inverse reinforcement learning techniques adapted for biological data.

Main Results:

  • Demonstrated superior performance in quantifying biologist policies.
  • Successfully handled limitations and uncertainty in biological data.
  • Validated the method using mammalian cell cycle and synthetic gene-expression data.

Conclusions:

  • The proposed method offers a more efficient way to quantify biologist policies in GRNs.
  • This approach enhances the understanding of complex biological systems and disease mechanisms.
  • The findings pave the way for improved integration of expert knowledge into biological data analysis.