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

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
Synthetic Biology02:55

Synthetic Biology

Synthetic biology is an interdisciplinary science that involves using principles from disciplines such as engineering, molecular biology, cell biology, and systems biology. It involves remodeling existing organisms from nature or constructing completely new synthetic organisms for applications such as protein or enzyme production, bioremediation, value-added macromolecule production, and the addition of desirable traits to crops, to name a few.
Golden rice
Golden rice is a genetically modified...
Classification of Systems-I01:26

Classification of Systems-I

Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least squares (OLS)...
Classification of Systems-II01:31

Classification of Systems-II

Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence of...

You might also read

Related Articles

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

Sort by
Same author

Improved Small Molecule Identification through Learning Combinations of Kernel Regression Models.

Metabolites·2019
Same author

Temporal clustering analysis of endothelial cell gene expression following exposure to a conventional radiotherapy dose fraction using Gaussian process clustering.

PloS one·2018
Same author

Parameter estimation of perfusion models in dynamic contrast-enhanced imaging: a unified framework for model comparison.

Medical image analysis·2016
Same author

Fast metabolite identification with Input Output Kernel Regression.

Bioinformatics (Oxford, England)·2016
Same author

Collaborative analysis of multi-gigapixel imaging data using Cytomine.

Bioinformatics (Oxford, England)·2016
Same author

gammaMAXT: a fast multiple-testing correction algorithm.

BioData mining·2015
Same journal

Abstracts from Specialized Centers of Research Excellence (SCORE) on Sex Differences 2025 annual meeting.

BMC proceedings·2026
Same journal

Conference abstracts the 1st UDOM scientific conference on health: healthy lives and well-being for all: opportunities and challenges.

BMC proceedings·2026
Same journal

Entrepreneurship beyond the lab: commercializing your creative outputs.

BMC proceedings·2026
Same journal

The need to strengthen laboratory leadership, systems, and networks to enhance outbreak detection and resilience in Africa: proceedings of a regional workshop.

BMC proceedings·2026
Same journal

Abstracts from the Globesync Community Research and Sustainability 2025 (GlobeCoReS 2025).

BMC proceedings·2026
Same journal

Bauru International Craniofacial Symposium: Comprehensive Care, Policy and Advocacy Proceedings.

BMC proceedings·2026
See all related articles

Related Experiment Video

Updated: Jun 27, 2026

Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

Machine learning in systems biology.

Florence d'Alché-Buc1, Louis Wehenkel

  • 1IBISC CNRS FRE 3190, Université d'Evry-Val d'Essonne, Genopole, Tour Evry II, Evry, France. florence.dalche@ibisc.univ-evry.fr

BMC Proceedings
|December 19, 2008
PubMed
Summary
This summary is machine-generated.

This collection presents advanced machine learning applications in systems biology. These extended papers from MLSB 2007 showcase innovative computational approaches for biological research.

More Related Videos

A Web Tool for Generating High Quality Machine-readable Biological Pathways
08:01

A Web Tool for Generating High Quality Machine-readable Biological Pathways

Published on: February 8, 2017

Related Experiment Videos

Last Updated: Jun 27, 2026

Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

A Web Tool for Generating High Quality Machine-readable Biological Pathways
08:01

A Web Tool for Generating High Quality Machine-readable Biological Pathways

Published on: February 8, 2017

Area of Science:

  • Systems Biology
  • Computational Biology
  • Bioinformatics

Background:

  • The workshop focused on Machine Learning in Systems Biology (MLSB) in 2007.
  • It brought together researchers to discuss advancements in the field.
  • The event was held in Evry, France, from September 24-25, 2007.

Discussion:

  • This supplement features extended versions of selected papers.
  • The content reflects the state-of-the-art in machine learning for biological systems at the time.
  • It highlights the intersection of computational methods and biological inquiry.

Key Insights:

  • Machine learning offers powerful tools for analyzing complex biological data.
  • Systems biology benefits from advanced computational approaches.
  • Interdisciplinary collaboration is crucial for progress in this field.

Outlook:

  • The presented research likely influenced subsequent developments in computational systems biology.
  • Continued innovation in machine learning is expected to further advance biological research.
  • This collection serves as a valuable historical record of MLSB 2007.