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

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:
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,
Methods of Classification and Identification01:28

Methods of Classification and Identification

Bacterial identification relies on a diverse array of techniques to classify and understand microorganisms, each tailored to uncover specific characteristics. Traditional morphological approaches, while still valuable, are limited for closely related or structurally simple organisms. Modern methods integrate biochemical, serological, genetic, and advanced molecular tools to achieve greater accuracy.Morphological and Biochemical TechniquesMorphological characteristics, such as cell shape and...
Classification of Signals01:30

Classification of Signals

In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
Force Classification01:22

Force Classification

Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...
Aggregates Classification01:29

Aggregates Classification

Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...

You might also read

Related Articles

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

Sort by
Same author

Physics-informed Koopman-constrained implicitQ-learning for safe offline reinforcement learning in mechanical ventilation.

Biomedical physics & engineering express·2026
Same author

Explainable Knowledge-Guided Algorithm for Contrast Extravasation Detection on Computed Tomography.

IEEE journal of translational engineering in health and medicine·2026
Same author

Classification of pediatric dental diseases from panoramic radiographs using natural language transformer and deep learning models.

Frontiers in artificial intelligence·2026
Same author

PDCFMO: Probabilistic dense correspondence of human body via fusion meta-optimization.

Neural networks : the official journal of the International Neural Network Society·2026
Same author

Engineered nanozymes enhance atherosclerosis therapy via inflammation-lipid homeostasis modulation.

Colloids and surfaces. B, Biointerfaces·2026
Same author

Single-cell atlas of human penile corpus cavernosum reveals cellular and functional heterogeneity of aging-related erectile dysfunction.

Frontiers in endocrinology·2025
Same journal

OpenIMC: an open-source platform for analyzing single-cell and spatial proteomics by imaging mass cytometry.

BMC bioinformatics·2026
Same journal

NAP: an open source pipeline for cross-domain microbiome profiling using Nanopore sequencing-derived amplicon data.

BMC bioinformatics·2026
Same journal

SurvGME: an R package for survival analysis with graphical and measurement error models.

BMC bioinformatics·2026
Same journal

SimMapNet: a Bayesian framework for gene regulatory network inference using gene ontology similarities as external hint.

BMC bioinformatics·2026
Same journal

Dual channel drug-drug interactions extraction based on cross attention.

BMC bioinformatics·2026
Same journal

FeSseqdb: a curated sequence-level database and interpretable machine learning framework for identifying iron-sulfur proteins.

BMC bioinformatics·2026
See all related articles

Related Experiment Video

Updated: Jun 2, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Phenotype recognition with combined features and random subspace classifier ensemble.

Bailing Zhang1, Tuan D Pham

  • 1Department of Computer Science and Software Engineering, Xi'an Jiaotong-Liverpool University, Suzhou, 215123, PR China. bailing.zhang@xjtlu.edu.cn

BMC Bioinformatics
|May 3, 2011
PubMed
Summary
This summary is machine-generated.

This study introduces a new method combining Haralick features and curvelet transform for accurate cellular phenotype identification in biological imaging. The random subspace ensemble classifier significantly outperforms existing methods in classifying microscopy images.

Related Experiment Videos

Last Updated: Jun 2, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Area of Science:

  • Bioimage analysis
  • Computational biology
  • Machine learning for microscopy

Background:

  • Automated, image-based high-content screening is crucial for biological discovery.
  • Large datasets from robotic fluorescence microscopes require efficient computational methods for cellular phenotype identification.
  • Existing methods struggle with the complexity and scale of modern biological image data.

Purpose of the Study:

  • To develop an efficient computational method for extracting quantitative features from microscopy images.
  • To improve cellular phenotype identification accuracy using combined feature descriptors.
  • To evaluate the performance of a random subspace ensemble classifier for bioimage analysis.

Main Methods:

  • Extraction of quantitative features by combining Haralick features (second-order statistics) with curvelet transform.
  • Utilizing a random subspace (RS) ensemble classifier with Multi-Layer Perceptrons (MLPs) as base classifiers.
  • Employing grey level co-occurrence matrix (GLCM) for Haralick feature estimation and curvelet transform for sparse image representation.

Main Results:

  • The combined feature descriptor outperformed individual features in classification accuracy.
  • The random subspace ensemble achieved higher classification rates (91.20% for HeLa, 98.86% for CHO, 91.03% for RNAi) compared to published results (84%, 93%, 82%).
  • Optimal performance was achieved with medium feature subset dimensionality and small ensemble sizes.

Conclusions:

  • Curvelet transform is well-suited for describing microscopy images due to its multiscale and multidirectional properties.
  • Curvelet-based features are superior to wavelet-based features for bioimage description.
  • The random subspace ensemble of MLPs significantly outperforms common multi-class classifiers for phenotype recognition.